Podcasts about applied statistics

In this episode Gudrun speaks with Nadja Klein and Moussa Kassem Sbeyti who work at the Scientific Computing Center (SCC) at KIT in Karlsruhe. Since August 2024, Nadja has been professor at KIT leading the research group Methods for Big Data (MBD) there. She is an Emmy Noether Research Group Leader, and a member of AcademiaNet, and Die Junge Akademie, among others. In 2025, Nadja was awarded the Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award (ELA). The COPSS ELA recognizes early career statistical scientists who show evidence of and potential for leadership and who will help shape and strengthen the field. She finished her doctoral studies in Mathematics at the Universität Göttingen before conducting a postdoc at the University of Melbourne as a Feodor-Lynen fellow by the Alexander von Humboldt Foundation. Afterwards she was a Professor for Statistics and Data Science at the Humboldt-Universität zu Berlin before joining KIT. Moussa joined Nadja's lab as an associated member in 2023 and later as a postdoctoral researcher in 2024. He pursued a PhD at the TU Berlin while working as an AI Research Scientist at the Continental AI Lab in Berlin. His research primarily focuses on deep learning, developing uncertainty-based automated labeling methods for 2D object detection in autonomous driving. Prior to this, Moussa earned his M.Sc. in Mechatronics Engineering from the TU Darmstadt in 2021. The research of Nadja and Moussa is at the intersection of statistics and machine learning. In Nadja's MBD Lab the research spans theoretical analysis, method development and real-world applications. One of their key focuses is Bayesian methods, which allow to incorporate prior knowledge, quantify uncertainties, and bring insights to the “black boxes” of machine learning. By fusing the precision and reliability of Bayesian statistics with the adaptability of machine and deep learning, these methods aim to leverage the best of both worlds. The KIT offers a strong research environment, making it an ideal place to continue their work. They bring new expertise that can be leveraged in various applications and on the other hand Helmholtz offers a great platform in that respect to explore new application areas. For example Moussa decided to join the group at KIT as part of the Helmholtz Pilot Program Core-Informatics at KIT (KiKIT), which is an initiative focused on advancing fundamental research in informatics within the Helmholtz Association. Vision models typically depend on large volumes of labeled data, but collecting and labeling this data is both expensive and prone to errors. During his PhD, his research centered on data-efficient learning using uncertainty-based automated labeling techniques. That means estimating and using the uncertainty of models to select the helpful data samples to train the models to label the rest themselves. Now, within KiKIT, his work has evolved to include knowledge-based approaches in multi-task models, eg. detection and depth estimation — with the broader goal of enabling the development and deployment of reliable, accurate vision systems in real-world applications. Statistics and data science are fascinating fields, offering a wide variety of methods and applications that constantly lead to new insights. Within this domain, Bayesian methods are especially compelling, as they enable the quantification of uncertainty and the incorporation of prior knowledge. These capabilities contribute to making machine learning models more data-efficient, interpretable, and robust, which are essential qualities in safety-critical domains such as autonomous driving and personalized medicine. Nadja is also enthusiastic about the interdisciplinarity of the subject — repeatedly changing the focus from mathematics to economics to statistics to computer science. The combination of theoretical fundamentals and practical applications makes statistics an agile and important field of research in data science. From a deep learning perspective, the focus is on making models both more efficient and more reliable when dealing with large-scale data and complex dependencies. One way to do this is by reducing the need for extensive labeled data. They also work on developing self-aware models that can recognize when they're unsure and even reject their own predictions when necessary. Additionally, they explore model pruning techniques to improve computational efficiency, and specialize in Bayesian deep learning, allowing machine learning models to better handle uncertainty and complex dependencies. Beyond the methods themselves, they also contribute by publishing datasets that help push the development of next-generation, state-of-the-art models. The learning methods are applied across different domains such as object detection, depth estimation, semantic segmentation, and trajectory prediction — especially in the context of autonomous driving and agricultural applications. As deep learning technologies continue to evolve, they're also expanding into new application areas such as medical imaging. Unlike traditional deep learning, Bayesian deep learning provides uncertainty estimates alongside predictions, allowing for more principled decision-making and reducing catastrophic failures in safety-critical application. It has had a growing impact in several real-world domains where uncertainty really matters. Bayesian learning incorporates prior knowledge and updates beliefs as new data comes in, rather than relying purely on data-driven optimization. In healthcare, for example, Bayesian models help quantify uncertainty in medical diagnoses, which supports more risk-aware treatment decisions and can ultimately lead to better patient outcomes. In autonomous vehicles, Bayesian models play a key role in improving safety. By recognizing when the system is uncertain, they help capture edge cases more effectively, reduce false positives and negatives in object detection, and navigate complex, dynamic environments — like bad weather or unexpected road conditions — more reliably. In finance, Bayesian deep learning enhances both risk assessment and fraud detection by allowing the system to assess how confident it is in its predictions. That added layer of information supports more informed decision-making and helps reduce costly errors. Across all these areas, the key advantage is the ability to move beyond just accuracy and incorporate trust and reliability into AI systems. Bayesian methods are traditionally more expensive, but modern approximations (e.g., variational inference or last layer inference) make them feasible. Computational costs depend on the problem — sometimes Bayesian models require fewer data points to achieve better performance. The trade-off is between interpretability and computational efficiency, but hardware improvements are helping bridge this gap. Their research on uncertainty-based automated labeling is designed to make models not just safer and more reliable, but also more efficient. By reducing the need for extensive manual labeling, one improves the overall quality of the dataset while cutting down on human effort and potential labeling errors. Importantly, by selecting informative samples, the model learns from better data — which means it can reach higher performance with fewer training examples. This leads to faster training and better generalization without sacrificing accuracy. They also focus on developing lightweight uncertainty estimation techniques that are computationally efficient, so these benefits don't come with heavy resource demands. In short, this approach helps build models that are more robust, more adaptive to new data, and significantly more efficient to train and deploy — which is critical for real-world systems where both accuracy and speed matter. Statisticians and deep learning researchers often use distinct methodologies, vocabulary and frameworks, making communication and collaboration challenging. Unfortunately, there is a lack of Interdisciplinary education: Traditional academic programs rarely integrate both fields. It is necessary to foster joint programs, workshops, and cross-disciplinary training can help bridge this gap. From Moussa's experience coming through an industrial PhD, he has seen how many industry settings tend to prioritize short-term gains — favoring quick wins in deep learning over deeper, more fundamental improvements. To overcome this, we need to build long-term research partnerships between academia and industry — ones that allow for foundational work to evolve alongside practical applications. That kind of collaboration can drive more sustainable, impactful innovation in the long run, something we do at methods for big data. Looking ahead, one of the major directions for deep learning in the next five to ten years is the shift toward trustworthy AI. We're already seeing growing attention on making models more explainable, fair, and robust — especially as AI systems are being deployed in critical areas like healthcare, mobility, and finance. The group also expect to see more hybrid models — combining deep learning with Bayesian methods, physics-based models, or symbolic reasoning. These approaches can help bridge the gap between raw performance and interpretability, and often lead to more data-efficient solutions. Another big trend is the rise of uncertainty-aware AI. As AI moves into more high-risk, real-world applications, it becomes essential that systems understand and communicate their own confidence. This is where uncertainty modeling will play a key role — helping to make AI not just more powerful, but also more safe and reliable. The lecture "Advanced Bayesian Data Analysis" covers fundamental concepts in Bayesian statistics, including parametric and non-parametric regression, computational techniques such as MCMC and variational inference, and Bayesian priors for handling high-dimensional data. Additionally, the lecturers offer a Research Seminar on Selected Topics in Statistical Learning and Data Science. The workgroup offers a variety of Master's thesis topics at the intersection of statistics and deep learning, focusing on Bayesian modeling, uncertainty quantification, and high-dimensional methods. Current topics include predictive information criteria for Bayesian models and uncertainty quantification in deep learning. Topics span theoretical, methodological, computational and applied projects. Students interested in rigorous theoretical and applied research are encouraged to explore our available projects and contact us for further details. The general advice of Nadja and Moussa for everybody interested to enter the field is: "Develop a strong foundation in statistical and mathematical principles, rather than focusing solely on the latest trends. Gain expertise in both theory and practical applications, as real-world impact requires a balance of both. Be open to interdisciplinary collaboration. Some of the most exciting and meaningful innovations happen at the intersection of fields — whether that's statistics and deep learning, or AI and domain-specific areas like medicine or mobility. So don't be afraid to step outside your comfort zone, ask questions across disciplines, and look for ways to connect different perspectives. That's often where real breakthroughs happen. With every new challenge comes an opportunity to innovate, and that's what keeps this work exciting. We're always pushing for more robust, efficient, and trustworthy AI. And we're also growing — so if you're a motivated researcher interested in this space, we'd love to hear from you." Literature and further information Webpage of the group G. Nuti, Lluis A.J. Rugama, A.-I. Cross: Efficient Bayesian Decision Tree Algorithm, arxiv Jan 2019 Wikipedia: Expected value of sample information C. Howson & P. Urbach: Scientific Reasoning: The Bayesian Approach (3rd ed.). Open Court Publishing Company. ISBN 978-0-8126-9578-6, 2005. A.Gelman e.a.: Bayesian Data Analysis Third Edition. Chapman and Hall/CRC. ISBN 978-1-4398-4095-5, 2013. Yu, Angela: Introduction to Bayesian Decision Theory cogsci.ucsd.edu, 2013. Devin Soni: Introduction to Bayesian Networks, 2015. G. Nuti, L. Rugama, A.-I. Cross: Efficient Bayesian Decision Tree Algorithm, arXiv:1901.03214 stat.ML, 2019. M. Carlan, T. Kneib and N. Klein: Bayesian conditional transformation models, Journal of the American Statistical Association, 119(546):1360-1373, 2024. N. Klein: Distributional regression for data analysis , Annual Review of Statistics and Its Application, 11:321-346, 2024 C.Hoffmann and N.Klein: Marginally calibrated response distributions for end-to-end learning in autonomous driving, Annals of Applied Statistics, 17(2):1740-1763, 2023 Kassem Sbeyti, M., Karg, M., Wirth, C., Klein, N., & Albayrak, S. (2024, September). Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection. In Uncertainty in Artificial Intelligence (pp. 1890-1900). PMLR. M. K. Sbeyti, N. Klein, A. Nowzad, F. Sivrikaya and S. Albayrak: Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection pdf. To appear in Transactions on Machine Learning Research, 2025 Podcasts Learning, Teaching, and Building in the Age of AI Ep 42 of Vanishing Gradient, Jan 2025. O. Beige, G. Thäter: Risikoentscheidungsprozesse, Gespräch im Modellansatz Podcast, Folge 193, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2019.

