Podcasts about nsf career award

This week, we present two stories about confronting threats -- whether it's actual physical danger or a threat to your career. Part 1: Climate scientist Kim Cobb is exploring a cave in Borneo when rocks begin to fall. Part 2: Neurobiologist Lyl Tomlinson is startled when he's accused of stealing cocaine from his former lab. Kim Cobb is a researcher who uses corals and cave stalagmites to probe the mechanisms of past, present, and future climate change. Kim has sailed on multiple oceanographic cruises to the deep tropics and led caving expeditions to the rainforests of Borneo in support of her research. Kim has received numerous awards for her research, most notably a NSF CAREER Award in 2007, a Presidential Early Career Award for Scientists and Engineers in 2008, and the EGU Hans Oeschger Medal in 2020. She served as Lead Author for the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, and as a member of the President's Intelligence Advisory Board under President Biden. As a mother to four, Kim is a strong advocate for women in science, and champions diversity and inclusion in all that she does. She is also devoted to the clear and frequent communication of climate change to the public through speaking engagements and social media. Lyl Tomlinson is a Brooklyn native and a post-doctoral researcher and program coordinator at Stony Brook University. He is also a science communication fanatic who often asks: “Would my grandma understand this?” Using this question as a guiding principle, he won the 2014 NASA FameLab science communication competition and became the International final runner-up. In addition to making complex information understandable, he has a growing interest in science policy. Lyl meets with government representatives to advocate for science related issues and regularly develops programs to tackle problems ranging from scientific workforce issues to the Opioid Epidemic. Outside of his work and career passions, he seems to harbor an odd obsession with sprinkles and is a (not so secret) comic book and anime nerd. Learn more about your ad choices. Visit megaphone.fm/adchoices

We are pleased to invite you to a LIVE distinguished YouTube panel discussion on Strategies for Securing Early Career Awards

Brendon Allen has some exoskeletons in his closet, and the National Science Foundation (NSF) wants to find out more about them.The assistant professor in the Department of Mechanical Engineering was recently tapped for a five-year $588,408 NSF CAREER Award aimed at increasing access to rehabilitation for individuals with movement disorders through a deep learning control framework for home-based hybrid exoskeletons.

Obtaining an NSF CAREER Award is a milestone for academics in the sciences. Early-career geotechical engineer and researcher Diane Moug shares her experiences writing and applying for – and then (finally) successfully winning, a CAREER Award.

Simge Küçükyavuz is Chair and David A. and Karen Richards Sachs Professor in the Industrial Engineering and Management Sciences Department at Northwestern University. She is an expert in mixed-integer, large-scale, and stochastic optimization, with applications in complex computational problems across numerous domains, including social networks, computing and energy infrastructure, statistical learning, and logistics. Her research has been supported by multiple grants from the National Science Foundation (NSF) and the Office of Naval Research (ONR). She is an INFORMS Fellow, and the recipient of the NSF CAREER Award and the INFORMS Computing Society (ICS) Prize. She is the past chair of ICS and serves on the editorial boards of Mathematics of Operations Research, Mathematical Programming, Operations Research, SIAM Journal on Optimization, and MOS-SIAM Optimization Book Series. She received her Ph.D. in Industrial Engineering and Operations Research from the University of California, Berkeley.

The conversation this week is with Kevin Liu. Kevin is an associate professor in the Department of Electrical and Computer Engineering at Ohio State University and an Amazon visiting academic with Amazon.com. From August 2017 to August 2020, he was an assistant professor in the Department of Computer Science at Iowa State University. He currently serves as a Managing Director of the NSF AI Institute for Future Edge Networks and Distributed Intelligence at OSU. He received his PhD degree from the Department of Electrical and Computer Engineering at Virginia Tech in 2010. And among many awards, he was an NSF Career Award recipient and a winner of the Google Faculty Research Award in 2020.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 EpisodeNSF AI Institute for Future Edge Networks and Distributed IntelligenceGoogle DeepMindMulti-armed banditReinforcement learningFederated learningMMLS 2024Retrieval-Augmented GenerationAlphaGoEnjoy!Your host,Justin Grammens

Dr. Allison Okamura is a Professor of Mechanical Engineering at Stanford University. She also holds a courtesy appointment in Computer science there. Research in Allison's lab examines three different areas of robotics. The first is haptics, which involves human machine interactions through the sense of touch. The second is designing medical robots that can, for example, be used to help people recover from stroke or perform surgery. A final area that Allison studies is creating soft robots that can conform to their environments. Much of Allison's free time is spent with her husband, daughter, and son. When she's not at work, Allison also enjoys relaxing, running, and playing ice hockey. Allison received her B.S. in Mechanical Engineering from the University of California, Berkeley, and she was awarded her M.S. and Ph.D. both in Mechanical Engineering from Stanford University. Before joining the faculty at Stanford University, Allison was Professor and Vice Chair of Mechanical Engineering at Johns Hopkins University. Allison is the recipient of numerous awards and honors, including being elected as a fellow for the Institute of Electrical and Electronics Engineers (IEEE). She has also been awarded the IEEE Technical Committee on Haptics Early Career Award, the IEEE Robotics and Automation Society Early Academic Career Award, and an NSF CAREER Award. In addition, Allison was honored as a Duca Family University Fellow in Undergraduate Education, a Robert Bosch Faculty Scholar, a Gabilan Fellow, and an Alumni Distinguished Scholar by Stanford University, as well as a Decker Faculty Scholar by Johns Hopkins University. In our interview, Allison speaks more about her experiences in life and science.

In this episode, Pedro Domingos - AI - 2040 - Lauren Hawker Zafer is joined by Pedro Domingos. This unique conversation explores AI's impact on politics, particularly in voter targeting and campaign strategies, and the concept of AI as a tool for enhancing collective intelligence. Domingos, with over 200 technical publications and numerous accolades, shares insights on the future of AI, its challenges, and opportunities. Who is Pedro Domingos? Pedro Domingos is a renowned AI researcher, tech industry insider, and Professor Emeritus of Computer Science and Engineering at the University of Washington. He is the author of the best-selling book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Basic Books, 2015), which has been translated into over twelve languages and sold over 300,000 copies. He won the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in data science and AI. Domingos is Fellow of the AAAS and AAAI and received an NSF CAREER Award, a Sloan Fellowship, a Fulbright Scholarship, an IBM Faculty Award, several best paper awards, and other distinctions. Pedro received an undergraduate degree (1988) and M.S. in Electrical Engineering and Computer Science (1992) from IST in Lisbon and an M.S. (1994) and Ph.D. (1997) in Information and Computer Science from the University of California at Irvine. Pedro is the author/co-author of over 200 technical publications in machine learning, data science, and other areas. He's a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR. He was the program co-chair of KDD-2003 and SRL-2009, and I've served on the program committees of AAAI, ICML, IJCAI, KDD, NIPS, SIGMOD, UAI, WWW, and others. His work has been featured in the Wall Street Journal, Spectator, Scientific American, Wired, and elsewhere. Lastly, Domingos helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks. He lives in Seattle. #ai #techpodcast #redefiningai #squirro

Season Three - Spotlight Thirteen Our thirteenth spotlight of this season is a snippet of our upcoming episode: Pedro Domingos - AI - 2040 Join host Lauren Hawker Zafer as she engages with Pedro Domingos. This unique conversation explores AI's impact on politics, particularly in voter targeting and campaign strategies, and the concept of AI as a tool for enhancing collective intelligence. Domingos, with over 200 technical publications and numerous accolades, shares insights on the future of AI, its challenges, and opportunities. Who is Pedro Domingos? Pedro Domingos is a renowned AI researcher, tech industry insider, and Professor Emeritus of Computer Science and Engineering at the University of Washington. He is the author of the best-selling book The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (Basic Books, 2015), which has been translated into over twelve languages and sold over 300,000 copies. He won the SIGKDD Innovation Award and theIJCAI John McCarthy Award, two of the highesthonors in data science and AI. Domingos is Fellow of the AAAS and AAAI and received an NSF CAREER Award, a Sloan Fellowship, a Fulbright Scholarship, an IBM Faculty Award, several best paper awards, and other distinctions. Pedro received an undergraduate degree (1988) and M.S. in Electrical Engineering and Computer Science (1992) from IST in Lisbon and an M.S. (1994) and Ph.D. (1997) in Information and Computer Science from the University of California at Irvine. Pedro is the author/co-author of over 200 technical publications in machine learning, data science, and other areas. He's a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR. He was the program co-chair of KDD-2003 and SRL-2009, and I've served on the program committees of AAAI, ICML, IJCAI, KDD, NIPS, SIGMOD, UAI, WWW, and others. His work has been featured in the Wall Street Journal, Spectator, Scientific American, Wired, and elsewhere. Lastly, Domingos helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks. He lives in Seattle. #ai #techpodcast #redefiningai #squirro

Over the last number of years, the internet has facilitated much greater connectivity and interaction between people – both on a personal and professional level. Intuitively we might expect that this would lead to an upsurge in innovation as people are exposed to new ideas and can easily collaborate with many more people. And, indeed, this would very much with the recombinant theory of innovation. Yet is that really the case? To explore this further I am delighted to be joined by Professor Lingfei Wu of the University of Pittsburgh. Lingfei Wu is Assistant Professor of Information Science at the University of Pittsburgh. His research leverages big data, complexity sciences, and AI to understand how science and technology can advance through collaborative teamwork, known as the Science of Team Science and Innovation.His research has been published in prestigious academic journals like Nature and Proceedings of the National Academy of Science and featured in renowned media outlets. Lingfei Wu also advises organizations like Novo Nordisk Fonden and John Templeton Foundation on the use of data science to evaluate teamwork in science. He has received multiple awards for his research and teaching, including the NSF Career Award, Richard King Mellon Award, and Oxford Martin School Fellowship. Lingfei's personal site is accessible here: http://lingfeiwu.github.io/The paper discussed in the interview is available here: https://arxiv.org/ftp/arxiv/papers/2206/2206.01878.pdf Hosted on Acast. See acast.com/privacy for more information.

Dr. Marcie O'Malley is the Stanley C. Moore Professor of Mechanical Engineering, as well as a Professor of Computer Science and Electrical and Computer Engineering at Rice University. Marcie is also an Adjunct Associate Professor in the Departments of Physical Medicine and Rehabilitation at Baylor College of Medicine and at the University of Texas Medical School at Houston. In addition, she is Director of the Mechatronics and Haptic Interfaces Lab at Rice University, Director of Rehabilitation Engineering at TIRR-Memorial Hermann Hospital, and co-founder of Houston Medical Robotics, Inc. The goal of Marcie's research is to use robotic systems to maximize what people can achieve. She is working on incorporating robotics to rehabilitate and restore function in people after spinal cord injury or stroke. To do this, Marcie creates wearable and interactive robots to assist with therapy. Another area of Marcie's research focuses on the use of robots for training via surgical simulations. Outside of her scientific interests, Marcie loves to travel and explore new cities. She is also a mom of eleven year old twin boys, so she spends a lot of time working on school projects, attending sporting events, going to art classes, exploring parks, and visiting museums with them. She received her B.S. in mechanical engineering from Purdue University, and she was awarded her M.S. and Ph.D. in mechanical engineering from Vanderbilt University. Marcie has received recognition for her teaching and research through receipt of the George R. Brown Award for Superior Teaching at Rice University, an Office of Naval Research Young Investigator Award, and an NSF CAREER Award. She has also been named a Fellow of the American Society of Mechanical Engineers. Marcie joined us for an interview to talk about some of her experiences in life and science.

Aaditya Ramdas is an assistant professor at Carnegie Mellon University, in the Departments of Statistics and Machine Learning. His research interests include game-theoretic statistics and sequential anytime-valid inference, multiple testing and post-selection inference, and uncertainty quantification for machine learning (conformal prediction, calibration). His applied areas of interest include neuroscience, genetics and auditing (real-estate, finance, elections). Aaditya received the IMS Peter Gavin Hall Early Career Prize, the COPSS Emerging Leader Award, the Bernoulli New Researcher Award, the NSF CAREER Award, the Sloan fellowship in Mathematics, and faculty research awards from Adobe and Google. He also spends 20% of his time at Amazon working on causality and sequential experimentation. Aaditya's website: https://www.stat.cmu.edu/~aramdas/ Game theoretic statistics resources Aaditya's course, Game-theoretic probability, statistics, and learning: https://www.stat.cmu.edu/~aramdas/gtpsl/index.html Papers of interest: Time-uniform central limit theory and asymptotic confidence sequences: https://arxiv.org/abs/2103.06476 Game-theoretic statistics and safe anytime-valid inference: https://arxiv.org/abs/2103.06476 Discussion papers: Safe Testing: https://arxiv.org/abs/1906.07801 Testing by Betting: https://academic.oup.com/jrsssa/article/184/2/407/7056412   Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats

Chibueze Amanchukwu is the Neubauer Family Assistant Professor of Molecular Engineering. His group works on energy-related challenges, with a specific focus on understanding how electrolytes can control electrochemical processes in batteries and catalysis. His work has been recognized with the NSF CAREER Award, the DOE Early Career Award, and the CIFAR Azrieli Global Scholar Award, amongst others. Tune in to hear Professor Amanchukwu's insights into becoming a professor and his dreams of impacting the world with his research.

In Episode 83, Patrick and Ciprian speak with returning guest Dr. Prineha Narang of UCLA. The team discusses distributed quantum sensor networks, lasers, magnons, and new technology application opportunities through organic conversations.Dr. Prineha Narang is a Professor in Physical Sciences and Electrical and Computer Engineering at UCLA with an interdisciplinary group spanning areas of physics, chemistry, and engineering. Prior to moving to UCLA, she was an Assistant Professor of Computational Materials Science at Harvard University. Before starting on the Harvard faculty in 2017, Dr. Narang was an Environmental Fellow at HUCE, and worked as a research scholar in condensed matter theory in the Department of Physics at MIT. She received an M.S. and Ph.D. in Applied Physics from Caltech. Her group works on theoretical and computational quantum materials, non-equilibrium dynamics, and quantum information science. Narang's work has been recognized by many awards and special designations, Narang's work has been recognized by many awards and special designations, including the 2023 Guggenheim Fellowship in Physics, Maria Goeppert Mayer Award from the American Physical Society, 2023 ONR Young Investigator Award, 2022 Outstanding Early Career Investigator Award from the Materials Research Society, Mildred Dresselhaus Prize, Bessel Research Award from the Alexander von Humboldt Foundation, a Max Planck Sabbatical Award from the Max Planck Society, and the IUPAP Young Scientist Prize in Computational Physics all in 2021, an NSF CAREER Award in 2020, being named a Moore Inventor Fellow by the Gordon and Betty Moore Foundation, CIFAR Azrieli Global Scholar by the Canadian Institute for Advanced Research, a Top Innovator by MIT Tech Review (MIT TR35, )and a leading young scientist by the World Economic Forum in 2018. In 2017, she was named by Forbes Magazine on their “30under30” list for her work in atom-by-atom quantum engineering, that is, designing materials at the smallest scale, using single atoms, to enable the leap to quantum technologies. Dr. Narang has held leadership roles in a DOE EFRC ‘Photonics at Thermodynamic Limits', DOE NQI Quantum Science Center, and the NSF ERC ‘Center for Quantum Networks', among others. Her continued service to the science community includes chairing the Gordon Conference on Ultrafast and Cooperative Phenomena, Materials Research Society (MRS) Spring Meeting (2022) and the MRS-Kavli Foundation Future of Materials Workshop: Computational Materials Science (2021), organizing APS, Optica (OSA), and SPIE symposia, and a leadership role in APS' Division of Materials Physics. Narang is an Associate Editor at ACS Nano of the American Chemical Society, an Associate Editor at Applied Physics Letters of the American Institute of Physics, and the Editorial Advisory Boards of Nano Letters and Advanced Photonics. Dr. Narang is also the founder and Chief Technology Officer of Aliro, a VC-backed US quantum network company. At Aliro, she spearheads the effort in quantum information, towards commercializing scalable quantum networks.

Dr. Siwei Lyu, SUNY Empire Innovation Professor at the University at Buffalo Dr. Siwei Lyu received his B.S. degree (Information Science) in 1997 and his M.S. degree (Computer Science) in 2000, both from Peking University, China. He received his Ph.D. degree in Computer Science from Dartmouth College in 2005. From 1998 to 2000, he worked at the Founder Research and Development Center (Beijing, China) as a Software Engineer. From 2000 to 2001, he worked at Microsoft Research Asia (then Microsoft Research China) as an Assistant Researcher. From 2005 to 2008, he was a Post-Doctoral Research Associate at the Howard Hughes Medical Institute and the Center for Neural Science of New York University. Starting in 2008, he is Assistant Professor at the Computer Science Department of University at Albany, State University of New York. Dr. Lyu is the recipient of the Alumni Thesis Award of Dartmouth College in 2005, IEEE Signal Processing Society Best Paper Award in 2010, and the NSF CAREER Award in 2010. He has authored one book, and held two U.S. and one E.U. patents. He has published more than 50 conference and journal papers in the research fields of natural image statistics, digital image forensics, machine learning and computer vision. For links and resources discussed in this episode, please visit our show notes at https://www.forcepoint.com/govpodcast/e260

Dr. C. Denise Okafor is an Assistant Professor of Biochemistry and Molecular Biology, and of Chemistry at Pennsylvania State University. Many of the medications we take work by binding to a particular target and either turning off whatever is causing a problem or turning something on that is not working correctly. Denise's research examines how small molecules like drugs find and interact with the targets they are supposed to interact with. She is particularly interested in proteins that can be turned on or off by the small molecules/drugs that they bind with. While science is a large part of Denise's life, she also enjoys reading and writing fiction. Lately, she has been spending much of her free time with her kids, learning dances from Youtube videos and hanging out together. She received her B.S. in biomedical chemistry from Oral Roberts University and was awarded her M.S. in chemistry and a Ph.D. in biochemistry from Georgia Institute of Technology. Afterwards, Denise was selected to complete an Institutional Research and Academic Career Development Award from the NIH to conduct postdoctoral research at Emory University and teach at Morehouse and Spelman colleges in Atlanta. Denise has received a variety of awards and honors for her work, including a Burroughs Wellcome Fund Career Award at the Scientific Interface, an NSF CAREER Award for early investigators, and the NIH Director's New Innovator Award. In addition, she has been named a Keystone Symposia Fellow and a Kavli Foundation Fellow. In our interview, Denise talks more about her life and science.

Hosts: Sabrina Hsueh PhD Adela Grando, PhD Guest: Regina Barzilay, School of Engineering Distinguished Professor of AI & Health in the Department of Computer Science at the Massachusetts Institute of Technology and the AI Faculty Lead at MIG Jameel Clinic In this episode, part of a special collaboration between ACM ByteCast and the American Medical Informatics Association (AMIA)'s For Your Informatics podcast, hosts Sabrina Hsueh and Adela Grando welcome Regina Barzilay, a School of Engineering Distinguished Professor of AI & Health in the Department of Computer Science at the Massachusetts Institute of Technology and the AI Faculty Lead at MIT Jameel Clinic. She develops machine learning methods for drug discovery and clinical AI. In the past, she worked on natural language processing. Her research has been recognized with the MacArthur Fellowship, an NSF Career Award, and the AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity. Regina is a member of the National Academy of Engineering and American Academy of Arts and Sciences. Regina describes her career journey, and how a personal experience with the healthcare system led her to work on an AI-based system for the early detection—and prediction of—breast cancer. She explains why entering the interdisciplinary field of clinical AI is so challenging and offers valuable advice on how to overcome some of these challenges. Regina also opines on new models for using AI, including the promise of ChatGPT in healthcare. Finally, she talks about inequity in medicine, and offers actionable insights on how to mitigate these shortfalls while moving the field of clinical AI forward.

In this episode, part of a special collaboration between ACM ByteCast and the American Medical Informatics Association (AMIA)'s For Your Informatics podcast, hosts Sabrina Hsueh and Adela Grando welcome Regina Barzilay, a School of Engineering Distinguished Professor of AI & Health in the Department of Computer Science at the Massachusetts Institute of Technology and the AI Faculty Lead at MIT Jameel Clinic. She develops machine learning methods for drug discovery and clinical AI. In the past, she worked on natural language processing. Her research has been recognized with the MacArthur Fellowship, an NSF Career Award, and the AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity. Regina is a member of the National Academy of Engineering and American Academy of Arts and Sciences. Regina describes her career journey, and how a personal experience with the healthcare system led her to work on an AI-based system for the early detection—and prediction of—breast cancer. She explains why entering the interdisciplinary field of clinical AI is so challenging and offers valuable advice on how to overcome some of these challenges. Regina also opines on new models for using AI, including the promise of ChatGPT in healthcare. Finally, she talks about inequity in medicine, and offers actionable insights on how to mitigate these shortfalls while moving the field of clinical AI forward.