In this episode Gudrun speaks with Nadja Klein and Moussa Kassem Sbeyti who work at the Scientific Computing Center (SCC) at KIT in Karlsruhe. Since August 2024, Nadja has been professor at KIT leading the research group Methods for Big Data (MBD) there. She is an Emmy Noether Research Group Leader, and a member of AcademiaNet, and Die Junge Akademie, among others. In 2025, Nadja was awarded the Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award (ELA). The COPSS ELA recognizes early career statistical scientists who show evidence of and potential for leadership and who will help shape and strengthen the field. She finished her doctoral studies in Mathematics at the Universität Göttingen before conducting a postdoc at the University of Melbourne as a Feodor-Lynen fellow by the Alexander von Humboldt Foundation. Afterwards she was a Professor for Statistics and Data Science at the Humboldt-Universität zu Berlin before joining KIT. Moussa joined Nadja's lab as an associated member in 2023 and later as a postdoctoral researcher in 2024. He pursued a PhD at the TU Berlin while working as an AI Research Scientist at the Continental AI Lab in Berlin. His research primarily focuses on deep learning, developing uncertainty-based automated labeling methods for 2D object detection in autonomous driving. Prior to this, Moussa earned his M.Sc. in Mechatronics Engineering from the TU Darmstadt in 2021. The research of Nadja and Moussa is at the intersection of statistics and machine learning. In Nadja's MBD Lab the research spans theoretical analysis, method development and real-world applications. One of their key focuses is Bayesian methods, which allow to incorporate prior knowledge, quantify uncertainties, and bring insights to the “black boxes” of machine learning. By fusing the precision and reliability of Bayesian statistics with the adaptability of machine and deep learning, these methods aim to leverage the best of both worlds. The KIT offers a strong research environment, making it an ideal place to continue their work. They bring new expertise that can be leveraged in various applications and on the other hand Helmholtz offers a great platform in that respect to explore new application areas. For example Moussa decided to join the group at KIT as part of the Helmholtz Pilot Program Core-Informatics at KIT (KiKIT), which is an initiative focused on advancing fundamental research in informatics within the Helmholtz Association. Vision models typically depend on large volumes of labeled data, but collecting and labeling this data is both expensive and prone to errors. During his PhD, his research centered on data-efficient learning using uncertainty-based automated labeling techniques. That means estimating and using the uncertainty of models to select the helpful data samples to train the models to label the rest themselves. Now, within KiKIT, his work has evolved to include knowledge-based approaches in multi-task models, eg. detection and depth estimation — with the broader goal of enabling the development and deployment of reliable, accurate vision systems in real-world applications. Statistics and data science are fascinating fields, offering a wide variety of methods and applications that constantly lead to new insights. Within this domain, Bayesian methods are especially compelling, as they enable the quantification of uncertainty and the incorporation of prior knowledge. These capabilities contribute to making machine learning models more data-efficient, interpretable, and robust, which are essential qualities in safety-critical domains such as autonomous driving and personalized medicine. Nadja is also enthusiastic about the interdisciplinarity of the subject — repeatedly changing the focus from mathematics to economics to statistics to computer science. The combination of theoretical fundamentals and practical applications makes statistics an agile and important field of research in data science. From a deep learning perspective, the focus is on making models both more efficient and more reliable when dealing with large-scale data and complex dependencies. One way to do this is by reducing the need for extensive labeled data. They also work on developing self-aware models that can recognize when they're unsure and even reject their own predictions when necessary. Additionally, they explore model pruning techniques to improve computational efficiency, and specialize in Bayesian deep learning, allowing machine learning models to better handle uncertainty and complex dependencies. Beyond the methods themselves, they also contribute by publishing datasets that help push the development of next-generation, state-of-the-art models. The learning methods are applied across different domains such as object detection, depth estimation, semantic segmentation, and trajectory prediction — especially in the context of autonomous driving and agricultural applications. As deep learning technologies continue to evolve, they're also expanding into new application areas such as medical imaging. Unlike traditional deep learning, Bayesian deep learning provides uncertainty estimates alongside predictions, allowing for more principled decision-making and reducing catastrophic failures in safety-critical application. It has had a growing impact in several real-world domains where uncertainty really matters. Bayesian learning incorporates prior knowledge and updates beliefs as new data comes in, rather than relying purely on data-driven optimization. In healthcare, for example, Bayesian models help quantify uncertainty in medical diagnoses, which supports more risk-aware treatment decisions and can ultimately lead to better patient outcomes. In autonomous vehicles, Bayesian models play a key role in improving safety. By recognizing when the system is uncertain, they help capture edge cases more effectively, reduce false positives and negatives in object detection, and navigate complex, dynamic environments — like bad weather or unexpected road conditions — more reliably. In finance, Bayesian deep learning enhances both risk assessment and fraud detection by allowing the system to assess how confident it is in its predictions. That added layer of information supports more informed decision-making and helps reduce costly errors. Across all these areas, the key advantage is the ability to move beyond just accuracy and incorporate trust and reliability into AI systems. Bayesian methods are traditionally more expensive, but modern approximations (e.g., variational inference or last layer inference) make them feasible. Computational costs depend on the problem — sometimes Bayesian models require fewer data points to achieve better performance. The trade-off is between interpretability and computational efficiency, but hardware improvements are helping bridge this gap. Their research on uncertainty-based automated labeling is designed to make models not just safer and more reliable, but also more efficient. By reducing the need for extensive manual labeling, one improves the overall quality of the dataset while cutting down on human effort and potential labeling errors. Importantly, by selecting informative samples, the model learns from better data — which means it can reach higher performance with fewer training examples. This leads to faster training and better generalization without sacrificing accuracy. They also focus on developing lightweight uncertainty estimation techniques that are computationally efficient, so these benefits don't come with heavy resource demands. In short, this approach helps build models that are more robust, more adaptive to new data, and significantly more efficient to train and deploy — which is critical for real-world systems where both accuracy and speed matter. Statisticians and deep learning researchers often use distinct methodologies, vocabulary and frameworks, making communication and collaboration challenging. Unfortunately, there is a lack of Interdisciplinary education: Traditional academic programs rarely integrate both fields. It is necessary to foster joint programs, workshops, and cross-disciplinary training can help bridge this gap. From Moussa's experience coming through an industrial PhD, he has seen how many industry settings tend to prioritize short-term gains — favoring quick wins in deep learning over deeper, more fundamental improvements. To overcome this, we need to build long-term research partnerships between academia and industry — ones that allow for foundational work to evolve alongside practical applications. That kind of collaboration can drive more sustainable, impactful innovation in the long run, something we do at methods for big data. Looking ahead, one of the major directions for deep learning in the next five to ten years is the shift toward trustworthy AI. We're already seeing growing attention on making models more explainable, fair, and robust — especially as AI systems are being deployed in critical areas like healthcare, mobility, and finance. The group also expect to see more hybrid models — combining deep learning with Bayesian methods, physics-based models, or symbolic reasoning. These approaches can help bridge the gap between raw performance and interpretability, and often lead to more data-efficient solutions. Another big trend is the rise of uncertainty-aware AI. As AI moves into more high-risk, real-world applications, it becomes essential that systems understand and communicate their own confidence. This is where uncertainty modeling will play a key role — helping to make AI not just more powerful, but also more safe and reliable. The lecture "Advanced Bayesian Data Analysis" covers fundamental concepts in Bayesian statistics, including parametric and non-parametric regression, computational techniques such as MCMC and variational inference, and Bayesian priors for handling high-dimensional data. Additionally, the lecturers offer a Research Seminar on Selected Topics in Statistical Learning and Data Science. The workgroup offers a variety of Master's thesis topics at the intersection of statistics and deep learning, focusing on Bayesian modeling, uncertainty quantification, and high-dimensional methods. Current topics include predictive information criteria for Bayesian models and uncertainty quantification in deep learning. Topics span theoretical, methodological, computational and applied projects. Students interested in rigorous theoretical and applied research are encouraged to explore our available projects and contact us for further details. The general advice of Nadja and Moussa for everybody interested to enter the field is: "Develop a strong foundation in statistical and mathematical principles, rather than focusing solely on the latest trends. Gain expertise in both theory and practical applications, as real-world impact requires a balance of both. Be open to interdisciplinary collaboration. Some of the most exciting and meaningful innovations happen at the intersection of fields — whether that's statistics and deep learning, or AI and domain-specific areas like medicine or mobility. So don't be afraid to step outside your comfort zone, ask questions across disciplines, and look for ways to connect different perspectives. That's often where real breakthroughs happen. With every new challenge comes an opportunity to innovate, and that's what keeps this work exciting. We're always pushing for more robust, efficient, and trustworthy AI. And we're also growing — so if you're a motivated researcher interested in this space, we'd love to hear from you." Literature and further information Webpage of the group G. Nuti, Lluis A.J. Rugama, A.-I. Cross: Efficient Bayesian Decision Tree Algorithm, arxiv Jan 2019 Wikipedia: Expected value of sample information C. Howson & P. Urbach: Scientific Reasoning: The Bayesian Approach (3rd ed.). Open Court Publishing Company. ISBN 978-0-8126-9578-6, 2005. A.Gelman e.a.: Bayesian Data Analysis Third Edition. Chapman and Hall/CRC. ISBN 978-1-4398-4095-5, 2013. Yu, Angela: Introduction to Bayesian Decision Theory cogsci.ucsd.edu, 2013. Devin Soni: Introduction to Bayesian Networks, 2015. G. Nuti, L. Rugama, A.-I. Cross: Efficient Bayesian Decision Tree Algorithm, arXiv:1901.03214 stat.ML, 2019. M. Carlan, T. Kneib and N. Klein: Bayesian conditional transformation models, Journal of the American Statistical Association, 119(546):1360-1373, 2024. N. Klein: Distributional regression for data analysis , Annual Review of Statistics and Its Application, 11:321-346, 2024 C.Hoffmann and N.Klein: Marginally calibrated response distributions for end-to-end learning in autonomous driving, Annals of Applied Statistics, 17(2):1740-1763, 2023 Kassem Sbeyti, M., Karg, M., Wirth, C., Klein, N., & Albayrak, S. (2024, September). Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection. In Uncertainty in Artificial Intelligence (pp. 1890-1900). PMLR. M. K. Sbeyti, N. Klein, A. Nowzad, F. Sivrikaya and S. Albayrak: Building Blocks for Robust and Effective Semi-Supervised Real-World Object Detection pdf. To appear in Transactions on Machine Learning Research, 2025 Podcasts Learning, Teaching, and Building in the Age of AI Ep 42 of Vanishing Gradient, Jan 2025. O. Beige, G. Thäter: Risikoentscheidungsprozesse, Gespräch im Modellansatz Podcast, Folge 193, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2019.

Chad Bareither is the owner and principal consultant of Bareither Group Consulting. Chad shares his journey from working as a civilian engineer in the U.S. Army to becoming a Lean Six Sigma Master Black Belt and consultant for medtech and pharma companies. Chad discusses his process improvement framework, the importance of understanding both systems and people, and insights from his recently published book "Improve LESS." He also covers the transition from being an employee to an entrepreneur and the qualities essential for leadership in the industry.    Guest links: https://www.linkedin.com/in/chadbareither/ | https://www.bareithergroup.com/ | https://www.youtube.com/channel/UCTjC2ZBL3mqnriCeAIkmSlQ Charity supported: Polaris Project Interested in being a guest on the show or have feedback to share? Email us at theleadingdifference@velentium.com.  PRODUCTION CREDITS Host: Lindsey Dinneen Editing: Marketing Wise Producer: Velentium   EPISODE TRANSCRIPT Episode 045 - Chad Bareither [00:00:00] Lindsey Dinneen: Hi, I'm Lindsey and I'm talking with MedTech industry leaders on how they change lives for a better world. [00:00:09] Diane Bouis: The inventions and technologies are fascinating and so are the people who work with them. [00:00:15] Frank Jaskulke: There was a period of time where I realized, fundamentally, my job was to go hang out with really smart people that are saving lives and then do work that would help them save more lives. [00:00:28] Diane Bouis: I got into the business to save lives and it is incredibly motivating to work with people who are in that same business, saving or improving lives. [00:00:38] Duane Mancini: What better industry than where I get to wake up every day and just save people's lives. [00:00:42] Lindsey Dinneen: These are extraordinary people doing extraordinary work, and this is The Leading Difference. Hello, and welcome to another episode of The Leading Difference Podcast. I'm your host, Lindsey, and I am so excited to introduce you to my guest today, Chad Bareither. Chad is the owner and principal consultant of Bareither Group Consulting. He partners with med device and pharma company leaders to boost productivity. This is delivered through the Focus and Align Framework, the subject of his book, "Improve LESS.". Chad is a Certified Lean Six Sigma Master Black Belt and holds a Bachelor's degree in Mechanical Engineering from Michigan Technological University, as well as Master's degrees in both Industrial and Systems Engineering and Applied Statistics from Rutgers University. He has over 10 years of experience in the med device and pharma industries and almost 20 years of professional experience. All right. Well, welcome to the show, Chad. I'm so excited to talk to you today. [00:01:42] Chad Bareither: Yeah, thanks for having me on. [00:01:45] Lindsey Dinneen: Of course! I'd love if you wouldn't mind by starting off telling us a little bit about yourself, your background, and maybe what led you to what you're doing now. [00:01:54] Chad Bareither: Sure. So I started my career in the US Army as a civilian engineer. So my background's in engineering, mechanical, I have a degree in mechanical engineering and also industrial engineering. So I started out in the U. S. Army as a civilian doing acquisition projects. So we would design and then purchase componentry for our warfighters from various defense contractors. And so my role in that was quality. So understanding are we designing all of the elements correctly. Then when they're being produced, are they meeting our specifications? And then once they're in stockpile, do they continue to work before we hand them to the brave men and women that are defending our freedom. And so I worked there for a while and pretty early found my niche that I was really into process improvement. So I would visit defense contractors, and if we had an issue, what I was really seem to have a knack for was helping to understand the process and make it better. So we could either expand capacity or have better quality. And so that kind of bridged right into a unique program they were introducing at the time, which was called Lean Six Sigma, which is a corporate program for reducing variation and improving efficiency of processes and the corporation at large. So I was pretty lucky that these two things coincide at the same time is that I was finding my niche and they were rolling out a program that really focused in that. So I was able to get into one of those programs, get trained and certified. And then I followed that path on to several other industries, including med device, pharma, and then was also a corporate employee in some utility, electric, natural gas. After my last corporate engagement, I went off on my own and I began consulting. So delivering the same services I had internally to those larger organizations. But now I have the ability to target smaller or growing organizations. In my consulting engagements, if you combine corporate experience and consulting engagement, it's somewhere around eight industries that I worked in. But I really enjoy the work and the challenges in med device and pharma a lot more. There's the purpose behind it of serving patients, and there's also some really significant technical challenges that I just find are fascinating to learn about. So for the last-- oh, it's six years now-- I've been consulting delivering those services in various industries, but really trying to focus my space into the medtech arena. [00:04:46] Lindsey Dinneen: Nice. Well, first of all, thank you for sharing a little bit about your background. I appreciate it. And it's fascinating to hear how you started off with one focus and then it just continued to evolve and twist and turn into this amazing consulting career that you have now. So many questions, but the first is could seven year old Chad have possibly anticipated what you're doing now, since it's different than what you started off with. [00:05:15] Chad Bareither: Yeah, no, I think seven year old Chad probably wanted to be a professional baseball player. But if I zoom forward a little bit from that, once middle school and high school, I always. naturally gravitated to our math and science was thinking it was going to be engineering. And I did, I studied engineering and most of my day is not engineering. It's really understanding people. But what's fascinating is if I look back, I think what all of my engineering education taught me was really a system for solving problems, right? So the problems that we solved happen to be mechanical design or industrial design. Got it. But taking that mindset of problem solving and now saying, well, the systems that I work with on a daily basis with my customers are a little bit more complex because you have mechanical systems, but you also have people systems that are intertwined with that, right? So, whether I've worked across the spectrum and still do of research and development, clinical trials, manufacturing and post market surveillance and across that. You can have systems set up, but people still operate it. So, it's difficult to just analyze your way into the perfect solution. Even if I can show on paper that it works, like you still need to understand the people elements of it. So I think that's been the biggest evolution through my career is early on, it was like, "I don't understand why we're doing this. It makes sense on paper." And it's like the change management component of that has been really something I've been able to develop personally, I'd say, over the last 6 to 10 years. [00:06:51] Lindsey Dinneen: Yeah. And I'm curious, so bridging that gap between systems and people and understanding that what looks good on paper might not translate as perfectly into real life as one would hope, because we're people and people are complex. So were there certain learning opportunities that you had that helped bridge that gap of gaining your expertise and knowledge in that way? Or what led you to be able to do that so efficiently now? [00:07:23] Chad Bareither: Oof, well, you're assuming I do it efficiently now, so but I'd say I still believe we learn more from failures than we do from success, right? So, there are specific projects or engagements I can look back to. So I'll talk about one specifically. This was a medical device assembly plant. And the particular production line that we were working on, we were trying to increase capacity on, and we even had the team engaged, right? So we were doing everything right in terms of the engagement project, had the teams involved, understood their pain points. We were trying to make it easier for them. And then like on paper again, showed we could do the production line, with the main assembly line, with three operators instead of four. And so we were really pushing for that because being just transparent, looking back now, it's like the productivity gain would have looked really sweet to management. But we had the operators telling us like, "I don't think it's going to work. I don't think it's going to work that way." And we're like, " No, it's going to work," right and pushing for it. And I don't know, you, you get a little focused on your own goals or whatever you, however you want to phrase that. And yeah it was a struggle to launch. And they ended up having to cover some of that with overtime. They made some adjustments long term, but that was a big learning for me of, I mean, if the people actually doing that work eight to 10 hours a day are telling you it's not going to work, like you should probably pull back and either, you've got more explaining to do, more improvements to do, or you should just maybe listen to them a little bit more. But you know, there's other scenarios I can look at that were. You know, when I say failure, right? Not everything means it's a flaming dumpster fire, but sometimes you don't get exactly what you expect out of it. And a lot of it can, I can point back to and say, " Ooh, you know, I could have done a better job. It's not that the analysis was wrong. It's not that the tool we put in place or the management technique or the visualization, it's that we didn't have the right level of buy in or the right people buying in." [00:09:35] Lindsey Dinneen: Okay. Well, I, thank you. I appreciate your honesty and transparency. But I do think to your point, failure or whatever we perceive to be as failure because it didn't work out quite the way we hoped for, is such a powerful learning tool if you can take it and go, "Okay, here's what worked. Here's what didn't. Here's what I can do better next time." And you don't have to go, "Okay, that was a waste." It's never a waste if you can learn from it. [00:10:01] Chad Bareither: A hundred percent. And I think only in recent years, I'd say the last four to five years, that I've really gotten into that of more of a bias for action of, " What's the worst that's gonna happen?" And honestly, I'm not talking about changes that are gonna bankrupt a company, right? It's if you're changing the direction, but " Well, let's try it." So having a bias for action and thinking, just like you pointed out, that it's going to be a learning experience, right? So if you treat it more of an experiment, success isn't necessarily binary-- it was a success or it wasn't-- we learned something. Maybe we got better. Maybe we didn't. But that means the next round, the engagement that we talked about before we started recording, I'm just coming back from-- we had two weeks of not going so well. And then the last week there was finally a breakthrough. And it was like, but I'm comfortable with it. The team was getting disengaged and I'm like, "We're going to get there! You guys stay positive, 'cause I know we're going to get there." And the failures we learned, we know so much more about that process now than we did three weeks ago. [00:10:59] Lindsey Dinneen: Yeah. Yeah. And that brought up an interesting point. So persistence and the willingness or the bias to action, which I really liked the way you put that, the willingness to experiment is something that does take a level of comfort that maybe not everyone is so excited about. But I'm wondering what you have seen over the years as being some of the top qualities of a leader that contribute to that success and that willingness to experiment. [00:11:33] Chad Bareither: Yeah. So, it helps me to think about specific leaders when I, that are like embodying that rather than just speak generically about it. And there was an R&D manager that I work with, his first name was John, but really took the stigma of failure, and I think not even using that word as much, out of it, and just saying, " Let's try and see what happens." And kind of building that learning mindset of, I'd rather move fast and learn something than move slow and get it perfect. And in industries, especially like a bunch of the medtech fields, I know in some of the pharma clients I've worked with, they're looking at things like new technologies, new modality of disease and I'm not a scientist, but these are things that we've never done before. And so the mindset of trying to get it perfect-- like this leader I work with previously, John is like, "Why are we wanting to get it perfect? We won't get it perfect the first time. And if we try to, we're going to be moving too slowly." so that's kind of the first thing that I think of is taking the stigma out of failure and turning it more into trying, learning mindset, things like that. I think the other thing is keeping open communication. And what I mean by that is there's another leader I'm thinking of and his first name was Mickey. And trying to have more open conversations. Information can be used for power, in some cases, or if you're harboring information or knowledge, like, " I'm the conduit, right? So then I become what puts it all together." And he was big on breaking down some of those and having more open conversations about what we're learning and what works and what doesn't work. And I mean, you see teams grow together faster. And so then when you take those two qualities, if I take the stigma of failure off of the organization as a whole, and I work to build more open lines of communication and you build trust, right? So then I'm more, I don't want to say confident-- that's not maybe the right word to go after-- but there's less hesitancy, less fear, maybe. So not being confident doesn't mean I'm not fearful, but if I can take a little bit of that fear, a little bit of the stigma of failure out, I'm willing to try. I'm willing to go off on something new. And as we look at this industry of new advances in technology, new challenges of diseases, we're going to have to keep moving fast and do it in areas that are pretty uncertain. So those are some things that I think help, of saying that we're not going to get it right every time, opening up lines of communication to build trust in the team. And then we can really move faster to a shared goal. [00:14:08] Lindsey Dinneen: Yeah. I really like that. Thank you for that advice and insight. That's really helpful. So now with your own company, consulting, well, a couple of questions, but the first is what stage of business do you usually typically come in on? Or is there not necessarily a stage that's your sweet spot? [00:14:28] Chad Bareither: There's, I wouldn't say right now there's a stage where I could say I have a, a litany of business cases for one stage, so multiple stages. I work with some organizations that are still in-- I mean, so if you think about the business, the corporate stage, established businesses, so they're past what would that be? Series two funding. So commercialized product. So I'm either working with the R&D pipeline on next generation products, next innovation, or in the operation space of improving manufacturing operations are typically the two areas that I'm working in the most. [00:15:09] Lindsey Dinneen: Yeah. Was there an interesting learning curve going from being an employee to being an entrepreneur? [00:15:18] Chad Bareither: Yeah, so let me answer that two ways. The first is moving from being an internal employee to being a consultant, right? Because it's just a different, you're of a different role in the company, right? And then there's also to your point is great moving from being an employee to an entrepreneur. So if you don't mind, I'll kind of tackle both of those. The first is moving from employee to consultant is interesting. Because I was on the employee side when you would have consultants come in. And so leaving the bad taste in my mouth from some consultants we had worked in, they're there to make an impact so that they can either upsell their services or whatever. And I can remember being on engagement. So it's like pushing so hard and just, " I have to work with these people when you leave. So you're kind of creating a mess for us." And just trying to meet people more where they're at. But you know, there's an adage of "a prophet isn't recognized in their hometown." It's sometimes they just need someone from the outside to point out what everyone has showing. And I know that sounds simple, but sometimes you just need to come in and say, "Independent third party here. And yes, that is the problem." So it's nice that you have that sense of authority, but I am personally, I am very cautious about the fact of, look, these people need to live with the solution when I walk away. The worst thing in my mind could be helping a client solve a problem, and then it returned for them. So even if they did want to call me back, that would be seen as not ideal in my mind. I want to help them get to a solution that then they can buy in and sustain. So that, that first change is going from internal employee to consultant where, you do have to make an impact, a splash, a return on investment, whatever you say. But, I'm cautious to also say, but they need to adopt the change. They need to own it. It can't just be my great idea. The other side that you talked about is going from employee to entrepreneur, which is also an interesting transition. As an employee, there's some perceived safety and stability, and I say that just perceived, because depending upon the industry that you're in, as markets change and things like that, layoffs come, things of that nature. So job security is never a hundred percent, but there is some perceived job security and stability there. But as you get past the startup stage, you start to specialize, which means your job responsibility narrows, right? So in a larger organization, typically you become a specialist, but not very broad in thinking, and, and so that can be comfortable as well. You develop some technical expertise. Moving into the entrepreneurial space, which you probably have dabbled in a little bit as an, as a business owner yourself is, you are simultaneously the chief marketing officer and IT support and delivery services, and fill in the blank. So you're wearing a lot of hats. And it can be difficult to gravitate towards the stuff you're really good at. So, I am best at the delivery, the actual client engagements. But I recognize if I'm not doing sales and marketing, and building new connections like that, eventually that work goes away. So it's trying to manage yourself and not stay where you're comfortable, if that makes sense. And not just deep dive all the way down to specialty in one area and have to learn some of these things. Or, you know, find the right people to do it for you. [00:18:49] Lindsey Dinneen: Yeah. Yeah. Yeah. That's very insightful. So you are also a published author and I was wondering if you could share a little bit about your book. [00:18:57] Chad Bareither: Yeah. So the name of the book I wrote in the fall of 2023, it was released, is called "Improve LESS" and intentionally thought provoking title that I got to it in a very roundabout way. The whole concept of the book started behind that, when I launched my consulting firm, I was still working full time as a corporate employee. So a friend of a friend asked if I can help. And I said, "Sure!" And that was a side gig. And then had another one come up and another one come up and then one of those clients wanted something more. And eventually I didn't have time to do a full time corporate job anymore. But then I had three clients that were all kind of different phases and asking for different things. And so I had one client that was really focused on strategy. And, " We need to align our strategy. We need a better way to cascade that in the organization." Another client that was really focused on process improvement. "We want to build our problem solving and process improvement skills for the organization." And I had a third client that really wanted to have better eyes on the business, so we would call it a daily management system, visualization of metrics and understanding the business so we can diagnose problems. Well, once you get good at strategy, then you actually have to go improve the processes. Once I'm pretty good at process improvement, I should probably align those strategically. Once I can see the problems in my business, I need to-- so essentially all of those three clients needed the three parts that were together. So I sat back and I said, "Well, this is starting to become a little bit of a mess. What would I do if I had a new client? What, where would I start?" So I started writing down the process really for my own benefit. And then working with a business coach, I was like, "I'm going to give this away as like a PDF or whatever." They're like, "No, you should turn this into a book." And I'm like, "Like a book?" And they're like, "Yeah." And I had no idea how to do that. So, you know, back to our conversation about entrepreneurs is, so I found someone who did. And I'm work with someone else, a publishing strategist helped me go through everything, which I thought it was pretty good, in terms of editing, that was not the case. So, went through some content editing and professional editing, and then, hired a professional illustrator from my hand drawn drawings. So, yeah, it was a journey, but that's how it started was me saying, " Well, what's my process?" And so really the purpose of the book is it is a framework. Anyone can pick it up and follow it. And I also tried to keep it short. I don't like to be very verbose in the communications to my clients because they need to understand it. So it's literally something that you could read in a weekend and start on Monday. [00:21:39] Lindsey Dinneen: I love that. Okay. So yeah. Yeah. So you've written this book, and you have your consulting firm, and what are you excited about coming up? Maybe both personally and professionally. [00:21:53] Chad Bareither: Yeah. Oh, I think it's easier for me to answer personally. So I'll start there. So my wife and I have three children and they're all pretty active in different competitive endeavors, gymnastics. We talked before, my middle daughter is a dancer, the two girls, the gymnast and the dancer, also play volleyball. And then my youngest son is on a baseball and a soccer team. And so, I mean, I just love supporting them in those. Now I say all that academics are also important. They're doing well academically. That's kind of the condition for doing the sports and stuff like that, but really pouring into them right now. It's It's going to sound so cliche, but our oldest is 13 right now. And some pictures came up, memories on my phone, and it's goes by quick. So personally, I'm just excited about in them right now. And they're turning-- I use this term and my coworker laughs at me-- but they're turning into real people, with their own personalities and their own likes, and it's frustrating at times because they have their own thoughts. Yeah. But it's fascinating right now. And just being able to spend more time investing in them is, is great. Professionally is exciting to really I'm niching back down into this medtech area, right? So I'm carrying a pharma client. I came off a pharma engagement. That was just at the beginning of this year and I've worked in other industries, but I'm just really fired up about the work, the technical challenges in these areas. So getting back into some client engagements that are med device and pharmaceuticals, and then, pharmaceuticals has stuff going on that I don't, I can't begin to understand. Bio therapeutic proteins and cell therapy stuff, which is-- it's fascinating technology, but it's still process, right? And so I might not understand the science, but I do understand process. And I've been able to help in those areas. And it's just, it's humbling to be contributing to the field. So I'm really excited to niche back down in that area and do some more work in this medtech field. [00:23:56] Lindsey Dinneen: Yeah. And when it comes to medtech, are there any moments working with clients that stand out to you as just confirmation that you are in the right place in the right industry at the right time? [00:24:10] Chad Bareither: Yeah. So, I know very little about cell therapy, but basically, you grow stem cells and you make them into other type of cells that would be beneficial. If there's people in cell therapy listening to this, you can correct me if I'm wrong. But I mean, it's just, it's mind boggling the science, but I was working with that group and so they were building up their pilot capabilities. And I'm looking at for more like an industrial engineering, manufacturing point of view, developing standard work. And so they're like, "Oh, this is so helpful." And I'm just thinking, I'm like, "I don't even understand what you guys do. So the fact that I can be of any contribution here is..." But I think, pulling back on that is, you need to invest in your strength. So here's, very skilled multi year experience, PhD scientists. And sometimes they just need someone to help them with structuring up the process flow and the capacities and the standard work that they need to do this consistently. And I'm good at that. And so this kind of harkens back to our conversations about entrepreneurship, right, of knowing what you're good at and knowing what you need help with. And I just, I know what I'm good at. And if I find clients that need help in that area, I'm thrilled to support it. But that was one engagement where it was like, "I understand about zero of what you just explained to me, but I think I can help you." [00:25:36] Lindsey Dinneen: I love that. That's fantastic. One of the things I've noticed and really appreciated about the medtech industry is everybody is really good about celebrating and acknowledging how we all fit into the efforts to make it successful. So even if you are not the scientist, or you are also an engineer, but say in my case, I'm not a scientist, I'm not an engineer, but I do have a marketing ability. And the respect mutually that occurs for everybody's contributions, I think is really special in the medtech industry. I'm wondering if you experienced that too. [00:26:17] Chad Bareither: Yeah. You know, I think there's definitely times it's kind of like a family, right? Families fight, the families get along together. There's definitely times where it's like people are like, "Ah, sales department doesn't know what they're doing," or and you're like, but at the end of the day, you recognize you do need all those parts. Unfortunately, these technologies and this research is expensive. So you do need to sell, right? I mean, that's a reality. So you're right. They do all need to get to, and if people slow down, I think you're right. Eventually everyone's, " Yes, we need all these parts to work." I think there's definitely times where people are having a bad time and they get a little grumpy and they're like, "That department doesn't know what they're doing." But it's, but no, I think all the departments are actually really good at what they're doing. So, you just look at the growth that you're seeing in the industry and the valuation of some of these companies and it's, they know what they're doing and they're serving a need that, that we have supporting our health and wellness. And so it's cool. It's really cool to see that all come together. I think you get a very interesting view of that at some of the smaller organizations 'cause there is a lot more of that trust and that team camaraderie, but even, you know, I worked for a fortune 500 company when I was in in med device, as a corporate employee. And you still have that, within the product teams, within the production teams, that they're there to support each other, they're there for the win. There's also a healthy dose of competition in the industry, I think, that makes it a really driven. So it's, it's fun to be a part of it's fast paced because of the personalities. It's fast paced because of the science. It's fast paced because of the competitiveness with other competitors in the industry. So yeah, it's a fun space to be in. [00:28:00] Lindsey Dinneen: Yeah, absolutely. Yeah, so pivoting the conversation just for fun, imagine that you were to be offered a million dollars to teach a master class on anything you want. It could be in your industry, but it doesn't have to be. What would you choose to teach? [00:28:17] Chad Bareither: Yeah. So this is maybe, I'm hopefully not being risk averse here, because I would teach something that I'm already good at teaching. So some of my favorite things to teach are structured problem solving. So most people that are in any type of leadership position got there because they were probably good at solving problems. And I think where we have challenges in, as organizations grow, is that not everyone solves a problem the same way. So how do you develop the new talent coming up to be like those next leaders? And you can't, you shouldn't just rely on individual people to be like, "We'll just find the good problem solvers and they'll go up." I've seen in organizations where you can really multiply, even exponentially grow, the pace of improvement by having structured problem solving in. So that's what I would do. Personally, that's DMAIC formatted problem solving. It's a five phase problem solving approach: Define, Measure, Analyze, Improve and Control. So that's something that I love teaching because I love the lightbulb moment that goes off in people's heads and we teach them that. There is a portion of that is statistics and I love teaching statistics because most people think this is going to be the worst thing ever and I tried to make it a little bit fun and they're like, "Oh, that was fun. And I learned something." And that's what fires me up. So yeah, it would be structured problem solving. That's what I would teach a masterclass on. [00:29:43] Lindsey Dinneen: Okay, I like it. And how do you wish to be remembered after you leave this world? [00:29:49] Chad Bareither: Oh, my. So my love language that I express as in service. Helpful, that's, I think that's the main thing. Whether it's in a client engagement or in the neighborhood or the family, I enjoy helping people. And so whether that's consulting on the launch of a new diagnostic device or helping someone repair their tractor, right? I enjoy engaging and learning with people and solving problems together. So I really like helping people. So I think I'd like to be remembered in that way. Helpful. [00:30:27] Lindsey Dinneen: Yeah, I like it. Absolutely. And then final question. What is one thing that makes you smile every time you see or think about it? [00:30:38] Chad Bareither: One thing. Well, I don't know. I've smiled a lot this week, seeing pictures of my kids when they were younger, because I don't know, maybe my iPhone's just paying tricks on me. It keeps showing pictures of my kids when they were little. So that's it. I think right now, just the point of life that I'm at right now as kids, two of my brothers just had babies as well. So little kids and just me realizing like my kids are never going to be that age again. I've been on travel and seen a lot of little kids in different cities, and it's sweet because it's so simple. Their world is so simple at that age. So I think it makes me smile just because the innocence is there. Yeah. I'm gonna stick with that. [00:31:17] Lindsey Dinneen: Yeah, great answer. It's, it's special to witness and it always brings a smile too. Especially little kids at airports that are dragging their tiny little backpacks or rollie bags behind them and they've got their best stuffed friend. Oh my gosh, it's so cute. [00:31:35] Chad Bareither: So one thing that's been interesting to see is when people have younger kids, and maybe they're misbehaving or maybe they're just excited, right? And the parents are kind of flustered. It's just it's, it brings a smile to my face. Not because the parents are flustered. It's just because I can remember being a parent and you make a big deal out of it, and it's man, but I just appreciate the innocence and the genuine joy that this small human is trying to have right now. And it's, I think, that's the thing right now in my life. That's bringing a smile every time I see it or think about it. [00:32:05] Lindsey Dinneen: I love it. I love it. Well, Chad, this has been an incredible conversation. I really appreciate your insights and advice and everything that you're doing. If anyone's listening and needs some outside support, please definitely get in touch with Chad. We are so honored to be making a donation on your behalf as a thank you for your time today to the Polaris Project, which is a non governmental organization that works to combat and prevent sex and labor trafficking in North America. So thank you for choosing that organization to support. And we just wish you the most continued success as you work to change lives for a better world. [00:32:42] Chad Bareither: Thanks a lot. And you got a lot going on. So I wish you continued success in all your endeavors as well. [00:32:49] Lindsey Dinneen: Awesome. Well, thank you so much. And thank you also to our listeners for tuning in. And if you're feeling as inspired as I am right now, I would love it if you would share this episode with a colleague or two, and we will catch you next time. [00:33:04] Ben Trombold: The Leading Difference is brought to you by Velentium. Velentium is a full-service CDMO with 100% in-house capability to design, develop, and manufacture medical devices from class two wearables to class three active implantable medical devices. Velentium specializes in active implantables, leads, programmers, and accessories across a wide range of indications, such as neuromodulation, deep brain stimulation, cardiac management, and diabetes management. Velentium's core competencies include electrical, firmware, and mechanical design, mobile apps, embedded cybersecurity, human factors and usability, automated test systems, systems engineering, and contract manufacturing. Velentium works with clients worldwide, from startups seeking funding to established Fortune 100 companies. Visit velentium.com to explore your next step in medical device development.