Dr. Sarah Bergbreiter is an Associate Professor in the Department of Mechanical Engineering with a joint appointment in the Institute for Systems Research at the University of Maryland. Sarah's research involves building and conducting experiments with tiny locomoting robots that are about the size of ants. They also apply the same technologies used in their tiny robots to build better sensors and actuators for bigger robots to help improve performance of these robots. Spending time with her family is a big part of Sarah's life outside of work. Her kids enjoy swimming, playing with legos, and building things. Sarah also spends her free time swimming and playing water polo. She received her B.S.E. degree in electrical engineering from Princeton University and was awarded her M.S. and Ph.D degrees in electrical engineering from the University of California at Berkeley where she focused on microrobotics. Sarah has been the recipient of multiple awards for her outstanding work including the DARPA Young Faculty Award, an NSF CAREER Award, the Presidential Early Career Award for Scientists and Engineers, and Sarah gave a TED Talk in 2015. Sarah joins us for an interview to discuss her life and work.

Dr. Young-Hui Chang is a Professor of Biological Sciences at Georgia Institute of Technology where he directs research in the Comparative Neuromechanics Laboratory. Research in Young-Hui's lab aims to examine how the control of movement by the nervous system is influenced by mechanics and physics during locomotion. He is interested in broad mechanisms for behaviors like walking, running, and hopping that apply within and across species. Young-Hui likes to spend his free time with his family. He, his wife, and his two boys enjoy exploring the outdoors, hiking, and camping together. Though Young-Hui was not always a particularly outdoorsy person, enrolling his sons in the Scouts program has provided an avenue for him and his family to learn more and get outside. Young-Hui received his B.S. in Mechanical Engineering and his M.S. in Animal Physiology from Cornell University. Next, he conducted his doctoral studies at the University of California, Berkeley, earning his PhD in Integrative Biology in 2000. Prior to joining the faculty at Georgia Tech, Young-Hui was a postdoctoral researcher at Emory University. While at Emory, he was awarded the Association of Korean Neuroscientists President Outstanding Research Hanwha Award. Young-Hui has also been awarded an NSF CAREER Award. In this interview, he discusses his experiences in life and science.

In this episode of ACM ByteCast, Rashmi Mohan hosts Anima Anandkumar, a Bren Professor of Computing at California Institute of Technology (the youngest named chair professor at Caltech) and the Senior Director of AI Research at NVIDIA, where she leads a group developing the next generation of AI algorithms. Her work has spanned healthcare, robotics, and climate change modeling. She is the recipient of a Guggenheim Fellowship and an NSF Career Award, and was most recently named an ACM Fellow, among many other prestigious honors and recognitions. Her work has been extensively covered on PBS, in Wired magazine, MIT Tech Review, YourStory, and Forbes, with a focus on using AI for good. Anima talks about her journey, growing up in a house where computer science was a way of life and family members who served as strong role models. She shares her path in education and research at the highly selective IIT-Madras, the importance of a strong background in math in her computing work, and some of the breakthrough moments in her career, including work on using tensor algorithms to process large datasets. Anima spends some time discussing topic modeling and reinforcement learning, what drives her interests, the possibilities of interdisciplinary collaboration, and the promise and challenges brought about by the age of generative AI.