Since the COVID pandemic accelerated reliance on unsupervised ‘take-home exams', the education system has found itself with a fundamental problem –– one which has only been exacerbated by the recent rise in easily-accessible AI tools like ChatGPT. And new research shows just how great the scale and impact of the problem is for the education system and Early Careers teams alike. Dr Peter Scarfe, Associate Professor, and Dr Etienne Roesch, Professor of Applied Statistics & Cognitive Science, both at the University of Reading, are two of the minds behind a recent groundbreaking study which proved just how easy it is for students to not only use AI in exams but to outperform real students. And most importantly –– evade AI detection in the process. Join Robert, Peter and Etienne for the first episode of our new season of the TA Disruptors podcast on the impact of the AI-enabled candidate on recruitment. Our guests dive into:

On New York University Week:  Receiving a diagnosis can be done from behind a computer screen, but is it as reliable as an in-person visit? Daphna Harel, associate professor of applied statistics, explores this question. Daphna Harel is an Associate Professor of Applied Statistics at the Steinhardt School of Culture, Education, and Human Development at […]

Public Health Careers podcast episode with Eric J. Daza, DrPH, MPS

Greg Hudnall - Hope Squad: Working to Spread Hope and Prevent Suicide. This is episode 706 of Teaching Learning Leading K12, an audio podcast. Greg Hudnall is the current Chief Executive Officer of Hope Squad, Inc. He has a dual Bachelor of Science in Applied Statistics and a Bachelor of Arts in Spanish from BYU. He received his MBA from Indiana University and now currently resides in Cincinnati, OH. He spent a decade in corporate America at The Kroger Co. and Johnson & Johnson in process improvement, marketing, and sales roles. Greg firmly believes that good personal mental health habits, active listening, and reaching out to trusted peers have the ability to save lives. His personality type is ISTJ or logistician. His 5 StrengthsFinder results are Deliberative, Input, Restorative, Learner, and Discipline. When he's not at work he enjoys hiking, exercise, and reading. Fun facts: he's climbed 14 of Colorado's 58 fourteeners. He's been to 5 continents and lived in 4 different countries. His next bucket list item is Mount Kilimanjaro. Our focus is Hope Squad. Awesome program! Great talk! Before you go... You could help support this podcast by Buying Me A Coffee. Not really buying me something to drink but clicking on the link on my home page at https://stevenmiletto.com for Buy Me a Coffee or by going to this link Buy Me a Coffee. This would allow you to donate to help the show address the costs associated with producing the podcast from upgrading gear to the fees associated with producing the show. That would be cool. Thanks for thinking about it.  Hey, I've got another favor...could you share the podcast with one of your friends, colleagues, and family members? Hmmm? What do you think? Thank you! You are AWESOME! Thanks so much! Connect & Learn More: https://hopesquad.com/ https://twitter.com/HopeSquads https://instagram.com/hopesquad https://www.facebook.com/339861456463343 https://www.linkedin.com/in/greghudnall/ https://tiktok.com/@hopesquad gregjr@hopesquad.com   Length - 31:46

Cristhiam joined Procter & Gamble Beauty Consumer Measurement Sciences group about 1 year ago as a Sensory Scientist for the Skin Care Category. Before joining P&G Cristhiam worked in product research, sensory science, and quality control within the U.S. and Nicaraguan food industries in addition to post-graduate internships in food engineering and packaging, human nutrition, and research and development at Louisiana State University, Texas A&M, and Ventura Foods.   Cristhiam obtained her MS & PhD degrees in Nutrition and Food Sciences with a Minor in Applied Statistics from Louisiana State University. Her doctoral research integrated statistical methods for the analysis of formulation, elicited emotions, and cognitive cues effect on overall acceptability and purchase behavior towards products containing edible cricket protein at different stages of the consumer-product interaction. Her research uncovered important insights for the development of more sustainable products containing insect protein. Cristhiam on LinkedIn: https://www.linkedin.com/in/cristhiam-g-curran/   To learn more about Aigora, please visit www.aigora.com

In part II of our Olympics special, we meet more of the Olympic entourage supporting Luxembourg's athletes in Paris this summer, plus more sport experts. - Raymond Conzemius - Chef de mission of Team Lëtzebuerg for the Olympic Games in Paris 2024, Technical Sports Director at COSL - Christophe Ley - Associate Professor of Applied Statistics at the University of Luxembourg - Aude Aguilaniu - Physiotherapist, ex elite athlete (Ski Cross) - Max Englaro - Strength & Conditioning, & Rehab Coach U23 FC Augsburg - Frank Muller - Sport Psychologist - Nina Goedert - Physiotherapist Raymond Conzemius, Chef de Mission of Team Lëtzebuerg for the Olympic Games in Paris 2024 joins me with some of the Olympic Team's entourage including sport psychologist Frank Müller and physiotherapist Nina Goedert. Christophe Ley discusses the increasing use of statistics in sport science and many accompanied sports ventures. Strength & Conditioning, & Rehab Coach for U23 FC Augsburg, Max Englaro, uses such metrics to work with his footballers. And Aude Aguilaniu, now a physiotherapist, previously an Olympic level athlete for Ski Cross talks about the absolute need to build resilience after career-shattering injuries. Raymond is a former international athlete in high jump, and still holds the national record with 2.22m. Unfortunately he didn't have the chance to participate in the Olympic Games or World Championships but has happily found a career supporting others to attain that dream.   Conzemius is the Founder and Former director of Sportlycée, the sport secondary school in Luxembourg, and highlights the importance of an integrated approach to sports development in Luxembourg. Max Englaro is a Strength & Conditioning, and Rehab Coach for FC Augsburg U23. Prior to this, he was Head of Performance in the Vienna Football Academy. Max observes how young children or adolescents are talent spotted and then developed into sports stars with the help of targeted training, nutrition, medication and sleep, to name but a few of the metrics. With increasing emphasis on sport sciences the measurements and data around elite performance can enhance results. Christophe Ley, Associate Professor of Applied Statistics at the University of Luxembourg, President of the Luxembourg Statistical Society, President of ECAS (European Center for Advanced Statistics Courses) and leader of the international network S-TRAINING (bridging sports science and data science) is, in fact, the catalyst of these two week's of Olympic conversations. Christophe and Yves Dominicy (from last week's show) have written books on statistics in sport. Through chosen measurements it is possible to use maths to predict outcomes of matches. The accuracy of such predictions naturally depends on many factors. For instance, handball will give you about 81% accuracy compared to football where, apparently, more luck is involved in scoring and there are generally fewer scores. So with football the outcome of positive prediction stands at about 65%. Sport medicine and metrics is a fast growing industry, even for non-professional athletes, with the possibility of wearables and nutrition information available to us all. However, data science and AI is also vital to help prevent injury. Christophe will be organising the international MathSport Conference next year in Luxembourg, June 2025. https://math.uni.lu/midas/events/mathsports2025/ Aude Aguilaniu is now a physiotherapist and researcher, having previously been a world-class skier. Aude actually qualified for the Sotchi Winter Olympics in 2014 but was seriously injured just a few months before and so couldn't participate. She talks about resilience building, injury prevention and her latest research project on injury prevention: Healthy Active. Frank Müller is a former competitive basketball player and now a sport psychologist at the Sportlycée in Luxembourg. He is also an external expert for the LIHPS (Luxembourg Institute for High Performance in Sports) and the COSL (National Olympic Committee), providing psychological support to elite athletes and coaches. Frank talks about his different responsibilities and how he coaches the minds of elite athletes for all possible eventualities. He also works with the group around that athlete which includes coaches, physios and parents. As with so many things, being an elite athlete means that you sit in the centre of a team of experts. Nina Goedert, a sports physiotherapist, reiterates the absolute importance of communication in a cross-disciplinary collaboration. Nina Goedert works with athletes of all ages and levels, those dealing with injury and those working on prevention strategies. She has participated in multiple national and international sports events in her role as a sports physio, including the Tokyo Olympic Games 2021, World Games 2022, European Games 2019 & 2023, Games of the Small States of Europe 2019 & 2023, and several European Championships in Karate, etc.). Nina will be part of the team supporting the athletes in Paris this summer. We wish all of the athletes the very best of luck this summer in the Olympic Games, and the supporting entourage too! https://teamletzebuerg.lu/ https://www.fcaugsburg.de/games/?team=u23 www.sportlycee.lu https://math.uni.lu/midas/events/mathsports2025/ https://www.linkedin.com/in/raymond-conzemius-328a9147/ https://www.linkedin.com/in/christophe-ley-b71607166/ https://www.linkedin.com/in/aude-aguilaniu-24a05343/

Barbara Kowalcyk, Ph.D., M.A., is an Associate Professor in the Department of Exercise and Nutrition Sciences and the Director of the Food Policy Institute at George Washington University's (GW's) Milken Institute School of Public Health. She also has an appointment in the U.S. Department of Environmental and Occupational Health and is a fellow with the Sumner M. Redstone Global Center for Prevention and Wellness. Dr. Kowalcyk's research spans a range of topics related to food safety and infectious foodborne disease, and their intersection with nutrition security. She has extensively used epidemiologic methods, data analytics, and risk analysis to assess food safety risks and potential intervention strategies in both the U.S. and the Global South. Prior to joining GW in 2023, Dr. Kowalcyk was faculty at Ohio State University with appointments in the Department of Food Science and Technology and the Department of Environmental Health Sciences, and directed the Center for Foodborne Illness Research and Prevention (CFI), a nonprofit organization she co-founded in 2006. Prior to joining OSU, she was a senior food safety and public health scientist at RTI International and a research assistant professor in the Department of Food, Bioprocessing, and Nutrition Science at North Carolina State University. Dr. Kowalcyk holds a B.A. degree in mathematics from the University of Dayton, an M.A. degree in Applied Statistics from the University of Pittsburgh, and a Ph.D. in Environmental Health from the University of Cincinnati. She has served on many national committees, including two National Academy of Sciences committees and her current appointment to the U.S. Food and Drug Administration's (FDA's) Science Board.   In this episode of Food Safety Matters, we speak with Dr. Kowalcyk [39:50] about: Her research and advocacy work in the food safety realm, which focuses on advancing equitable food systems that promote public health and prevent foodborne illness How Dr. Kowalcyk's background and personal experiences shaped her career in food safety The interconnectedness of food safety, nutrition, and food security, and the need for an integrated approach to drive improvement in these three areas Dr. Kowalcyk's experience as part of the Reagan-Udall Foundation independent panel that conducted the 2022 evaluation of FDA's operations, and her impressions of the proposed reorganization plan for the agency's Human Foods Program The benefits of whole genome sequencing (WGS) and other technological advancements in foodborne illness outbreak detection and monitoring, and why “boots-on-the-ground” data is still crucial Current food safety challenges on Dr. Kowalcyk's radar, like the effects of climate change on the safety of water used in food production and the need for workforce development in the food safety industry. News and Resources FDA Publishes FSMA Pre-Harvest Agricultural Water Final Rule [7:41] USDA Testing Retail Ground Beef for HPAI H5N1; Maintains That U.S. Meat Supply is Safe [17:28] FDA Testing Finds HPAI in Retail Milk Samples; Research Required to Determine Infectivity, Food Safety Risk Florida Becomes First State to Ban Cell-Based Meat [23:50] Alabama Poised to Ban Cell-Based Meat Thanks, Sesame: U.S. Food Recalls Due to Undeclared Allergens Skyrocketed in 2023, Causing Half of All Food Recalls [32:53] Food Safety Summit 2024 Keynote: On-Demand Replay Now AvailableFood Industry Counsel—Food Recall Search We Want to Hear from You! Please send us your questions and suggestions to podcast@food-safety.com

In today's episode, we're joined by two of Moser's finest:  our Vice President of the Data & Analytics Division, Adrienne Watts, and the Director of Strategy for our Data & Analytics Division, Cody Friend.  We're talking  about how AI can enhance jobs and productivity.Adrienne Watts is Vice President of Data & Analytics for Moser Consulting. She brings more than 20 years of experience leveraging data to drive business growth and innovation. She has worked with a diverse range of clients, including large Fortune 500 companies, small-to-medium businesses, and non-profit organizations. As a thought leader, she enjoys partnering with clients to develop an organization's strategy, helping them gain value from their data and transform "noise" into narrative via data visualization and advanced analytics. Adrienne has a passion for leveraging AI and Machine Learning to improve the human experience and automate processes. Her role at Moser provides the opportunity to partner with all kinds of organizations, helping them unravel the potential within their data and achieve measurable results. She has a Bachelor's degree in Computer Science from DePauw University, a Master's in Business Administration from Anderson University, and a commitment to continuous learning as demonstrated through certifications, including MIT's professional education program, studying Machine Learning and Applied Statistics for data science. No matter where you are in your D&A journey: just beginning to think about exploring options & unsure of where to start, or well-advanced with a dedicated team, she welcomes a conversation - reach out to chat about strategies and opportunities in this dynamic field. Cody Friend is a talented strategist and data expert with more than a decade of experience in various industries. As the Director of Strategy in Data & Analytics at Moser Consulting, he leads a team of professionals in delivering innovative solutions for clients in industries ranging from healthcare to finance. Prior to joining Moser, Cody worked as a data engineer for a large regional bank, where he honed his technical skills and developed a deep understanding of the role data plays in business strategy. He also spent six years in higher education, working for both public and private universities, where he helped to shape the next generation of professionals. Cody earned his MBA with a concentration in Finance from Anderson University, where he developed a comprehensive understanding of financial strategy and management. He also holds a Bachelor of Arts in Computer Science, which has been instrumental in his success in the field of data analytics. 

As the owner and principal consultant of Bareither Group Consulting, Chad Bareithet enables organizations for sustainable continuous improvement. Chad partners with his clients to clarify and deploy their strategy, make process improvements to achieve those goals, and establish a system for daily management of the business. Why do most companies not achieve their strategic goals? How should a leader think about starting their continuous improvement journey? How do I identify problems? What is the key to sustaining performance? How do you create a culture of continuous improvement? Chad Bareithet As the owner and principal consultant of Bareither Group Consulting, Chad Bareithet enables organizations for sustainable continuous improvement. Chad partners with his clients to clarify and deploy their strategy, make process improvements to achieve those goals, and establish a system for daily management of the business. These three elements of The Focus and Align FrameworkTM are the subject of a forthcoming book to help even more organizations and leaders Improve LESS … and get better performance. Chad is a certified Lean Six Sigma Master Black Belt and holds a bachelor's degree in Mechanical Engineering from Michigan Technological University as well as master's degrees in both Industrial & Systems Engineering and Applied Statistics from Rutgers University. Chad and his wife reside in Southwest Michigan with their three children. Excellent Executive Coaching Podcast If you have enjoyed this episode, subscribe to our podcast on iTunes. We would love for you to leave a review. The EEC podcasts are sponsored by MKB Excellent Executive Coaching that helps you get from where you are to where you want to be with customized leadership and coaching development programs. MKB Excellent Executive Coaching offers leadership development programs to generate action, learning, and change that is aligned with your authentic self and values. Transform your dreams into reality and invest in yourself by scheduling a discovery session with Dr. Katrina Burrus, MCC to reach your goals. Your host is Dr. Katrina Burrus, MCC, founder and general manager of Excellent Executive Coaching a company specialized in leadership development.

Episode page with video, transcript, and more My guest for Episode #494 of the Lean Blog Interviews Podcast is Chad Bareither, the founder and principal consultant of Bareither Group Consulting. He brings a wealth of experience as a change agent in the corporate world, having worked with organizations that include several Fortune 500 companies. He's now the author of a new book, Improve LESS: The Focus and Align Framework for Sustainable Continuous Improvement. Chad holds a Bachelor's degree in Mechanical Engineering from Michigan Technological University, is a certified Lean Six Sigma Master Black Belt, and has further honed his expertise with Master's degrees in both Industrial & Systems Engineering, and Applied Statistics from Rutgers University. In this episode, we discuss his experience in various industries where, of course, Lean is not about building cars. We also discuss his book, the "Focus and Align Framework," and why trying to improve less can lead to greater results. Questions, Notes, and Highlights: What's your Lean origin story? Civilian role with the U.S. Army – working with the manufacturers / suppliers vs. internal Army processes? Can't copy and paste? “We don't build cars”?? Becoming a consultant? Being an outsider vs. insider – what have you learned about that? The story behind the book — why this book? Tell us about the common problem statement and the current state — trying to do too many things, being too busy? The “focus and align” framework? The podcast is sponsored by Stiles Associates, now in its 30th year of business. They are the go-to Lean recruiting firm serving the manufacturing, private equity, and healthcare industries. Learn more. This podcast was also brought to you by Arena, a PTC Business. Arena is the proven market leader in Cloud Product Lifecycle Management (PLM) with over 1,400 customers worldwide. Visit the link arenasolutions.com/lean to learn more about how Arena can help speed product releases with one connected system. This podcast is part of the #LeanCommunicators network.   

7.35 wades into the ‘23 WS matchup (0:38), intros Bayesian Stats 101 for Fantasy Baseball (7:03), and reviews Joey Votto (18:37).

Final introductory episode. It's a quick one because next episode we will cover a new topic.

Welcome to Episode 555 of the Yeukai Business Show. In this episode, Yeukai Kajidori and Chad Bareither talk about the importance of having proper processes in place and how to implement them for business success. So, if you want to know more about The Power of Streamlined Processes, tune in now! In this episode, you'll discover:  The importance of processes in business transformationThe Power of Proper ProcessesSteps to Establish Effective ProcessesOvercoming Common Challenges About Chad Bareither Chad has years of experience as a change agent for larger organizations, including several Fortune 500 companies, Chad takes the operational excellence mindset beyond the “shop floor”. The owner and principal consultant of Bareither Group Consulting, and he enables organizations for sustainable continuous improvement. Chad partners with his clients to clarify and deploy their strategy, make process improvements to achieve those goals, and establish a system for daily management of operations. Chad is a certified Lean Six Sigma Master Black Belt and holds a bachelor's degree in Mechanical Engineering from Michigan Technological University as well as master's degrees in both Industrial and Systems Engineering and Applied Statistics from Rutgers University. More Information  Learn more about The Power of Streamlined Processes at https://www.bareithergroup.com/ LinkedIn: https://www.linkedin.com/in/chad-bareither-32712222/ Facebook: https://www.facebook.com/BareitherGroup Thanks for Tuning In! Thanks so much for being with us this week. Have some feedback you'd like to share? Please leave a note in the comments section below! If you enjoyed this episode on How to Expand Your Business, please share it with your friends by using the social media buttons you see at the bottom of the post. Don't forget to subscribe to the show on iTunes to get automatic episode updates for our "Yeukai Business Show !" And, finally, please take a minute to leave us an honest review and rating on iTunes. They really help us out when it comes to the ranking of the show and I make it a point to read every single one of the reviews we get. Please leave a review right now

#enterpriseai #generativeai #aiethics Harvard Business School professor Iavor Bojinov explains how to make enterprise AI projects successful, given their high rate of failure. Here are his key points:► Prioritize Projects: Focus on AI initiatives that align with your business goals and are feasible to implement.►Leadership and Culture: Make sure your company culture is open to AI, with leaders who grasp both the technical and commercial dimensions.►Scalable Systems: Develop an 'AI factory' to streamline and scale AI development.►Vendor Flexibility: When working with outside partners, don't lock yourself into one platform. Be ready to switch if necessary.►Trust: Ensure that AI systems are transparent, audit for bias, and establish clear lines of accountability for failures.Bojinov stresses that each stage of an AI project, from conception to ongoing management, needs expertise, adaptability, a receptive culture, and trust. With a well-thought-out strategy, companies can navigate typical challenges and harness AI's full potential.Our guest co-host for this episode is QuHarrison Terry.Read the complete transcript and see more of CXOTalk: www.cxotalk.com/episode/harvard-business-school-professor-how-to-lead-enterprise-aiSubscribe for more: www.cxotalk.com/subscribeIavor Bojinov is an Assistant Professor of Business Administration and the Richard Hodgson Fellow at Harvard Business School. He is the co-PI of the AI and Data Science Operations Lab and a faculty affiliate in the Department of Statistics at Harvard University and the Harvard Data Science Initiative. His research and writings center on data science strategy and operations, aiming to understand how companies should overcome the methodological and operational challenges presented by the novel applications of AI. His work has been published in top academic journals such as Annals of Applied Statistics, Biometrika, The Journal of the American Statistical Association, Quantitative Economics, Management Science, and Science, and has been cited in Forbes, The New York Times, The Washingon Post, and Reuters, among other outlets. Before joining Harvard Business School, Professor Bojinov worked as a data scientist leading the causal inference effort within the Applied Research Group at LinkedIn. He holds a Ph.D. and an MA in Statistics from Harvard and an MSci in Mathematics from King's College London.QuHarrison Terry is head of growth marketing at Mark Cuban Companies, a Texas venture capital firm, where he advises and assists portfolio companies with their marketing strategies and objectives.Michael Krigsman is an industry analyst and publisher of CXOTalk. For three decades, he has advised enterprise technology companies on market messaging and positioning strategy. He has written over 1,000 blogs on leadership and digital transformation and created almost 1,000 video interviews with the world's top business leaders on these topics. His work has been referenced in the media over 1,000 times and in over 50 books. He has presented and moderated panels at numerous industry events around the world.

In this episode of Transforming Trauma, Emily is joined by Salman Alawadi, a NARM practitioner and Ph.D. candidate in Research, Measurement, and Evaluation at the University of Miami, for a fascinating discussion about the challenges of developing an evidence-based standard for NARM and distilling that research into accessible data.  In medical terminology, “evidenced-based” is a gold star endorsement awarded to recurrent standards of proof that arise from comparison studies of a specific treatment protocol or prevention. Is it possible to validate NARM's efficacy using those same evidence-based standards? Should we? Salman calls out psychology's fixation with the medical model and advocates for creating more rigorous criteria to understand a psychological intervention.  About Salman Alawadi:  Salman Alawadi is a psychotherapist from Kuwait. He holds a Master of Science in Applied Statistics and Psychometrics from Boston College and is currently a Ph.D. candidate in Research, Measurement, and Evaluation at the University of Miami. Salman worked with Dr. Mary Zanarini in the Laboratory for the Study of Adult Development at McLean Hospital, Harvard Medical School. In addition to being a NARM therapist, Salman is also trained in Somatic Experiencing, Integrative Somatic Psychology, Hakomi, Neuroaffective Touch, Mentalization-Based Therapy, and Intensive Short-Term Dynamic Psychotherapy. Learn More: LinkedIn To read the full show notes and discover more resources visit https://www.narmtraining.com/podcast *** NARM Training Institute https://www.NARMtraining.com View upcoming trainings: https://narmtraining.com/schedule Join the Inner Circle: https://narmtraining.com/online-learning/inner-circle Sign up for a free preview of The NARM Inner Circle Online Membership Program: https://www.narmtraining.com/freetrial *** The NARM Training Institute provides tools for transforming complex trauma through: in-person and online trainings for mental health care professionals; in-person and online workshops on complex trauma and how it interplays with areas like addiction, parenting, and cultural trauma; an online self-paced learning program, the NARM Inner Circle; and other trauma-informed learning resources.   We want to connect with you! Facebook @NARMtraining YouTube Instagram @thenarmtraininginstitute

This episode starts and ends kind of abruptly. I was in a rush while recording.