How is computer vision being used to spot autism symptoms much earlier in children? What is augmented cognition? And how can you use AI to make data models work even with small data sets? We will learn those answers and more in this episode with Dr. Sarah Ostadabbas. Dr. Ostadabbas is an associate professor in Electrical and Computer Engineering at Northeastern University, where she is also the director of the Augmented Cognition Laboratory (ACLab), which works at the intersection of computer vision, pattern recognition, and machine learning. Before joining Northeastern, she was a post-doctoral researcher at Georgia Tech and earned her Ph.D. at the University of Texas at Dallas. A renowned expert in the field, her research focuses on the goal of enhancing human information-processing capabilities through the design of adaptive interfaces based on rigorous models using machine learning and computer vision algorithms. With over 100 peer-reviewed publications, Professor Ostadabbas has received recognition and awards from prestigious government agencies such as the National Science Foundation (NSF), the Department of Defense (DoD) as well as several private industries. In 2022, she received an NSF CAREER award to use artificial intelligence for the early detection of autism, which she is working on with Oracle for Research. http://www.oracle.com/research   ---------------------------------------------------------   Episode Transcript:   00;00;00;00 - 00;00;26;15 How are computer vision and contactless techniques spotting signs of autism much earlier in children? What is augmented cognition and how can you use AI to make data models work, even with small datasets? We'll find all that out and more in this episode of Research in Action. Hello and welcome back to Research in Action, brought to you by Oracle for Research.   00;00;26;15 - 00;00;50;10 I'm Mike Stiles, and today we have with us Dr. Sarah Ostadabbas, an Associate Professor in the Electrical & Computer Engineering Department Northeastern University, where she's also director of the Augmented Cognition Laboratory (ACLab), which works at the intersection of computer vision, pattern recognition and machine learning. Before joining Northeastern, she was a postdoctoral researcher at Georgia Tech and got her Ph.D. at the University of Texas at Dallas.   00;00;50;13 - 00;01;24;04 Her research looks at how we can enhance human information processing capabilities by designing adaptive interfaces based on rigorous models using machine learning and computer vision algorithms. With over 100 peer reviewed publications. Professor Ostadabbas has received recognition and awards from government agencies like the National Science Foundation, the Department of Defense and several private industries. In 2022, she received an NSF career award to use AI for early detection of autism, and she's working on that with Oracle for Research.   00;01;24;04 - 00;01;43;26 Dr. Ostadabbas, thank you so much for being with us today. Thanks for having me. I'm excited to be here and feel free to call me Sarah. Well, listeners, get ready because we're going to get all into computer vision, machine learning, augmented cognition and wherever else I can get nosy about. But first, let's hear about you, Sarah, and your background.   00;01;43;26 - 00;02;12;08 Your passion for technology and physics kind of started back in childhood, right? Yes, that's correct. Actually, physics was my favorite subject in middle school and high school. I was so passionate about it that I even went through the whole volume of Fundamentals of Physics by David Halliday and Robert Resnick in I believe it was in 10th year of my high school, and I was seriously considering to pursue the continuous PhD in physics even before graduating from high school.   00;02;12;10 - 00;02;39;09 And alongside my love for physics, I was always also fascinated by technology, especially computers and programing. I started coding in a language called Basic, which some of your audience may not even heard about that. Why I was in middle school and loved it. Data Analytics capabilities of computer and how computers are giving advanced processing power to human no matter where they are.   00;02;39;11 - 00;03;12;14 I was still living in Iran at the time and experiencing technological advances at that time, such as Internet and cell phone, and they were all very much interesting. And fast forward, all of this led me to pursue a natural combination of my interests, which was an electrical and computer engineering degree with a double majoring in biomedical engineering. And now when I look back, it's actually heartwarming to see one that one seemed to be diverse.   00;03;12;14 - 00;03;41;17 Interesting collection of interests now have shaped my academic journey so far. Was it unusual for someone, you know, at your age, at that early age of middle school, to be coding and thinking about technology and physics and looking that far into the future? I was actually going to date if school, middle school and high school at that time was designed for for math and science.   00;03;41;17 - 00;04;06;00 So no, I had a lot of of my classmates going and exploring different science topics. So it wasn't unusual. I mean, it was unusual when I was taking these heavy books to my gathering at parties, at my family, but not at the school. So I'm glad. And it was 200 of us, 200 girls at and now all of us are all around the world.   00;04;06;06 - 00;04;28;02 Most of us have PhDs. And yeah, it wasn't unusual, but it, it was something that I cherish. Yeah, it's great that you had a school that focused on things like that. So let's kick things off with your NSF CAREER Award focused on developing machine learning algorithms towards the early detection of autism. Tell me if I get this wrong.   00;04;28;02 - 00;04;53;08 But this is about using computer vision to predict autism a lot earlier in children. And what does what does that research involve and what does Oracle for Research have to do with it? You're certainly right. As I mentioned, my academic background revolves around electrical and computer engineering, focusing on data processing. And these data sources can be signals, images and videos.   00;04;53;11 - 00;05;21;06 How might a specific focus a work on computer vision began when I joined Northeastern University as an assistant professor in 2016. As you may know and have heard of over the past decade, deep learning models have been driving advancements in many AI topics, including computer vision. But these algorithms often require a large amount of training data. They are very data hungry.   00;05;21;08 - 00;05;48;24 So my National Science Foundation CAREER Award aims to leverage this advancement in computer vision for a specific health related domain that suffera from limited data. And I'm in particularly focusing on detecting autism in infant even before the first birthday. And this is true processing videos that is collected from them when they are doing daily activities, which is not a lot of things that they do.   00;05;49;01 - 00;06;16;13 They are sleeping, playing or eating. And as I mentioned, my algorithm, they are designed to be data deficient because I'm working on the area that the there are not a lot of data due to this privacy and security reason, but adapting these complex networks, these complex neural networks which are which are building blocks of deep learning necessitates powerful computing resources.   00;06;16;20 - 00;06;44;25 And that's where our collaboration with Oracle become highly valuable, allows me to make this model adapted to this specific application. So you have videos, video cameras, monitoring the kids and kind of like an in the wild get capturing of data. And then the computing power is needed to crunch all that video and that pulls out certain patterns that reveal autism earlier.   00;06;44;25 - 00;07;07;14 Is that how it works? Yeah. I mean, you can say that you put that on the simpler words. Yes, exactly. I'm a simple man. No, no, no. I'm just it's a good I mean, it's a good, good way to describe that. Yes, that's correct. So what we do, we actually leverage these computer vision techniques and contactless video processing algorithm to predict autism, as I mentioned, from daily activities.   00;07;07;19 - 00;07;35;17 And these are daily activities captured by commercial video recording messages. Imagine like a baby monitor or even parent's cell phone cameras. Every parent's love to record videos from the day of their child. So they focus on this specific developmental sign. How will that that relates to motor function, which means that relates to infants posture, muscle tone, body symmetry, and they balance and range of movement.   00;07;35;18 - 00;08;04;05 So these are specific markers that actually has been shown to be early visible warning signs of more developmental disorders such as autism. And they appear actually interestingly, long before the core feature of autism that you may have heard of and these are actually very known, such as social or communication difficulties as well as repetitive behavior. So we are focusing on these early signs.   00;08;04;08 - 00;08;29;11 However, currently the standard approach to monitor this motor function is through visits to child doctor, pediatrician and how is it, unfortunately, over half of these visits are missed. You could imagine often due to the lack of transportation, for parents, it's hard to take time off from work and also lack of child care for other other kids set at home.   00;08;29;13 - 00;09;12;29 So half of these visits are missed and a lot of this early sign has been overlooked. So to address this in equitable access to actually to clinical assessment and a lot of practical constraints, we are trying to to make a home based a I guided in monitoring tools that can track early motor function development very unobtrusively, like just a video that is watching like a baby monitor is rolling and then be the process this video on the back end and track this specific developmental sign and hopefully be we help for the early detection of autism.   00;09;13;02 - 00;09;40;15 I want to also point the fact that it's actually important, very important and crucial to have timely detection in the autism case, because early intervention, it's actually shown that is most effective before the age of four. Yet the average age of autism diagnosis is still around four and a half. So we are hoping to make a clear detection tools better intervention outcome.   00;09;40;18 - 00;10;00;06 It's really interesting to me that body symmetry is a hallmark of development. I guess my question is why would that be and how is Body Cemetery being addressed in your research? That's a very good question. So we are as I mentioned, a motor development is very important. If early signs offer any visible sign of something that may not working out right.   00;10;00;09 - 00;10;32;14 So one interesting aspects of motor function that has been identified as an indicator of neurodevelopmental health is body symmetry. You can imagine that symmetrical movements and posture are crucial for supporting independent movements such as sitting, crawling and walking, especially infant. Then an infant is typically developing movement posture. Actually, you start asymmetric and then gradually they become more symmetrical as our sensorimotor coordination develops.   00;10;32;16 - 00;11;05;06 And during the first year of life, infants could go through the various milestones, such as days rolling over, sitting up, standing so little by little watching, and all of these movement progressed from less symmetric to more symmetric movement and then also study, they have been looking at the infant movement. They have a map showing that the position is symmetry in their movement can be indication of disorders like autism.   00;11;05;09 - 00;11;28;09 However, if we want to have motor functional function assessment in infant, especially body symmetry in larger scale for a long period of time, our for health care provider is going to be very expensive. I mean, somehow impossible and very challenging because imagine if you have 10 hours of videos, how long does it take for you to watch that?   00;11;28;09 - 00;11;54;10 10 hours. I mean, it's going to take 10 hours. But what we want to do, we want to have these computer vision tools apply on these videos to automatically evaluate them all to a function and is start having something in home that people can use and start escorting to one of the mutual developmental indicators, escorting them the symmetry.   00;11;54;12 - 00;12;23;06 So the idea is that we are actually using infant pose estimation algorithms that we have already developed in the lab to assess postural asymmetry based on differences in joint angle between opposing the arms, between the left side and right side. So the effect the the difference is more than 45 degrees, which has been suggested by Esposito in this study in 2009, in the we can call it asymmetric.   00;12;23;12 - 00;12;50;15 We have also come up with our own measure, which is a data learned based assessment on using Bayesian assets to collect aggregation that we could actually come up with two different angles. But how that these are all allows us to do to process the beat you automatically. And then the video is called the whole movement of the infants based based on all of this processing symmetric or asymmetry.   00;12;50;15 - 00;13;12;01 And then physicians can look at that and see that it is something alarming or not. And then as the process of the science and research goes on, well, I've talked to enough researchers to know that recruiting is usually a challenge for any experiment. But with this, the target population is children like babies. How did you manage to get your patient population?   00;13;12;01 - 00;13;39;15 Were there any privacy, access or ethical concerns? It's a very good question and also absolutely an important matter. When recruiting for our experiment, we noticed that the challenge of targeting infants subject under the age of one, parents are already overworked, sleep deprived, and imagine asking them to to be part of yet another task. So it's very hard, however, to be able to overcome this this problem.   00;13;39;18 - 00;14;16;20 We leverage the fact that many parents already are using baby monitoring systems, so they just want to wash them. I mean, a lot of these baby monitors, even the one that they call smart, they don't do anything. It's just a trigger. If the mat the baby's crying or they are moving. So we are aiming to develop this normal system that not only allow the parents to observe the child, but also offers this long term monitoring capability to track the child's developmental process and provides alert if some abnormalities are detected.   00;14;16;26 - 00;14;38;14 So this may be a good incentive for for parents to take part in our study. And as one of the points that you mention about the privacy and ethical concern, we have taken several measures to make sure to address these concerns. We are collaborating with health care professional that they are more familiar with to dealing with the human subject.   00;14;38;17 - 00;15;15;14 And also we are working closely with a Northeastern Institutional Review board known as IAB to make sure our data collection protocol has strict security and privacy standard. We make sure that the parents that they are participating in our study are fully informed about the purpose of the research. And also we get they consent to to use some some part of these data for public use and public release for scientific and technological advancement, because a lot of them these days, how to win is shared in other a study can be built on top of that.   00;15;15;14 - 00;15;37;19 So but we make sure that parents are that the parents that they are part of this study, they are they are aware, fully aware of that. And I want to emphasize that our priority is to preserve the privacy and confidentiality of them, the participant to out the whole process, although they are looking and working on very important and impactful research.   00;15;37;19 - 00;16;05;12 QUESTION But this is also very important at the top of our list. Yes, security and privacy data for data that is important. Is that why a tech concern like Oracle Cloud that obsesses over things like privacy and security kind of speeds up the research? That's very good. Good point that you brought up. That's true. As I mentioned, security and privacy of the data, especially in our field based on the sensitive nature of data that we are collecting, is important.   00;16;05;16 - 00;16;50;21 We are working with them with personal health related information. So we required some sort of robust measure to to protect confidentiality and prevent unauthorized access. And working alongside part industry partners like Oracle ensures that we are actually having a huge safeguard on our sensitive information. The team that I am working with, Oracle has this huge expertise in data management and security practices, and this allows us to then when we are storing, processing and analyzing data in a in a protected environment, we can focus on our research objective while having a partner that gives us confidence in the security and privacy of the data that they are handling.   00;16;50;21 - 00;17;22;04 So it's a very useful and necessary collaboration. So your lab Augmented Cognition Laboratory or the A.C. Lab works with Computer Vision and machine learning. How did that lab come to be and what exactly is augmented cognition? This is actually brings back many fond memories for me, I think. Tell you the story behind the name, Why I was interested in physics, computers, math, and even literature.   00;17;22;04 - 00;17;53;11 I mean, this is specific. Interest by itself can be another podcast session, but not now. I always had a vision of becoming a university professor and leading my own research lab. I remember clearly that I wasn't seen earlier for my Ph.D. when I started to look at look for names for my future lab to reflect the into intersection of engineering inspired artificial intelligence because I was farming, doing school and data analytics.   00;17;53;18 - 00;18;28;25 But also I wanted to emphasize the positive impact of A.I. in human life rather than replacing them. So I came up with the name Augmented Cognition. Augmented Cognition. I actually represent the core idea that I have about enhancing human information processing capability through the design of adaptive interfaces guided by A.I. algorithm, especially machine learning and computer vision. This is specific definition is actually opening of my my web page when I started at my my position at Northeastern University.   00;18;28;28 - 00;18;59;00 This also highlights my focus on utilizing these advanced tools to augment human ability, especially in the data processing domain. Imagine what I'm doing here as part of my NSF CAREER and what I want to to give physician parents the power of processing hours and hours of data and then let them to extract the information that is needed to to make sure to make the informed decisions.   00;18;59;02 - 00;19;23;13   I often have this phrase that at the ACLab we use artificial intelligence or AI to do human intelligence amplification or IEEE. So I do more Iot and A.I.. Your work relies a lot on machine learning and computer vision as tools to generate truly augmented intelligence solutions. How do you leverage the recent advancement of AI in your work?   00;19;23;13 - 00;20;02;06 Because you've probably been watching it for years, but for most of the public, this A.I. thing came on like a tidal wave. So how does that get applied to computer vision? That's true. I mean, I it's the main wave, and I believe in my my opinion that the main a wave and also success is started from with the introduction of deep learning in 2012 2015 and the actually expand the recent advancement in AI to tackle challenges in understanding and predicting human behaviors from vision sources.   00;20;02;06 - 00;20;43;22 As I said, images or videos, I am focused my my work focus on representation learning in visual perception problems such as object detection, tracking and action recognition and using all of these these tools, we want to estimate the physical, physiological or even emotional states of the individual under study. So to be able to do a robust estimation, the algorithms that we are developing at the Sea Lab utilizes this concept called Pose, which is a low dimensional embedding that captures the essential information in the state of the human that we are monitoring.   00;20;43;28 - 00;21;10;14 For example, body pose, facial pose. You could imagine that you could from that to you can get body symmetry, you can get the emotional feeling of the the human. So help me that I want to emphasize the fact that many of these human data focus application that I work on belong to this small data domain. But the data collection and labeling are limited or restricted, such as healthcare application or even military application.   00;21;10;21 - 00;21;42;26 So to address the data limitation, my algorithm also integrate explicit domain knowledge into the learning process through the use of a generative AI model. We actually built our genitive AI model that this model, they are all data efficient machine learning while incorporating valuable insight from domain experts. So this allows us to to use less data. But on the other hand, we have all of these backing from from the experts that allows us to to make our model work.   00;21;43;04 - 00;22;18;24 This means collaborating with professionals from various fields such as physicians, psychologists, even physicians and neuroscientists are very much important and ensures the practical relevance of many of the models that we are developing in the lab. I definitely see use cases for improving health care and data analysis and augmentation. But for the clinical space, are you a let's go for it person when it comes to AI or more of a cautious person and there is a responsible way to apply, I think that your question comes from all of these debates happening.   00;22;18;24 - 00;22;43;25 Is AI for good or for bad? I mean, what we do, to be honest as a researcher working at the intersection of AI and health, I have been trying to keep a balanced perspective on this overall impact of AI. I am an optimistic optimist when it comes to the potential benefit of AI for health care, particularly for the data analysis and intelligence augmentation.   00;22;43;25 - 00;23;05;06 As the name of my lab, we then come back. I believe that A.I. has the potential to change the healthcare and improve diagnosis, personalized treatment, enhancing patient care, and expanding access to care, as I mentioned. I mean, you can actually make an air power system at your home and get the monitoring and the diagnosis that that you need.   00;23;05;08 - 00;23;35;10 And it can help clinician to make more accurate and timely decision leading to better outcomes for patient health. There is not that I'm just only say is the best and now we don't need to to think about other aspects. I also approach the use of AI in the clinical space, especially with caution. We have to be concerned and to address this concern related to privacy, security and ethical use.   00;23;35;12 - 00;24;02;29 We have to be transparent and accountable and ensure that a AI system are fair, unbiased and trustworthy. These are useful for on on human subject. So proper validation and rigorous testing are necessary to make sure these models are reliable and robust. Also, it's very essential to involve health care professionals, patient and other a stakeholder in the development process.   00;24;03;05 - 00;24;29;20 It cannot be inside AI sitting the lab and come up with something as okay, this is perfect. Let's so let's put that in every baby monitor around the world. We have to make sure the system is safe. A specific needs in inside the health care domain. So in one sentence, I believe that with responsible development and implementation, AI has the potential to significantly improve improved health care outcome.   00;24;29;22 - 00;24;59;11 And I'm hoping this balance will that of you, especially in the clinical setting, allows us to to work more to make better and stronger and more robust AI model while addressing the concern and challenges that comes with its use in the clinical space. Well, I know based on what you said, and because I cheated and researched you before you came on the show, that you you believe that AI, as long as it's good, should be able to augment our capabilities.   00;24;59;11 - 00;25;24;04 And again, you're saying not replace human capability, but augment capabilities. So as you mentioned, the average age of detection for autism is about four and a half years olds. How much and you mentioned about one year old, that's how much sooner than that you think the research could detect autism. And if you do detect it that much earlier, then what Can we actually improve developmental growth?   00;25;24;06 - 00;25;54;17 So before I proceed, I want to make it clear that I don't have any formal academic training in the health care domain. Power through my extensive collaboration and engagement, I have come to understanding the significance of the early detection in neurodevelopmental conditions such as autism, and also how timely intervention can improve the developmental outcome. So as you mention and that's right, the current average age of autism detection is around four and a half years.   00;25;54;20 - 00;26;27;02 But through our research, we want to aim to significantly reduces this age and we are hoping to make it on the age of one because we are able to detect this specific neurodevelopmental model signs unobtrusively, automatically and long term using our computer vision algorithm. And let's remember that the fact that the brain exhibits its highest level of neural plasticity during the first year of life.   00;26;27;04 - 00;27;14;09 So intervening during this sensitive window can have profound impact on long term. So the sooner that we can catch some of these not neurodevelopmental disorder, then the rehabilitation can start. And also intervention can be much more accurate. Also detecting a system that can track and quantify infant development aside from autism can can be used to detect and test other hypotheses related to a motor function hypothesis that based on my collaboration with other health care professionals related to this, a liberal policy congenital tool to coalesce list out that all of this stuff that has some motor representations, but they are not catch early.   00;27;14;09 - 00;27;43;09 You know, because infants are at home. Parents are especially new. Babies have a lot of work so they they missed a sign and then the number of visit is very limited if not missed. So by advancing the age of detection and enabling early intervention, I am not only hoping to have the individual outcome, but also the whole idea is studying other and testing other hypotheses in their developmental science.   00;27;43;09 - 00;28;12;17 So hopefully that would be a tool that empower researcher, physician parents in the field to study these motor related developmental condition much earlier and less expensive and much more on up to the CV. Well, research does need data for exploration and reproducibility, but a lack of data sharing in the research community is kind of a hot topic. There are several people that just doesn't want our collective knowledge to collect.   00;28;12;20 - 00;28;48;13 So why is data sharing vital to advancing science and getting to new discoveries and treatments? For sure, I'm not among those group that they don't share. I think I believe the data sharing plays a very important role in advancing scientific research. So essential for reproducibility, transparency and collaboration. So by sharing data research, it can not only validate what you have done, reproduce that, but also they can build upon your finding and start building new and new discoveries.   00;28;48;15 - 00;29;14;03 So rather than everybody start from scratch. So sitting on your data and not sharing that, it's I don't see that is a scientific manner. This is very fundamental. We do, we do actually share the data on both the data and code in our lab, in the computer science and engineering field is is known that people share data. They could, but in the medical domain, this data is very protected.   00;29;14;06 - 00;29;44;06 And it's I understand all of their privacy consent. But in our data collection procedure, we make sure that we inform at the participant about the value of data sharing. So we get they consent to share these data is pieces of the video that they are collecting. And then I am hoping that collectively we can add best knowledge, at least address complex challenges related to data specific types of a question that we are addressing.   00;29;44;06 - 00;30;16;28 And ultimately we want to improve human health and well-being well-being and enhance the quality of life for everybody. Do you think some of that reluctance has to do with concerns about intellectual property and researchers thinking about, you know, the marketability of what they're doing? Absolutely. Absolutely. That's the case. But I have a counter argument for that. So this is not 2000 years ago that we we come up with an idea and write it down and then buried so nobody can find it after after us.   00;30;17;00 - 00;30;40;22 So I think by sharing with the acknowledgment of that there the research and who came up with that is important. But if we keep this strain of sharing thoughts, sharing ideas, sharing data, which data nowadays holds a lot of intelligence insight inside that, then we can actually build and everybody get into the training of the is Discovery new discovery.   00;30;40;29 - 00;31;13;07 So if we want to keep that it's possible and then in industry because now the line between industry and academy is not as the strict as before because there are a lot of collaboration happen which we're very much I admire. But yeah, we have to to make sure to acknowledge both sides, industry and academics, to acknowledge their contribution, but then share the data and see and be happy on the growth, be happy about advancing the knowledge and the complex problem cannot be solved if we just keep it to ourselves.   00;31;13;09 - 00;31;41;09 Well, our audience of researchers is pretty bright. So is there anything else you'd kind of like them to know or for them to think about that we haven't touched on yet? Just something that you wish people paid a little bit more attention to. Oh, thanks for asking. Yes, I think that this in this podcast you talk about my research related to the use of AI in computer vision for for autism.   00;31;41;11 - 00;32;07;08 A study, as I said, that I don't have any any health care background. However, in my my lab doesn't only work on the autism patients, we are actually interested in developing computer vision and machine learning solution for a wide range of application dealing with the small data problem. The data, it's the the bread and butter of us because the intelligence, especially in the era of deep learning, it's all hidden in the data.   00;32;07;10 - 00;32;34;07 So I work on the rehabilitation, animal monitoring, even autonomous driving scenarios that is hard to collect. Data is expensive, is dangerous to collect data or is impossible. Sometimes, for example, it's very hard to to collect data from animal in this specific pose or conditions. So that's one thing that's enabling these advancements, especially advancement in computation and machine learning in this small little domain is important.   00;32;34;09 - 00;33;05;00 So rather than to do not be afraid or shy, if you think that, okay, this specific application needs a lot of detail, we don't have that. So let's not use let's abandon all of these advancement that we have because we don't have a lot of data. No, it's possible. And in our lab we are working on that to enable these advancement in the domain that rather than having millions and millions of sample, you have only 100 samples, you have only 20 samples of that in Central and all that.   00;33;05;02 - 00;33;28;04 So in my lab we are looking at the problem time to size. First we want to see that if we can make our machine work with less amount of data as I mentioned earlier, how we can do that, we should actually make research a space for the parameters of the model, make it more constrained by bringing some outside domain knowledge inside the model.   00;33;28;07 - 00;33;47;08 So rather than be say that, look, I don't want to hear anybody else's idea. I just want to look at the data and see what's happening. We only take them. They are data driven models. We are putting in some understanding of about the physics, about this specific phenomenal behind that, about the specific types of movement that we are looking for into the model.   00;33;47;13 - 00;34;15;21 So to make the model work with a less amount of data. On the other hand, we we were thinking about this in digital expanded this data is called synthetic data generation. So we are looking at a lot of simulators, even game engines, to see that if we can use them and make an avatar of infant, for example, fall from the bit better than looking at videos or waiting for infant fall of the bit, we actually see that picture can be simulated.   00;34;15;21 - 00;34;35;10 These data can be simulated driving in a very low trouble stability environment rather than asking actually a driver to go to do that. So these are also use of their simulators and synthetic data generation. So we expand the data as much as we can in the synthetic domain. And also we make our model to work with less amount of data.   00;34;35;16 - 00;34;55;27 So hopefully in future we are not abandoning this specific application and the use of AI in there because we don't have data. And if our audience does want to learn more about you or your research or the lab, is there any way they can do that or get in touch with you? Yes. My email, I'm actually very fast and responding to email.   00;34;56;00 - 00;35;41;19 You can find my email at my web page.  And also you can find me a LinkedIn, send me a message there we we share our news in different platform but yeah the best way contacting me send me an email we do have them also even high schooler at our school right now that I'm talking with you Mike I have three high schooler they are collecting data from an avatar in fact in completely virtual world and they are just we are we want to use that to train our model to detect how intense to reach and grasp.   00;35;41;21 - 00;38;06;24 Gosh, that's great. So, Sarah, thank you so much for being on the show with us today. And to help people find you, I'm just going to spell your last name for them. It's Ostadabbas. So that's the way you can look up Sarah. And if you are interested in how Oracle can simplify and accelerate your research, check out Oracle dot com slash research and join us next time on Research in Action.  