Derek Morris is a virtual Chief Information Security Officer (vCISO) with almost 3 decades in IT, Information Security, Cybersecurity. He possesses numerous industry certifications including: CISSP, CISM, CISA, CDPSE, PCI-QSA, CCSFP, CCNA, and MCSA. Bachelor's Degree in Computer Information Systems from Bryant University with a minor in Applied Statistics. We discuss the virtual CISO space and what to look for in a virtual CISO, including "IT empathy". --- Send in a voice message: https://podcasters.spotify.com/pod/show/virtual-ciso-moment/message

Hey guys. Episode 2 is out let me know any of your questions on twitter. Sorry about the large gap between uploads. I'll hopefully sort that out soon.

The conversation this week is with Sam Tyner-Monroe. Sam is an accomplished, applied statistician and data scientist with a PhD in statistics from Iowa State University. She has expertise in data visualization, and deep experience in developing and applying statistical modeling and machine learning techniques to the social and physical sciences, as well as within the federal government. Currently, she's Managing Director of Accountable AI at DLA Piper If you are interested in learning about how AI is being applied across multiple industries, be sure to join us at a future AppliedAI Monthly meetup and help support us so we can make future Emerging Technologies North non-profit events!Emerging Technologies NorthAppliedAI MeetupResources and Topics Mentioned in this EpisodeSam Tyner-Monroe on TwitterR-Ladies GlobalR-Ladies Washington D.C.DLA PiperAccelerated UnderwritingPrediction Machines: The Simple Economics of Artificial IntelligenceFiveThirtyEight.comPosit ConferenceWomen in Statistics and Data ScienceData FeminismEnjoy!Your host,Justin Grammens

My guest is Ross Farrelly. Ross is the Director of Data Science and Artificial Intelligence for IBM Asia Pacific. He works with companies throughout the region to develop and execute on their strategies to adopt and realise the benefits of predictive analytics and AI. He has a Master of Applied Statistics, a Masters of Applied Ethics, a first class honours degree in Pure Mathematics and a PhD in Information Systems.Some of the highlights of our conversation include exploring the ethical questions that AI provokes, the explosion of ChatGPT, how AI is helping the planet, and what organisational leaders need to be understanding about AI to lead more effectively in today's world.Enjoy the conversation.   You can reach out to Ross directly at: https://www.linkedin.com/in/rossfarrelly  To learn more about what it takes to be an evolved leader, and to check out our other podcast episodes, go to:  https://www.evolvedstrategy.com.au Please note,  the views and opinions expressed in this podcast are the personal opinions of the speakers and do not necessarily reflect the views or positions of any entities they represent.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Decision-making and cost effectiveness analyses rarely get as important as in the health systems — where matters of life and death are not a metaphor. Bayesian statistical modeling is extremely helpful in this field, with its ability to quantify uncertainty, include domain knowledge, and incorporate causal reasoning.Specialized in all these topics, Gianluca Baio was the person to talk to for this episode. He'll tell us about this kind of models, and how to understand them.Gianluca is currently the head of the department of Statistical Science at University College London. He studied Statistics and Economics at the University of Florence (Italy), and completed a PhD in Applied Statistics, again at the beautiful University of Florence.He's also a very skilled pizzaiolo — so now I have two reasons to come back to visit Tuscany…Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, David Haas, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Trey Causey, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, and Arkady.Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)Links from the show:Gianluca's website: https://gianluca.statistica.it/Gianluca on GitHub: https://github.com/giabaio Gianluca on Mastodon: https://mas.to/@gianlubaioGianluca on Twitter: https://twitter.com/gianlubaioGianluca on...

#ai #aifailure Join us on CXOTalk episode 778 as we delve into the complexities of AI failure with our guest, Iavor Bojinov, Assistant Professor at Harvard Business School, and QuHarrison Terry, Head of Growth Marketing at Mark Cuban Companies. In this episode, we explore the various types of AI failures that can occur, the causes behind them, and how they differ from traditional technology and IT projects.Our experts also address the implications of these differences and distinguish between technology failures and human errors that can cause AI failures. In the second segment, we discuss solutions to address AI failure, including practical advice on how to avoid it in the first place. We also touch on the crucial ethical and privacy considerations associated with AI.This is a must-watch episode for business leaders building an AI group within their organization. Our guests offer valuable insights into AI governance, which can help prevent failures and maximize opportunities.The conversation includes these topics:► Understanding AI operations and ai project failures► Differences between AI projects and traditional technology or IT projects: AI is probabilistic► Five steps of an AI project--- Step 1: Project Selection - Choosing the Right Project--- Step 2: Development - Building the Prototype--- Step 3: Evaluation - Testing on Real People--- Step 4: Deployment and Scaling - Going from Pilot to Full Launch--- Step 5: Management - Monitoring and Preventing Failures► The shift from rule-based expert systems to probabilistic AI► Implementing AI in small businesses► AI pilot projects should have direct implications on revenue► Categories of AI failure► Successful projects integrate AI into processes and business operations► Data sets are a key difference between traditional IT projects and AI projects► Digital Transformation vs. AI► Ethical considerations of AI► Principles of privacy by design► Addressing bias in decision-making based on data► Governance and regulation of AI► Advice to business and technology leaders on preventing AI failuresSubscribe to the CXOTalk newsletter: https://www.cxotalk.comRead the complete transcript and watch more interviews: https://www.cxotalk.com/episode/why-ai-projects-fail-how-stop-itIavor Bojinov is an Assistant Professor of Business Administration and the Richard Hodgson Fellow at Harvard Business School. He is the co-PI of the AI and Data Science Operations Lab and a faculty affiliate in the Department of Statistics at Harvard University and the Harvard Data Science Initiative. His research and writings center on data science strategy and operations, aiming to understand how companies should overcome the methodological and operational challenges presented by the novel applications of AI. His work has been published in top academic journals such as Annals of Applied Statistics, Biometrika, The Journal of the American Statistical Association, Quantitative Economics, Management Science, and Science, and has been cited in Forbes, The New York Times, The Washingon Post, and Reuters, among other outlets.Professor Bojinov is also the co-creator of the first-year required MBA course “Data Science for Managers” and has previously taught the “Competing in the Age of AI” and “Technology and Operations Management” courses. Before joining Harvard Business School, Professor Bojinov worked as a data scientist leading the causal inference effort within the Applied Research Group at LinkedIn. He holds a Ph.D. and an MA in Statistics from Harvard and an MSci in Mathematics from King's College London.QuHarrison Terry is Head of Growth Marketing at Mark Cuban Companies, a Dallas, Texas venture capital firm, where he advises and assists portfolio companies with their marketing strategies and objectives.Previously, he led marketing at Redox, focusing on lead acquisition, new user experience, events, and content marketing. QuHarrison has been featured on CNN, Harvard Business Review, WIRED, Forbes and is the co-host of CNBC's Primetime Series – No Retreat: Business Bootcamp. As a speaker and moderator QuHarrison has presented at CES, TEDx, Techsylvania in Romania, Persol Holdings in Tokyo, SXSW in Austin, TX, and more. QuHarrison is a 4x recipient of Linkedin's top voices in Technology award.

What does the future of data science and machine learning have to teach you about life?   How can you take the best parts of your work life and use them to optimize your home life?   In this episode, we follow the data and learn as we go with Developer Advocate at CometML, Kristen Kehrer. She has been awarded “LinkedIn Top Voice” in data science and continues to share remarkable content with her audience of over 88,000 technical leaders.   Her passion for machine learning is mirrored by passion for optimizing her life, and we explore both together today.   Kristen is a former Data Science instructor at UC Berkeley Ext, Faculty/SME at Emeritus Institute of Management and Founder of Data Moves Me, LLC. Kristen holds an MS in Applied Statistics from Worcester Polytechnic Institute and a BS in Mathematics.   So press play and let's chat… it's time to take a look at the data of our lives and iterate to the next level!   >> Then join The Happy Engineer Community online and get access to bonus content and live coaching in our free group >>   https://www.facebook.com/groups/thehappyengineer    ==========================   SHOW NOTES: Find all the links from this episode and more >>   www.theHappyEngineerPodcast.com   ==========================   COACHING: Ready for promotion, more money, and more FUN in your career? Then let's chat!   Book your FREE session for podcast listeners at www.CareerClarityCall.com   ==========================   Rate, Review, and Follow   “I love Zach and The Happy Engineer Podcast.” If that sounds like you, please consider rating and reviewing the show! This helps me support more engineers -- just like you -- take the next step toward the career and life that they desire. On Apple Podcasts, click our show, scroll to the bottom, tap to rate with five stars, and select “Write a Review.” Then be sure to let me know what you loved most about the episode!   Remember, we only spread our message when you share this episode with others that need it. So if you enjoy this podcast, please SHARE it on your social media and tag @TheHappyEngineerPodcast so I can say hi and thank you.    Also, if you haven't done so already, subscribe to the podcast. I'll be releasing a lot of new content including bonus episodes to the feed and, if you're not subscribed, there's a good chance you'll miss out. Subscribe now!   For all the extras related to this episode, remember to visit >>   www.theHappyEngineerPodcast.com

Today Yash Desai is on the show, the Head of B2B Machine Learning @ Wayfair. He'll be diving into how he manages a team that stretches across multiple business units. In this episode, we cover: Ways to manage a team that stretches across multiple business units Organizing a team into sub-teams or pods Business leaders will often expect you to know about the business. Importance of blocking times Importance of relationship for your business Creating a strategy-based team roadmap Using data to influence stakeholders Having a simple alternative solutions Understanding your roadmap timelines. Having a mindset shift is really important. Transition from consulting to production. Tips for building a cohesive team culture. About today's guest: Yash Desai is a seasoned Machine Learning (ML) leader driving business impact and innovation at top organizations globally. Armed with a Bachelors & Masters in Electrical Engineering and Applied Statistics from IIT Bombay, his ML journey began when it was just an esoteric buzzword. He joined McKinsey right after school and served clients for AI transformation across Asia, Europe, and North America in Energy, Finance, and Healthcare sectors. Post consulting, he took up leadership roles at healthcare giant CVS Health and the home goods e-commerce leader Wayfair. He passionately engages in the ML community by mentoring budding data scientists from under-represented backgrounds and participates in conference panels. Outside work, Yash loves exploring beer gardens, playing cricket, and binge-watching Marvel movies. LinkedIn: www.linkedin.com/in/yashjdesai18 Instagram: https://www.instagram.com/whydesai ________ Thank you so much for checking out this episode of The Tech Trek, and if you enjoyed this episode, please take a minute and leave a quick rating and review on the Apple podcast app! Want to learn more about us? Head over at https://www.elevano.com Have questions or want to cover specific topics with our future guests? Please message me at https://www.linkedin.com/in/amirbormand (Amir Bormand)

Dr. Jennifer Hill, Professor of Applied Statistics at New York University, joins Jon this week for a discussion that covers causality, correlation, and inference in data science. In this episode you will learn: • How causality is central to all applications of data science [4:32] • How correlation does not imply causation [11:12] • What is counterfactual and how to design research to infer causality from the results confidently [21:18] • Jennifer's favorite Bayesian and ML tools for making causal inferences within code [29:14] • Jennifer's new graphical user interface for making causal inferences without the need to write code [38:41] • Tips on learning more about causal inference [43:27] • Why multilevel models are useful [49:21] Additional materials: www.superdatascience.com/607

We welcome Dr. Jennifer Hill, Professor of Applied Statistics at New York University, to the podcast this week for a discussion that covers causality, correlation, and inference in data science. In this episode you will learn: • How causality is central to all applications of data science [4:32] • How correlation does not imply causation [11:12] • What is counterfactual and how to design research to infer causality from the results confidently [21:18] • Jennifer's favorite Bayesian and ML tools for making causal inferences within code [29:14] • Jennifer's new graphical user interface for making causal inferences without the need to write code [38:41] • Tips on learning more about causal inference [43:27] • Why multilevel models are useful [49:21] Additional materials: www.superdatascience.com/607

We welcome Dr. Jennifer Hill, Professor of Applied Statistics at New York University, to the podcast this week for a discussion that covers causality, correlation, and inference in data science. In this episode you will learn: • How causality is central to all applications of data science [4:32] • How correlation does not imply causation [11:12] • What is counterfactual and how to design research to infer causality from the results confidently [21:18] • Jennifer's favorite Bayesian and ML tools for making causal inferences within code [29:14] • Jennifer's new graphical user interface for making causal inferences without the need to write code [38:41] • Tips on learning more about causal inference [43:27] • Why multilevel models are useful [49:21] Additional materials: www.superdatascience.com/607

New #TeesMe Podcast with Sidney Hardee What You'll Hear - The art of starting an investment firm - An overview of financial services - wealth & investment management - When investing, start with what you know, then learn EBITDA - Pay debt, acquire assets, and then be an entrepreneur - The different forms of debt, saving to create a foundation - A lesson from Warren Buffet school of of investing, people - Chasing other people's money, it's in the details - CFA 101, credibility, professionalism, and leveling up - Bermuda, there's plenty of swimming, rum and broker-dealers that love golf - The family foursome Stone Creek Golf Club - The Probabilities Fund - it's about numbers Things you should know Mentions: - Market Wizards — #JackSchwager - The Checklist Manifesto - #AtulGawande - Fairmont Scottsdale@fairmontscotsdl - #MontereyPeninsulaCountryClub - IG: @bigsidhardee Bio Sidney Hardee is the Managing Partner of Hardee Brothers, LLC and Global Investment Advisor for the Probabilities Fund, LLC. He has a broad base of experience in global investing, derivatives research, quantitative analysis, and portfolio management. Sidney is a former Trading Manager at the Bank of NT Butterfield in Bermuda where he led their fixed income and derivatives trading initiatives. He began his career as a Market Analyst at Salomon Brothers focused on European Bond Markets. Later he joined Lehman Brothers in both New York and London as a Bond Trader. He was also a Vice President in both Credit Markets Trading and Global Rates Strategy groups at JPMorgan. A Chartered Financial Analyst (CFA), Sidney is a member of the Alternative Investments committee and the Performance and Risk committee of the CFA Society of New York (CFANY). He is a former member of the United States Investment Performance Committee (USIPC) and current member of Global Promotions committee for the Global Investment Performance (GIPS). He is also a member of the Board of Advisory for the Master of Science Program in Financial Risk Management at the University of Connecticut School of Business. He holds a B.A in Economics and Mathematics from Yale University and holds a M.S in Applied Statistics from Columbia University. ************************* Listen on Apple, Spotify, Google https://anchor.fm/TeesMe #TeesMe #podcast #storiesNeedToBeTold #untoldStories #golf #blackGolfers #blackGolfMatters #2022 #IN18 #IN18Ways #entrepreneur #NYC #hardeebrothers #wealthmanagement #investor #strategies #probabalityFund --- This episode is sponsored by · Anchor: The easiest way to make a podcast. https://anchor.fm/app