Dr. Antoine van Oijen is a Distinguished Professor and Australian Research Council Laureate Fellow in the School of Chemistry at the University of Wollongong in Australia. The work Antoine does combines physics, chemistry, and biology. He develops new types of microscopes to visualize complex biochemical reactions at the level of individual molecules. In particular, his group is interested in how DNA is copied before a cell divides. Antoine is also doing research examining how bacteria acquire antibiotic resistance. Antoine and his family enjoy spending much of their free time exploring the beautiful beach and hiking in the wonderful parks nearby. Antoine received his MSc and PhD in Physics from Leiden University in the Netherlands, where his graduate work was recognized with the C.J. Kok prize for best doctoral thesis. Afterwards, he conducted postdoctoral research in Chemistry and Chemical Biology at Harvard University. Antoine served on the faculty at Harvard Medical School and Groningen University in the Netherlands before his recent move to the University of Wollongong where he is today. Antoine has received a wide array of honors and awards for his research, including the Armenise-Harvard Junior Faculty Award, a Searle Scholarship, a NSF CAREER Award, a Vici Award from the Dutch Science Foundation, the Dutch Society for Biochemistry and Molecular Biology Award for the most promising young scientist, and the prestigious Australian Research Council Laureate Fellowship. In this episode, Antoine discusses his research and his journey through life and science.