For more details on this podcast visit: https://www.notarycoach.com/blog/PodcastEpisode27Episode 27: Clyde shares his journey in building the Notary practice he loves.Clyde Heppner is a full-time mobile notary public and certified loan signing agent in Missouri. He is a graduate of both Carol Ray's Notary2Pro training and Bill Soroka's “Sign and Thrive” Loan Signing Course.Clyde came this career after being the Coordinator of Research for the Kansas City, Missouri Public Schools during its court order desegregation. He left the district for a position with Sprint (now known as T-Mobile) where he worked for 18 years as an executive. Clyde is a graduate of the University of Minnesota ­ Morris, and holds an advanced degree from the University of Nebraska, Lincoln. His training is in Experimental Psychology and Applied Statistics. Clyde spent time as an adjunct faculty member and sat on business advisory boards. In all of his roles, he learned how to make connections with people and solve problems. Like other notaries public, Clyde attempts to follow a standard of care for journaling, but he was unable to find a journal that would accommodate the process he wanted to follow. So, he set out to design a journal that would mirror his work flow. From different design layouts, reviews with notary public experts and loan signing trainers, and a beta trial with notaries public, the Integrity Notary Journal™ emerged. The journal quickly became an Amazon Best Seller and enjoys the support of many notary signing agents today.Learn more about the journal at https://mobilenotarykc.com/integrity-journal Episode Description:Clyde not only created one of the most highly reviewed Notarial journals in history with his Integrity Journal, but he is also building a Notary practice that he loves. Listen in this week as Clyde shares his strategy for building B2B relationships with attorneys, escrow, and more, to diversify his income streams. Episode Highlights:6:41   Self-reflection can give you the information to shift gears, or even pivot, during turning points of life and business.35:59   Following the curiosity string can lead to innovation and passion in your business.49:08   How do you answer your inner critic when it asks, "Who do you think you are?"Full transcription of this podcast: https://www.notarycoach.com/blog/PodcastEpisode27This episode was produced and marketed by the Get Known Podcast Service: www.getknownstrategy.com/podcast-service

Today I had the pleasure of interviewing Kristen Kehrer. Kristen is currently a Developer Advocate at Comet sharing about MLOps best practices.  Since 2010, Kristen has been delivering innovative and actionable machine learning solutions across multiple industries, including the utilities, healthcare, and eCommerce.  Kristen was a LinkedIn Top Voice - Data Science & Analytics in 2018. Previously Kristen was a Data Science instructor at UC Berkeley Ext, Faculty/SME at Emeritus Institute of Management and Founder of Data Moves Me, LLC. Kristen holds an MS in Applied Statistics from Worcester Polytechnic Institute and a BS in Mathematics. In this episode we talk about how Kristen was able to transform her life and her career after hitting rock bottom after college and how she has been able to break negative cycles in her life. We also discuss how social media interacts with mental health, and we learn more about her new role as a developer advocate at Comet ML. I really enjoyed this conversation with Kristen and I hope it inspires you to break some negative cycles in your life.00:00 Introduction02:20 Becoming a Developer Advocate06:03 How did your data journey start?18:05 How did you change your mentality toward a better life?30:17 How did you pick up the software skills coming from a math background?34:27 How did you start producing blog content?38:08 Social Media Presence47:27 Overcoming mental health struggles during the COVID-19 Pandemic55:23 Final remarks

We discuss An Examination of Olympic Sport Climbing Competition Format and Scoring System with Quang Nguyen (@qntkhvn). This paper won the Carnegie Mellon Sports Analytics Conference Reproducible Research Competition in November 2021. Quang Nguyen completed his Master of Science in Applied Statistics at Loyola University Chicago in 2021. He recently spent the Spring 2022 semester working as an instructor in the Dept of Mathematics and Statistics at Loyola. Quang previously completed his undergraduate degree in Mathematics and Data Science at Wittenberg University in Springfield, Ohio. Quang's current interests include statistics in sports, data science, statistics and data science education, and reproducibility. He is a die-hard supporter of Manchester United F.C. of the English Premier League. And last but not least, Quang is excited to join the Dept of Statistics and Data Science at CMU as a first-year PhD student this coming Fall 2022. For additional references mentioned in the show: Quang's blog posts: https://qntkhvn.netlify.app/blog.html Code for paper: https://github.com/qntkhvn/climbing Inducing Any Feasible Level of Correlation to Bivariate Data With Any Marginals R copula package: https://cran.r-project.org/web/packages/copula/index.html and book: http://copula.r-forge.r-project.org/book/ UConn Sports Analytics Symposium (UCSAS) CRAN Task View for Sports Analytics: https://cran.r-project.org/web/views/SportsAnalytics.html

Our guest today is Roger Urwin, Global Head of Investment Content with Willis Towers Watson. Roger joined Watson Wyatt in 1989 to start the firm's investment consulting practice and, under his leadership, grew to a global team of over 600. Roger has been influential with the CFA Institute and their Future of Finance initiative, as well as co-founder of the Thinking Ahead Institute. His investment innovations include three global firsts, one of which is the creation of the first target date and lifestyle defined contribution funds (1988). Roger is also Advisory Director at MSCI Inc. He has a degree in Mathematics and a Masters in Applied Statistics from Oxford University. In today's episode, we speak with Roger about his personal background, including a personal tragedy he experienced, followed by his professional background and his work at the Thinking Ahead Institute. We then do a deep dive on the current state of sustainable investing, which includes greenwashing, comparing investors globally, net zero, proxy voting, benchmarking and more. Our host today is Steve Curley, CFA & his guest host is James Sellon, CFA (Managing Partner, Maseco). Please enjoy the episode. Follow us on Twitter & LinkedIn.

Had a great conversation with classmate Sidney Hardee, who shared some great stories about his time at Yale. His mother was a hug influence on Sidney, as were his fraternity brothers. He also talks about how he has built his own business. Listen in! So you know more about Sidney, he is the Managing Partner of Hardee Brothers, LLC and Global Investment Advisor for the Probabilities Fund, LLC. He has a broad base of experience in global investing, derivatives research, quantitative analysis, and portfolio management. Sidney is a former Trading Manager at the Bank of NT Butterfield in Bermuda where he led their fixed income and derivatives trading initiatives. He began his career as a Market Analyst at Salomon Brothers focused on European Bond Markets. Later he joined Lehman Brothers in both New York and London as a Bond Trader. He was also a Vice President in both Credit Markets Trading and Global Rates Strategy groups at JPMorgan. A Chartered Financial Analyst (CFA), Sidney is a member of the Alternative Investments committee and the Performance and Risk committee of the CFA Society of New York (CFANY). He is a former member of the United States Investment Performance Committee (USIPC) and current member of Global Promotions committee for the Global Investment Performance (GIPS). He is also a member of the Board of Advisory for the Master of Science Program in Financial Risk Management at the University of Connecticut School of Business. He holds a B.A in Economics and Mathematics from Yale University and holds a M.S in Applied Statistics from Columbia University.

Sam Ellis understood early on that the people who lead the technology space as entrepreneurs are the ones who are obsessed with their craft. Sam Ellis is an early stage software VS who studied Research and Applied Statistics at West Point.He served as an Army intelligence officer at U.S. Cyber Command, where he was a software developer and an intelligence leader. Sam went on to be a co-founder of Dutchie, where he developed the first version of the industry-leading online cannabis marketplace software. Sam is now a partner at Scout Ventures, leading SaaS pre-seed, seed, and incubation deals.With a portfolio like that, there's no one better to learn how to lead (and invest) in technological innovation by loving the game and aiming to win big. Things you will learn in this episode:[00:01 - 08:37] Opening Segment I introduce today's guest, Sam EllisSam gives his background How Sam's strict upbringing instilled discipline Enlisting after 9/11The blessing of being pushed in self-development The benefits of fitness on your career [08:38 - 23:56] Leading in Tech Innovation by Loving the GameSam talks about why he went into entrepreneurshipAgency and expression - humans need expression Autonomy to create something beautiful Sam shares his experience in college and his next steps after graduatingEnjoying the intellectual challenge and rigor Craving for more applied practice of the educationHow coding and programming came into play Coding in his spare time instead of catching up on NetflixYou need to love the game Schooling can be great, but it's not the prizeAt some point, you learn how to learn What's missing in today's education Emotional regulation and sophistication The start of Sam's first venture, Dutchie A quick word from our sponsor[23:57 - 38:37] How to Build a Scalable BusinessThe building process of Dutchie How his partners utilized their strengths Operate at a competitive advantage Find some friction - solve the problem Dealing with the pushbacks Plan for success The mindset is we're here to winDefining your win Ex: being the biggest company in your space Focus on an abundance mindset Sam talks about his work with Scout Ventures Pivoting into investing Building up other entrepreneursLooking into the future of technology Tech begets tech More capital is going into leveraged value creation Start looking at emerging regions Look for the visionaries [38:38 - 40:05] Closing Segment Who you know or what you know? Who not how Entrepreneurs need to understand people How to connect with SamLinks below Final words  Tweetable Quotes: “Being an entrepreneur is all about endurance. It's all about just showing up every single day; it's showing up for every single mile, just getting it done… Even when it's a bad deal” - Sam Ellis“That's one of the ways I spot engineers who are passionate and great. It's those people who care enough to understand the leading edge of technology, care enough about their craft… they love the game.” - Sam Ellis“We're not thinking and planning for anything but success… The mindset is we're here to win!” - Sam EllisResources Mentioned: Scout Ventures Blog Want to connect with Sam? You can follow him on Twitter and LinkedIn. If you are in the early stage of your business and know you have massive potential, check out https://www.scout.vc/ and see if you're a fit. Did you love the value that we are putting out in the show? LEAVE A REVIEW and tell us what you think about the episode so we can continue putting out great content just for you! Share this episode and help someone who wants to connect with world-class people. Jump on over to travischappell.com/makemypodcast and let my team make you your very own show!If you want to learn how to build YOUR network, check out my website travischappell.com. You can connect with me on Facebook, Instagram, and Twitter. Be sure to join The Lounge to become part of the community setting up REAL relationships that add value and create investments.Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

In this episode, Chrissie discuss the data sources used for pharmaceutical research and developments, the ethical issues arising with the use of data in the pharmaceutical industry, and what the future of AI looks like in the pharmaceutical industry. About Christine Fletcher Chrissie is the Vice President of Development Statistics at GlaxoSmithKline. She leads a group of statisticians supporting the development of new and approved medicines in immunology, hepatology/gastrointestinal, respiratory, cardiovascular, neuroscience, HIV, global health and infectious disease areas. Chrissie has worked in the Pharmaceutical Industry for 30 years and has experience of developing and commercializing new medicines in a variety of clinical disease areas across all phases of clinical development. She previously worked at Amgen in a variety of leadership roles and began her career working at SmithKline Beecham. Chrissie is actively engaged in statistical societies, pharmaceutical trade associations and initiatives relating to the Pharmaceutical Industry. Chrissie is the Chair of PSI, a Council member of EFSPI, and a member of various European and International Special Interest Groups. Chrissie is a member of the EFPIA Clinical Research Expert Group and is leading the Innovation in Clinical Trials sub team. Chrissie is the lead EFPIA representative for the ICH E9(R1) Working Group, and she is co-leading an EFPIA/EFSPI estimand implementation working group. Chrissie is a Chartered Statistician of the Royal Statistical Society (RSS). Chrissie has an MSc in Applied Statistics and a BSc (Hons) in Statistics with Management Science Techniques. Overview The pharmaceutical industry has had many advancements thanks to data and AI. Chrissie discusses how these advancements have supported the research for vaccines during the pandemic. We spotlight the ethical problems that may arise when using data in the pharmacy industry. From her 30+ years of experience, Chrissie shares her advice for those who are looking to work in the pharmaceutical industry. She closed out the podcast by discussing what most excites her about the future of AI. Learn more about our mission and become a member here: https://www.womenindata.org/ --- Support this podcast: https://anchor.fm/women-in-data/support

In this episode, we reflect on all of our COVID conversations and what they have taught us about accessing science to become better agents of our own decisions. We reference our conversation with Dr. Janina Jeff, an amazing science communicator and geneticist from New Orleans, Louisiana, at the beginning of the pandemic where she shared with us how COVID changed the game for science research and access to the medical community. She also helps us better understand the genetic makeup of the COVID providing the foundation for us to interpret all of the data and trends we have followed throughout the pandemic. With her keen knowledge of history, culture, and science, Dr. Janina shares why there was so much mistrust and misinformation surrounding this virus and the vaccine.  Dr. Janina Jeff has a Ph.D. in Human/Medical Genetics from Vanderbilt University. She also received a Master's degree in Applied Statistics from Vanderbilt University and a Bachelor's degree in Biology and Spanish minor from Spelman. After her Ph.D., she pursued postdoctoral training in the labs of Eimear Kenny, Ph.D. and Erwin Bottinger, MD at Icahn School of Medicine at Mount Sinai. Her research career was focused on population genetics, specifically studying complex and admixed populations (descendants with African ancestry) and discovering population-specific genetic risk factors of common disease. The launch of her podcast, "In Those Genes," won Spotify's Sounds Up Boot Camp. Episodes can be found on Spotify, Apple, and all major podcasting apps.

Joy and Ilona are joined by two students, Naomi and Sanil, who share their inside perspectives on navigating college life, making friends, and loneliness.    Key Takeaways How to navigate campus mental health resources Advice for first-year students who might be feeling disconnected How to find your community How your own openness allows others to feel less alone   About Naomi Alvarado and Sanil Mittal Naomi Alvarado is going to be a Sophomore this fall. She is majoring in Psychology and minoring in Applied Statistics at University of Michigan Dearborn Campus. She loves to read, paint, play the saxophone, and work with children.  She is also the founder of The Unseen United Project and she's also a child research assistant.  Sanil Mittal is currently a Freshman and an Astronomy and Astrophysics Major. He is also one of the LSA Honor students.    Connect with Naomi and Sanil Naomi's Instagram: @naomi.alv_  Sanil's Book: Ask Me Anything: 17 Answers To Your Questions About US College Applications   Connect With Us Instagram: Unlocking College Life

This month we're doing what it says on the packet - implementation science! Marlena is an expert in this field having completed her PhD in the field in 2018. An OT by background, she is the Allied Health Research Translation Lead at Royal Melbourne Hospital. Now a post-doctoral researcher, she also has post-graduate quals in clinical rehabilitation (neurological rehabilitation) and is completing her Masters in Applied Statistics.In addition to chatting about implementing research into clinical practice we talk about her other passion - using assistive technologies in neuro rehab.

Welcome to The Psychologists Podcast, where we talk about all things psychology through a very personal lens.Just in time for #disabilitypridemonth …We're grappling with #neurodiversity today, as clinicians who work with neurodivergent clients, as parents of neurodivergent kids, and as humans who identify as neurodivergent themselves.RESOURCES for Autism, ADHD, emotional difficulties, etc., (feat. Special sections for adults and girls/women) https://www.thepsychologistspodcast.com/resources Honorable Mentions:-NeuroTribes book: https://tinyurl.com/89jscfcn-Community (vs. individual/medical) approach to mental health-How DO you say dyscalculia, anyway?-Kanner's and Asperger's autisms-superpower, disability, or both?-Why do we need labels? (Psychology Today blog post: https://tinyurl.com/35e8uuvx)-Neurodivergent/NT communication (the “double empathy problem”) https://tinyurl.com/4kx5dcnm -Applied Behavior Analysis (ABA) controversy-Broader Autism Phenotype (BAP) and more recently, BAPCO (thank you, Dr. Lindsay McCary): https://pubmed.ncbi.nlm.nih.gov/33412500/-Autism/ADHD overlap-misdiagnosis (borderline bipolar, anyone?)-early research on positive effects of later autism diagnosis https://www.tandfonline.com/doi/full/10.1080/21642850.2019.1684920-neurodivergent cultural identification vs. “mental illness” labeling-self-diagnosis/identification and diagnostic gatekeeping-getting help for understanding your storyDr. Darren Woodlief is a licensed clinical psychologist in Columbia, SC. He specializes in conducting a broad range of diagnostic evaluations for clinical and educational purposes and for treatment planning, including psychoeducational, ADHD, ASD, and mental health assessments, as well as neuropsychological screenings. He earned a Ph.D. in Clinical-Community Psychology and a Certificate in Graduate Study in Applied Statistics from the University of South Carolina. Darren resides near Columbia, SC with his wife, Katie and 16-year-old son, Emerson. He is devastatingly handsome and remarkably quick-witted. Darren is also an avid musician who loves to play, write, record, and otherwise collaborate with other musicians as often as he can fit it in.Darren's band: https://boohagmusic.com/ (well, one of them)—-Gill Strait PhD and Julia Strait PhD are both Licensed Psychologists (TX) and Licensed Specialists in School Psychology (LSSPs, TX). They are alumni of The University of South Carolina School Psychology Doctoral Program (Go Gamecocks).Gill is a teacher, researcher, and supervisor at a university graduate psychology training program.Julia is a testing psychologist at Stepping Stone Therapy in Houston, TX: https://steppingstonetherapy.org/strait/ Instagram: @drjuliatx https://www.instagram.com/drjuliatx/?hl=en

https://youtu.be/OKJjwTEfaKc The gender continuum, why gender is a complex and layered social construct. Jesse Lueck was hired at Prudential Financial just as she finished college and started her career as an associate in Fixed Income and Performance Reporting. She worked her way up to the management level and has transitioned into project management. Ms. Lueck is very active with diversity and inclusion and was appointed as a member of the Board of Directors for one of the business resource groups called Pride at Prudential. Because of her expertise, Ms. Lueck now speaks at other companies on the topics of inclusion in the workplace and the gender continuum. Ms. Lueck earned her B.S. in Mathematics from Seton Hall University and a Master of Science in Applied Statistics from the New Jersey Institute of Technology. Ms. Lueck is an avid soccer player and in her spare time enjoys home renovation and spending time with her wife and dog. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx TEDx Talks

To many scientists, statistics is just a means to an end, but to PhD student Abby Smith, it's the most interesting part! Abby tells us more about what it means to study Applied Statistics and how designing more thorough statistical modeling of social networks can make the scientific outcomes that much better. If you want to learn more about the topics discussed in this episode, check out: Invisible Women: Data Bias in a World Designed for Men by Caroline Criado Pérez (book)The Hidden Influence of Social Networks (TedTalk)How We Make Sure That Nobody Is Counted Twice: A Peek Into HRDAG's Record De-Duplication (article)Has Large-Scale Named-Entity Network Analysis Been Resting on a Flawed Assumption? (scientific article)Don't forget to follow us on Twitter @SpotlightThePod to stay up-to-date on all news and episode releases!Learn more about Northwestern University SPOT on Twitter @SPOTForceNU or at our website spot.northwestern.eduPodcast artwork created by Edie Jiang, available at her website https://ediejiang.weebly.com/ or on Instagram @ediejiangMusic in this episode: Earth by MusicbyAden https://soundcloud.com/musicbyadenCreative Commons — Attribution-ShareAlike 3.0 Unported — CC BY-SA 3.0Free Download / Stream: https://bit.ly/_earthMusic promoted by Audio Library https://youtu.be/5yIbZVOv438

Welcome to our sixth episode! Brian is currently a Master's student and Machine Learning Researcher at UC Berkeley where he studies Electrical Engineering and Computer Science. He recently graduated from UC Berkeley where he graduated with the highest distinction and majored in Computer Science, Applied Mathematics, and Statistics. During college he played an active role in the Student Association for Applied Statistics where he served various roles such as a data science consultant and a senior advisor. Throughout college, Brain also interned as an Algorithm Developer Intern at Hudson River Trading, a Software Development Engineer Intern at Amazon, and an Applied Statistics Research Intern at Lawrence Livermore National Laboratory. In this episode we talk about Brian's machine learning research on hypersonic vehicle simulations and DNA sequencing, how to avoid burnout, and more! Links to check out: - Statistics Undergraduate Student Association: https://susa.berkeley.edu/ YouTube Episode: https://youtu.be/kPaTrBSS7aI

We discuss Bang the Can Slowly: An Investigation into the 2017 Houston Astros with Ryan Elmore (@rtelmore) and Gregory J. Matthews (@StatsInTheWild). This paper was the winner of the Carnegie Mellon Sports Analytics Conference Reproducible Research Competition in October 2020. Ryan Elmore is an Assistant Professor in the Department of Business Information and Analytics in the Daniels College of Business at the University of Denver (DU). He earned his Ph.D. in statistics at Penn State University and worked as a Senior Scientist at the National Renewable Energy Laboratory prior to DU. He has over 20 peer reviewed publications in outlets such as Journal of the American Statistical Association, Biometrika, The American Statistician, Big Data, Journal of Applied Statistics, Journal of Sports Economics, among others. He is currently an Associate Editor for the Journal of Quantitative Analysis in Sports and recently organized the conference “Rocky Mountain Symposium on Analytics in Sports” hosted at DU. Gregory Matthews completed his Ph.D. In statistics at the University of Connecticut in 2011. From 2011-2014, he was a post-doc in the School of Public Health at the University of Massachusetts-Amherst. Since 2014, he has been a professor of statistics at Loyola University Chicago. He was recently promoted to Associate professor with tenure in March 2020. For additional references mentioned in the show: Tony Adams' (@adams_at) Houston Astros trash can banging data website: http://signstealingscandal.com/ Ryan and Greg's GitHub repository with code and data: https://github.com/gjm112/Astros_sign_stealing The causal effect of a timeout at stopping an opposing run in the NBA by Connor Gibbs (@cgibbs_10), Ryan Elmore, and Bailey Fosdick (@baileyfosdick)

Sarah Kellogg has a Master's in Applied Statistics. She also has 3 kidneys. Sarah explains the challenges and the life she lives along with her third kidney, Sidney. Support the show

In my 6th episode I speak to Kathleen about her experiences in the world of business analytics mainly in the banking domain, her approach to analysis, working with business partners, building solid and successful analytics teams, leadership and so much more! If you are thinking of getting into analytics, training to become a data analyst, data scientist or working in the business domain with analytics teams, then is this a MUST watch for you! Kathleen Maley is a business leader specializing in the science of data and analytics. With more than 15 years of experience, she helps her partners achieve outsized business value through the practical application of analytical techniques. She is a cultural change agent in the adoption of analytical strategies, and her passion is found in helping organizations leapfrog her early experience to accelerate their journeys toward value-generating analytics. Kathleen started her career on the leading edge of a revolution in data-based decision making at Bank of America. She built predictive models, ran a model risk management group and led several business-analytics verticals. In her culminating role as the bank's consumer deposits pricing executive, she transformed the business from intuition-based to predictive model-based, creating an efficiency of $65 million per basis point saved. As the head of consumer and digital analytics at KeyBank, Kathleen increased the economic impact of her team by elevating their role from data provider to strategic partner. She established a discipline for measuring realized benefit of analytically informed business initiatives and engineered a shift toward collaboration, ensuring analytical solutions are developed hand-in-hand with business execution plans. Kathleen is a member of the International Institute for Analytics' expert network, board member and volunteer statistician for Turner Syndrome Society of the United States, published writer and frequent speaker. She holds degrees in Mathematics and Applied Statistics. Previously, Kathleen taught high school mathematics and statistics in Costa Rica, Mexico and China. About Samir: Samir is a data strategy and analytics leader, CEO and Founder of datazuum. He has a history of helping data executives and leaders craft and execute their data strategies. His passion for data strategy led him to launch, the Data Accelerator Workshop, and host the popular Data Strategy Show. After a career in both private and public sectors Samir launched the datazuum brand in 2012, with a view to working with executives to deliver data strategy at a time when data was not seen as a business asset. Today datazuum delivers projects across both private and public sectors including: Charities, Financial Services (Banking & Insurance), Government, Housing & Construction, Law Enforcement, Logistics, Media & Publishing, Outsourcing, Postal, Retail, Telecoms, Transport and Utilities. Samir has 20 years of international experience across Europe, North America, and Africa. Is a regular speaker at international conferences, coach / mentor, a charity fundraiser, and youth champion for Working Knowledge - supporting young people to achieve their personal and career goals in life. Samir lives in London with his wife and daughter. Contact details for Samir LinkedIn: Samir Sharma Email: samir@datazuum.com website: www.datazuum.com

Catherine vanVonno is the President and CEO of 20four7VA, one of the most trusted remote staffing companies. She oversees the overall growth and success of the company, leads the short and long-term strategies and manages the company's finances. She also directs the management team when it comes to daily operations, brand management and marketing, client relations, strategic planning and business development areas. While it is certainly a challenge to work with staff from all over the world, her positive attitude and progressive leadership style enabled her to overcome the hurdles and establish a thriving and solid relationship with her team of 40+ talented individuals. On any given day, you'll find Catherine managing her global team of 40+ right from her kitchen table, demonstrating one of the biggest perks of remote work. Before starting 20four7VA, Catherine worked in the healthcare management industry. She has a Master's degree in Industrial-Organizational Psychology and a Ph.D. in Research and Evaluation Methods with a cognate in Applied Statistics from the Virginia Polytechnic Institute and State University. Website link: https://20four7va.com/ Social Media links: https://www.facebook.com/20four7VA/ https://www.instagram.com/20four7va/ https://www.linkedin.com/company/20four7va/

Mathematical law governs just about everything in your daily life, whether you realize it or not. Alex's guest today is Dr. Monica Geist. Dr. Geist has a Bachelor's in Applied Mathematics from CSU, a Masters in Applied Mathematics from CU-Denver, and a Ph.D. in Applied Statistics and Research Methods from UNC. To make a long story short, she is made of math. Dr. Geist and Alex have known each other for some years, as her husband took on the difficult task of tutoring Alex in mathematics during his time at ASU and now serves as his IT consultant. Unless you are Will Hunting, this one might blow your mind!   In this episode, Alex and Monica talk about… Monica's backstory growing up in Boulder How much of life is predicated on written mathematical proofs COVID-19 has changed the experience for students and teachers Zoom has changed the game for interactive teaching online The immediate future of teaching during the pandemic People are unique and everyone learns in different ways We will never go back to how we did things before Monica's proclivity toward teaching has impacted Alex's life Sometimes Alex uses words that he doesn't understand Alex and Monica throw around some old stories Just keep breathing when you face math problems in life   Links to resources: Spiderman Homecoming Zoom   Difficult Story Problems: Q: Why was the mathematician late to work? A: He took the rhombus.   Q: What did the mathematician say when she lost her protractor? A: Where's my protractor?   Q: Why was the fraction apprehensive about marrying the decimal? A: Because he would have to convert.   Q: Why do plants hate math? A: It gives them square roots.   Q: Why was the math book depressed? A: It had a lot of problems. Monica Geist Ph.D.: Monica.Geist@frontrange.edu   Asynchronous Definition: https://www.merriam-webster.com/dictionary/asynchronous   Pythagorean Theorem: https://en.wikipedia.org/wiki/Pythagorean_theorem   Law of Cosines: https://en.wikipedia.org/wiki/Law_of_cosines    Quadratic Equation: https://en.wikipedia.org/wiki/Quadratic_equation   www.TheBuildersJourney.com   Bobby's email: Bobby@PlumbKendall.com Alex's email: Alex@PlumbKendall.com   The Builders Journey Facebook: https://www.facebook.com/thebuildersjourney/   For more information about finding the right remodeler, check out http://remodelvail.com