Dr. Christy Haynes is the Elmore H. Northey Professor of Chemistry at the University of Minnesota. In Christy's research group, they are working to develop new methods to monitor small quantities of important chemicals in complex environments. Their research also aims to develop new, safe nanomaterials for applications in human health and sustainable energy. When she's not at work, Christy loves to go for a run around the lakes of Minneapolis and spend time with her spouse and two kids. Her son has an analytic mind and is interested in competitive sports, while her daughter enjoys art and music. She completed her undergraduate studies in Chemistry at Macalester College and received her MS and PhD in Chemistry from Northwestern University. Next, Christy was awarded a National Institutes of Health National Research Service Award Post-Doctoral Fellowship to conduct research at the University of North Carolina, Chapel Hill. She joined the faculty at the University of Minnesota in 2005. Christy has received many awards and honors for her research, including the Sara Evans Faculty Woman Scholar/Leader Award, the Taylor Award for Distinguished Research from the University of Minnesota, the Kavli Foundation Emerging Leader in Chemistry Lectureship, the Pittsburgh Conference Achievement Award, the Joseph Black Award from the Royal Society of Chemistry, an Alfred P. Sloan Fellowship, the Arthur F. Findeis Award for Achievements by a Young Analytical Scientist from the American Chemical Society Division of Analytical Chemistry, the Society for Electroanalytical Chemistry Young Investigator Award, the Camille and Henry Dreyfus Teacher-Scholar Award, the NIH New Innovator Award, the NSF CAREER Award, and the Victor K. LaMer Award from the American Chemical Society Division of Colloid and Surface Science. In addition, Christy has been recognized for her excellence in mentoring through receipt of the Advising and Mentoring Award and the Outstanding Postdoctoral Mentor Award both from the University of Minnesota. She has also been listed among the Top 100 Inspiring Women in STEM from Insight into Diversity magazine, the Analytical Scientist's “Top 40 Under 40” Power List, and one of the “Brilliant 10” chosen by Popular Science magazine. Christy is with us today to share stories from her journey through life and science.

Despite the demonstrated success of metal additive manufacturing (AM) in various industries, the performance uncertainty of AM parts undermines the potential of deploying AM for high-consequence applications. Air travel. Space travel. That sort of thing.Which is why the NSF is turning to assistant industrial and systems engineering professor Peter Liu. The Samuel Ginn College of Engineering's latest NSF CAREER Award winner recently sat down with the #GINNing crew to discuss the challenges of AM fatigue failure research — and the challenges of getting rice in northern China.

Dr. Jennifer Wilcox is an Associate Professor in Chemical and Biological Engineering at the Colorado School of Mines and an Investigator within the Clean Energy Conversions Laboratory there. The research in Jen's group focuses on carbon capture and trace metal pollution. On the carbon capture side, she tries to better understand and reduce CO2 emissions from coal and gas-fired power plants. In terms of trace metals, the most common source of trace metals like mercury in the fish we eat is burning coal in coal-fired power plants. Jen's research examines how to capture trace metals and reduce their emission into the environment. Outside of science, Jen keeps busy spending time with her family including her husband and daughter. She also loves being active outdoors through hiking, running, and bicycling. She received her bachelor's degree in mathematics from Wellesley College and her PhD in chemical engineering from the University of Arizona. She served on the faculty at the Worcester Polytechnic Institute and at Stanford University before joining the faculty at the Colorado School of Mines. Jen has received numerous awards and honors, including an Army Research Office Young Investigator Award, an American Chemical Society Petroleum Research Fund Young Investigator Award, and an NSF CAREER Award. She also was awarded the Stern Award for Distinguished Paper from the Journal of the Air and Waste Management Association. Jen is with us today to tell us all about her life and science.

The AI Asia Pacific Institute (AIAPI) is hosting a series of conversations with leading artificial intelligence (AI) experts to study ChatGPT and its risks, looking to arrive at tangible recommendations for regulators and policymakers. These experts include Dr. Toby Walsh, Dr. Stuart Russell, Dr. Pedro Domingos, and Dr. Luciano Floridi, as well as our internal advisory board and research affiliates. We have published a briefing note outlining some of the critical risks of generative AI and highlighting potential concerns. The following is a conversation with Dr. Pedro Domingos.  Dr. Pedro Domingos is a professor emeritus of computer science and engineering at the University of Washington and the author of The Master Algorithm. He is a winner of the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the highest honors in data science and AI. He is a Fellow of the AAAS and AAAI, and has received an NSF CAREER Award, a Sloan Fellowship, a Fulbright Scholarship, an IBM Faculty Award, several best paper awards, and other distinctions. Dr. Domingos received an undergraduate degree (1988) and M.S. in Electrical Engineering and Computer Science (1992) from IST, in Lisbon, and an M.S. (1994) and Ph.D. (1997) in Information and Computer Science from the University of California at Irvine. He is the author or co-author of over 200 technical publications in machine learning, data mining, and other areas. He is a member of the editorial board of the Machine Learning journal, co-founder of the International Machine Learning Society, and past associate editor of JAIR. Dr. Domingos was program co-chair of KDD-2003 and SRL-2009, and served on the program committees of AAAI, ICML, IJCAI, KDD, NIPS, SIGMOD, UAI, WWW, and others. He has written for the Wall Street Journal, Spectator, Scientific American, Wired, and others. He helped start the fields of statistical relational AI, data stream mining, adversarial learning, machine learning for information integration, and influence maximization in social networks. *** For show notes and past guests, please visit https://aiasiapacific.org/podcast/ For questions, please contact us at contact@aiasiapacific.org or follow us on Twitter or Instagram to stay in touch.

MLOps Coffee Sessions #155 with Matei Zaharia, The Birth and Growth of Spark: An Open Source Success Story, co-hosted by Vishnu Rachakonda. // Abstract We dive deep into the creation of Spark, with the creator himself - Matei Zaharia Chief technologist at Databricks. This episode also explores the development of Databricks' other open source home run ML Flow and the concept of "lake house ML". As a special treat Matei talked to us about the details of the "DSP" (Demonstrate Search Predict) project, which aims to enable building applications by combining LLMs and other text-returning systems. // About the guest: Matei has the unique advantage of being able to see different perspectives, having worked in both academia and the industry. He listens carefully to people's challenges and excitement about ML and uses this to come up with new ideas. As a member of Databricks, Matei also has the advantage of applying ML to Databricks' own internal practices. He is constantly asking the question "What's a better way to do this?" // Bio Matei Zaharia is an Associate Professor of Computer Science at Stanford and Chief Technologist at Databricks. He started the Apache Spark project during his Ph.D. at UC Berkeley, and co-developed other widely used open-source projects, including MLflow and Delta Lake, at Databricks. At Stanford, he works on distributed systems, NLP, and information retrieval, building programming models that can combine language models and external services to perform complex tasks. Matei's research work was recognized through the 2014 ACM Doctoral Dissertation Award for the best Ph.D. dissertation in computer science, an NSF CAREER Award, and the US Presidential Early Career Award for Scientists and Engineers (PECASE). // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links https://cs.stanford.edu/~matei/ https://spark.apache.org/ --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Vishnu on LinkedIn: https://www.linkedin.com/in/vrachakonda/ Connect with Matei on LinkedIn: https://www.linkedin.com/in/mateizaharia/ Timestamps: [00:00] Matei's preferred coffee [01:45] Takeaways [05:50] Please subscribe to our newsletters, join our Slack, and subscribe to our podcast channels! [06:52] Getting to know Matei as a person [09:10] Spark [14:18] Open and freewheeling cross-pollination [16:35] Actual formation of Spark [20:05] Spark and MLFlow Similarities and Differences [24:24] Concepts in MLFlow [27:34] DJ Khalid of the ML world [30:58] Data Lakehouse [33:35] Stanford's unique culture of the Computer Science Department [36:06] Starting a company [39:30] Unique advice to grad students [41:51] Open source project [44:35] LLMs in the New Revolution [47:57] Type of company to start with [49:56] Emergence of Corporate Research Labs [53:50] LLMs size context [54:44] Companies to respect [57:28] Wrap up

Dr. Tessa Hill is an Associate Professor in the Department of Earth and Planetary Sciences at the University of California, Davis. She is part of the Bodega Ocean Acidification Research group there at the Bodega Marine Laboratory. Research in Tessa's lab focuses on the ocean and the impacts of climate change on environments in the ocean in the past, present, and future. Outside of work, Tessa, her husband, and their two children spend a lot of time gardening, skiing, hiking, camping, and going on vacations together. Additionally, Tessa is a long-distance runner, so she enjoys running half and full marathons. Tessa received her B.S. in Marine Science from Eckerd College and her Ph.D. in Marine Science from the University of California, Santa Barbara. Next, Tessa was awarded a University of California President's Postdoctoral Fellowship at UC Davis before joining the faculty there. Tessa has received many awards and honors during her career, including the Presidential Early Career Award for Scientists and Engineers, as well as an NSF CAREER Award. She is also a Fellow of the California Academy of Sciences, an American Association for the Advancement of Science Leshner Public Engagement Fellow, and a panelist on the West Coast Ocean Acidification and Hypoxia Panel. Tessa is with us today to tell us about her journey through life and science.

Dr. Adam Abate is an Associate Professor in the Department of Bioengineering and Therapeutic Sciences at the University of California San Francisco. He is also a co-founder of the startup company Mission Bio. The overall goal of Adam's lab is to make biology a new kind of computer science. It is important to characterize the state of biological systems in detail so you can manipulate the system successfully to get the outcome you want. For example, a disease represents a problem with a biological system, and you have to understand the system and know what to change to successfully cure a disease. Adam builds technologies, focusing on microfluidics technologies, to allow us to comprehensively characterize cells in a system. When he's not doing science, Adam and his wife have been working on various home improvement projects around the house, including painting and installing new lighting. The instant gratification of remodeling is a refreshing contrast to work in the lab. Adam received his B.A. in Physics from Harvard College, his M.S. in Physics from the University of California Los Angeles, and his PhD in Physics from the University of Pennsylvania. Afterwards, Adam conducted postdoctoral research in Physics and Engineering at Harvard University, and during this time, his research became the foundation for the sequencing company GnuBIO. Adam is currently a member of the California Institute for Quantitative Biosciences (QB3) program that helps launch start-up companies on the UC campuses. He has received a number of awards and honors during his career, including the NSF CAREER Award, the NIH New Innovator Award, and the Presidential Early Career Award. Adam is here with us today to share stories about his life and science.

Chelsea Finn joins Host Pieter Abbeel to discuss distribution shift, meta-learning, editing LLMs, single-life RL, and what can AI not (yet) do today. Chelsea is a renowned expert in the field of robotics and artificial intelligence. She is an Assistant Professor in the Computer Science Department and the Electrical Engineering Department at Stanford University and is also a research scientist at Google Brain. Her research focuses on developing algorithms for robots and other intelligent systems that can learn from experience and adapt to new situations. She is a recipient of numerous awards, including the NSF CAREER Award, the MIT Technology Review 35 Innovators Under 35 Award, and the Sloan Research Fellowship.SUBSCRIBE TO THE ROBOT BRAINS PODCAST TODAY | Visit therobotbrains.ai and follow us on YouTube at TheRobotBrainsPodcast, Twitter @therobotbrains, and Instagram @therobotbrains. Hosted on Acast. See acast.com/privacy for more information.

As technology becomes a more prevalent part of health care communication, the role of compassionate communication remains important as mediums change. Ashley Barrett, associate professor in communication, recently earned a prestigious NSF CAREER Award to study how patients and providers adopt and adapt together. In this Baylor Connections, she takes listeners inside that study and examines communication's role in health outcomes.

In this episode, Professor Antonia Zaferiou shares her journey through art and science to becoming an Assistant Professor in the Department of Biomedical Engineering at Stevens Institute of Technology, as well as some of the exciting research projects in her lab. We discuss her recent and innovative NSF CAREER Award project using adaptive biofeedback (think the strings section of an orchestra responding to changes in angular momentum while you walk) to improve balance during everyday mobility. Antonia openly shares her passion for outreach in the episode and via the American Society of Biomechanics Teaching Repository. Thank you to our sponsor, Delsys! Enter a prize draw to win a two-sensor Trigno Lite system: https://delsys.com/boom Connect with Antonia Zaferiou! Lab website: https://www.zaferioulab.com/ Personal website: http://antonia-zaferiou.squarespace.com/ Twitter: @AZaferiou Connect with BOOM! Twitter, Instagram, and Facebook: @biomechanicsonourminds LinkedIn: linkedin.com/company/biomechanicsoom/ YouTube: Biomechanics On Our Minds Website and shop: biomechanicsonourminds.com

In this episode of ACM ByteCast, Bruke Kifle hosts Matei Zaharia, computer scientist, educator, and creator of Apache Spark. Matei is the Chief Technologist and Co-Founder of Databricks and an Assistant Professor of Computer Science at Stanford. He started the Apache Spark project during his PhD at UC Berkeley in 2009 and has worked broadly on other widely used data and machine learning software, including MLflow, Delta Lake, and Apache Mesos. Matei's research was recognized through the 2014 ACM Doctoral Dissertation Award, an NSF Career Award, and the US Presidential Early Career Award for Scientists and Engineers. Matei, who was born in Romania and grew up mostly in Canada, describes how he developed Spark, a framework for writing programs that run on a large cluster of nodes and process data in parallel, and how this led him to co-found Databricks around this technology. Matei and Bruke also discuss the new paradigm shift from traditional data warehouses to data lakes, as well as his work on MLflow, an open-source platform for managing the end-to-end machine learning lifecycle. He highlights some recent announcements in the field of AI and machine learning and shares observations from teaching and conducting research at Stanford, including an important current gap in computing education.

Dr. Erin E. Carlson is an Associate Professor in the Department of Chemistry at the University of Minnesota. Research in Erin's lab focuses on microbes. They are interested in how these organisms interact with one another, humans, and the environment. Over the course of modern medicine, we've come to appreciate that microbes make a lot of potentially important therapeutic agents. In particular, Erin's group is studying how microbes may be able to continue to provide us with antibacterial agents despite issues with increasing antibiotic resistance. Travel is a passion for Erin, and as a scientist, she has had many wonderful travel opportunities. She particularly enjoyed going on a safari in Tanzania, as well as traveling to Indonesia and South America to present her research. In addition, Erin is an avid photographer who documents all the places she has been in the world through her photos. Erin received her B.A. in chemistry from St. Olaf College and her PhD in organic chemistry from the University of Wisconsin, Madison. Subsequently, Erin was awarded an American Cancer Society Postdoctoral Fellowship to conduct research at The Scripps Research Institute. She served on the faculty at Indiana University before joining the faculty at the University of Minnesota where she is today. Among her many awards and honors, Erin is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE), a Pew Biomedical Scholarship, the NIH Director's New Innovator Award, the Indiana University Outstanding Junior Faculty Award, an NSF CAREER Award, and the Cottrell Scholar Award. In addition, she was named a Sloan Research Fellow, an Indiana University Dean's Fellow, and an American Chemical Society Women Chemists Committee Rising Star. In our interview, Erin shares some of her experiences in life and science.

In this episode, Balaji Prabhakar, co-founder of Clockwork and Professor of Computer Science at Stanford, discusses congestion problems in data centers and real-life congestion issues. Challenges when dealing with network congestion within cloud infrastructures. What does it mean to build out a system of accurate clocks? Understanding the package switching networking and distributor systems. Creating fair transactions (first come, first serve) Uses cases for highly accurate clocks How do to evaluate and solve congestion problems? About today's guest: Balaji Prabhakar is VMWare Founders Professor of Computer Science and a faculty member in the Departments of Electrical Engineering and Computer Science and, by courtesy, in the Graduate School of Business at Stanford University. His research interests are computer networks, notably in Data Center Networks and Cloud Computing Platforms. His work spans network algorithms, congestion control protocols, and stochastic network theory. He has also worked on Societal Networks, where he has developed "nudge engines" to incentivize commuters to travel in off-peak times so that congestion, fuel, and pollution costs are reduced. Balaji has been a Terman Fellow at Stanford University and a Fellow of the Alfred P. Sloan Foundation, IEEE, and ACM. He has received the NSF CAREER Award, the Erlang Prize from the INFORMS Applied Probability Society, the Rollo Davidson Prize was given to young Statisticians and Probabilists, and he delivered the Lunteren Lectures of the Dutch Operations Research Society. He is the inaugural recipient of the IEEE Innovation in Societal Infrastructure Award, which recognizes "significant technological achievements and contributions to the establishment, development, and proliferation of innovative societal infrastructure systems." He has received the IEEE Koji Kobayashi Award for his work on Computer Networks and the ACM Sigmetrics Award for his work on Stochastic Network Theory. He is a co-recipient of a few best paper and test of time awards In 2011, he co-founded Urban Engines (acquired by Google in 2016) and is currently on leave at Clockwork.io where he is co-founder and CEO. LinkedIn: https://www.linkedin.com/in/balaji-prabhakar-45426bb7/ ___ Thank you so much for checking out this episode of The Tech Trek, and we would appreciate it if you would take a minute to rate and review us on your favorite podcast player. 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)

The mission of the Cybersecurity Assurance and Policy (CAP) Center at Morgan State University is to provide the defense and intelligence community with the knowledge, methodology, solutions, and highly skilled cybersecurity professionals to mitigate penetration and manipulation of our nation's cyber-physical infrastructure. Internet of Things (IoT) devices permeate all areas of life and work, with unprecedented economic effects. Critical infrastructures in transportation, smart grid, manufacturing, health care, and many others depend on embedded systems for distributed control, tracking, and data collection. While protecting these systems from hacking, intrusion, and physical tampering is paramount, current solutions rely on unsustainable patchwork solutions. Transformative solutions are required to protect systems where the ubiquity of connectivity and heterogeneity of IoT devices exacerbate the attack surface. Our research focuses on the convergence of IoT, 5G, and artificial intelligence in the context of the Zero Trust networks. We will present our security-in-depth approach to provide secure and resilient operation. About the speaker: Dr. Kevin T. Kornegay received the B.S. degree in electrical engineering from Pratt Institute, Brooklyn, NY, in 1985 and the M.S. and Ph.D. degrees in electrical engineering from the University of California at Berkeley in 1990 and 1992, respectively. He is currently the Eugene Deloatch IoT Security Endowed Professor and Director of the Cybersecurity Assurance and Policy (CAP) Center for Academic Excellence in the Electrical and Computer Engineering Department at Morgan State University in Baltimore, MD. His research interests include hardware assurance, reverse engineering, secure embedded systems, side-­‐channel analysis, and differential fault analysis. Dr. Kornegay serves or has served on the technical program committees of several international conferences, including the IEEE Symposium on Hardware Oriented Security and Trust (HOST), USENIX Security, the IEEE Physical Assurance and Inspection of Electronics (PAINE), and the ACM Great Lakes Symposium on VLSI (GLSVLSI). He is the recipient of numerous awards, including He is the recipient of multiple awards, including the NSF CAREER Award, IBM Faculty Partnership Award, National Semiconductor Faculty Development Award, and the General Motors Faculty Fellowship Award. He is currently a senior member of the IEEE, and Eta Kappa Nu, Sigma Xi, and Tau Beta Pi engineering honor societies.

We have two special announcements!Very soon we will be celebrating a decade of DNA Today! That's right, we released our first episode on September 1st, 2012. It also coincides with our 200th episode. We want to mark these milestones with you on the show. So send in your favorite episode. You can write it, or better yet, record a voice memo sharing your favorite episode and why you enjoy listening to the show. After all, our podcast would not be possible without you loyal listeners. That's why we want to celebrate together! Send in your voice memo or written message about your fav episode of DNA Today to info@dnapodcast.com. Deadline is August 27th.Thank you to all you listeners for nominating us in the Podcast Awards, you did it! We have officially been nominated. It's year number 6 being nominated and it might be our third time winning the Best Science and Medicine Podcast Award. BUT that's only gong to happen if you check your email inbox for an email from The Podcast Awards with the subject line, “Podcast Awards Final Slate Voting”'. If you got this email you are one of the few that were selected to be a voter. It's imperative that you vote! There is a hyperlink to click to get to the voting page. You do have to quickly log back in. Once you do, select DNA Today in the “Science and Medicine category”, select your other fav podcasts and then Hit the “Save Nominations” button. It's that easy. You have until September 10th to do this, but please do it now if you got the email so you don't forget! Can't thank you all enough! Special shoutout to the following listeners for sharing after they nominated us…Heather, Dan, Janelle, Steven, Doug, Lynn, Taila, Lorraine, Katherine, Barbara, Jerry, Catherine, Kim, Ashlyn, Pricilla, Jane, Rob, Hari, Vishnu, Leticia, Meli, Wright, Mahfuz, Anne, Laura, Molly, Hibat, Rachael, Carol, Hal, Romer, JoanneJoining us this week is Dr. Kiana Aran, Associate Professor of Medical Diagnostics and Therapeutics and head of the Aran Lab at Keck Graduate Institute (KGI) where she works to develop CRISPR Quality Control standards.In addition to her important work at KGI, Dr. Aran is also the Chief Scientific Officer of Cardea Bio, is a visiting Assistant Professor at UC Berkeley, and serves as a Consultant of Drug Delivery and Medical Diagnostics for the Bill & Melinda Gates Foundation. She received her undergraduate degree in electrical engineering from the City University of New York in 2007 and her Ph.D. in Biomedical Engineering at the Rutgers University in 2012. She then continued her postdoctoral studies in bioengineering at the University of California, Berkeley and was a recipient of the National Institutes of Health (NIH) postdoctoral training fellowship at the Buck Institute for Aging Research in 2015. Her efforts have been recently recognized by many awards in science and STEM including the Clinical OMICs 10 under 40 Award and the Athena Pinnacle Award. Dr. Aran was also the recipient of the NSF Career Award to develop the next generation of electronic sensors, and Nature's Scientific Achievment Award in 2021.On This Episode We Discuss:What is CRISPR and how does it work? How CRISPR is different from other genetic editing technologiesCurrent standards for therapeutic applications using CRISPRPotential side effects of CRISPR treatmentsThe risk for off target CRISPR'd edits (edits in other genes that were not intended)The CRISPR Quality Control standards that Dr. Aran's lab is developingCRISPR-ChipTMApplications of CRISPR-Cas systems beyond genome editingPredicting when CRISPR treatments will be clinically available outside of studiesCRISPR babiesDr. Aran's most recent NIH grant and future workLearn more about Dr. Aran's research by visiting aranlab.org and read about her 1.63 million dollar NIH grant to help set Quality Control Standards for CRISPR Therapies! You can also read the paper that she co-authored in The CRISPR Journal about applications of CRISPR-Cas systems beyond genome editing in 2021. Follow Dr. Aran on Twitter and LinkedIn, and follow the Aran lab on Instagram!Stay tuned for the next new episode of DNA Today on August 19th, 2022 where we'll be joined by Sam Sternberg, co-author of A Crack in Creation (who he wrote with Nobel Prize winner, Jennifer Doudna), to continue our discussion about CRISPR! New episodes are released on Fridays. In the meantime, you can binge over 195 other episodes on Apple Podcasts, Spotify, streaming on the website, or any other podcast player by searching, “DNA Today”. Episodes since 2021 are also recorded with video which you can watch on our YouTube channel. DNA Today is hosted and produced by Kira Dineen. Our social media lead is Corinne Merlino. Our video lead is Amanda Andreoli. See what else we are up to on Twitter, Instagram, Facebook, YouTube and our website, DNApodcast.com. Questions/inquiries can be sent to info@DNApodcast.com. PerkinElmer Genomics is a global leader in genetic testing focusing on rare diseases, inherited disorders, newborn screening, and hereditary cancer. Testing services support the full continuum of care from preconception and prenatal to neonatal, pediatric, and adult. Testing options include sequencing for targeted genes, multiple genes, the whole exome or genome, and copy number variations. Using a simple saliva or blood sample, PerkinElmer Genomics answers complex genetic questions that can proactively inform patient care and end the diagnostic odyssey for families. Learn more at PerkinElmerGenomics.com. (SPONSORED)

How do we use technology to improve quality of life? What is the role of robots to improve human health and quality of life now and in the future? With robotics and AI, there is technology for helping people know themselves and help themselves. Things like mindfulness, cognitive behavior therapy, known interventions that are hard for people to do on their own can be done with a robot. Until everyone can have someone to help them, socially-assistive robots can fill a big void. Dr. Maja Matarić is a Chan Soon-Shiong distinguished professor of Computer Science, Neuroscience, and Pediatrics and the founder and director of the Interaction Lab at the University of Southern California where her research is aimed at developing technologies (particularly in robotics) that help improve human health and quality of life. Among others, Matarić received the Presidential Award for Excellence in Science, Mathematics, and Engineering Mentoring from President Barack Obama (2009), the Okawa Foundation Research Award, the NSF Career Award, the MIT Technology Review TR100 Innovation Award, the IEEE Robotics and Automation Society Early Career Award, and the Anita Borg Institute Women of Vision Award in Innovation. She is a Fellow of the American Association for the Advancement of Science (AAAS), IEEE, AAAI, and ACM.

Sanmay Das is a professor of Computer Science at George Mason University. From 2013-2020, he was on the faculty of the Dept. of Computer Science and Engineering at Washington University in St. Louis, where he also founded and served as the first chair of the steering committee of the Division of Computational and Data Sciences. Dr. Das received his Ph.D. from MIT and a Bachelor's degree from Harvard, both in Computer Science.He is chair of the Association for Computing Machinery Special Interest Group on Artificial Intelligence, a member of the board of directors of the International Foundation for Autonomous Agents and Multiagent Systems, and serves as an associate editor for the ACM Transactions on Economics and Computation, the Journal of Artificial Intelligence Research, and Autonomous Agents and Multiagent Systems. He has been recognized with awards for research and teaching, including an NSF CAREER Award and the Department Chair Award for Outstanding Teaching at Washington University.

This week we catch up with Dr. Siwei Lyu, a SUNY Empire Innovation Professor and founding Co-Director of Center for Information Integrity (CII) at the University at Buffalo, State University of New York. Siwei breaks down the deepfake experience, both the good and the misleading aspects of the technology. He shares insights on techniques researchers are developing to detect deepfakes, including GAN (Generative Adversarial Network) detected artifacts that produce tell-tale deepfake signs – if you know where to look. He also delves into the area of audio deepfakes and the sophistication of the human auditory system that makes this pathway a tough one to win. And, fun fact, to learn more about Siwei's research contributions be sure to Google “DeepFake-o-meter”!    Dr. Siwei Lyu, SUNY Empire Innovation Professor at the University at Buffalo Dr. Siwei Lyu received his B.S. degree (Information Science) in 1997 and his M.S. degree (Computer Science) in 2000, both from Peking University, China. He received his Ph.D. degree in Computer Science from Dartmouth College in 2005. From 1998 to 2000, he worked at the Founder Research and Development Center (Beijing, China) as a Software Engineer. From 2000 to 2001, he worked at Microsoft Research Asia (then Microsoft Research China) as an Assistant Researcher. From 2005 to 2008, he was a Post-Doctoral Research Associate at the Howard Hughes Medical Institute and the Center for Neural Science of New York University. Starting in 2008, he is Assistant Professor at the Computer Science Department of University at Albany, State University of New York. Dr. Lyu is the recipient of the Alumni Thesis Award of Dartmouth College in 2005, IEEE Signal Processing Society Best Paper Award in 2010, and the NSF CAREER Award in 2010. He has authored one book, and held two U.S. and one E.U. patents. He has published more than 50 conference and journal papers in the research fields of natural image statistics, digital image forensics, machine learning and computer vision. For links and resources discussed in this episode, please visit our show notes at https://www.forcepoint.com/govpodcast/e180

Dr. Tara Alvarez is Professor of Bio-Medical Engineering at New Jersey Institute of Technology and Chief Scientific Officer at OculoMotor Technologies. Tara's research focuses on how we move our eyes, how visual information is brought in through our visual system, and how the brain changes. In particular, she studies a condition called convergence insufficiency. In this condition, people have difficulty and discomfort when reading or maintaining focus on near objects. She is working to better understand convergence insufficiency and how the brain changes during visual therapy, resulting in reduced symptoms. In her free time, Tara loves spending time with her kids, doing renovation projects at home, cooking, and gardening. She was awarded her B.S. in Electrical Engineering and her Ph.D. in Bioengineering and Biomedical Engineering from Rutgers University. Afterwards, she conducted postdoctoral research at Bell Labs before joining the faculty at New Jersey Institute of Technology. She has received numerous awards and honors in her career, including an NSF Career Award, the Founding Members Award for Science from the Neuro-Optometric Rehabilitation Association, an Edison Patent Award, the NJIT Excellence in Research Award, and Augmented World Expo's Auggie Awards for Women XR Laureate and for Most Innovative Breakthrough. She has also been named an Outstanding Woman Scientist of NJ, a Fellow of the American Academy of Optometry, and a Fellow of the American Institute for Medical and Biological Engineering. In this interview, Tara shares more about her life and science.

In interview 21 of The Gradient Podcast, we talk to Percy Liang, an Associate Professor of Computer Science at Stanford University and the director of the Center for Research on Foundation Models.Subscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterPercy Liang's research spans many topics in machine learning and natural language processing, including robustness, interpretability, semantics, and reasoning.  He is also a strong proponent of reproducibility through the creation of CodaLab Worksheets.  His awards include the Presidential Early Career Award for Scientists and Engineers (2019), IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), a Microsoft Research Faculty Fellowship (2014), and multiple paper awards at ACL, EMNLP, ICML, and COLT.Sections:(00:00) Intro(01:21) Start in AI(06:52) Interest in Language(10:17) Start of PhD(12:22) Semantic Parsing(17:49) Focus on ML robustness(22:30) Foundation Models, model robustness(28:55) Foundation Model bias(34:48) Foundation Model research by academia(37:13) Current research interests(39:40) Surprising robustness results(44:24) Reproducibility and CodaLab(50:17) OutroPapers / Topics discussed:On the Opportunities and Risks of Foundation ModelsReflections on Foundation ModelsRemoving spurious features can hurt accuracy and affect groups disproportionately.Selective classification can magnify disparities across groups Just train twice: improving group robustness without training group information LILA: language-informed latent actions CodaLab Get full access to The Gradient at thegradientpub.substack.com/subscribe

Today I speak with Drexel University dean of engineering, Professor Sharon Walker about engineering education in the time of a pandemic. Dr. Sharon L. Walker, PhD, is Dean of Drexel's College of Engineering and Distinguished Professor in the Department of Civil, Architectural and Environmental Engineering. A Yale University-trained water quality systems expert focusing on the fate and transport of bacteria and nanoparticles in water, Walker is also a fellow in the Association of Environmental Engineering and Science Professors (AEESP) and in the American Association for the Advancement of Science (AAAS). She is a winner of the Fulbright Fellowship, for which she visited at Ben Gurion University of the Negev in Israel; received an NSF Career Award in 2010; and held an ELATE fellowship from 2014-15. Walker has produced more than 250 conference papers and publications, and in 2018 won the AEESP inaugural Mary Ann Liebert Award for Publication Excellence in Environmental Engineering Science.

John Swierk received his undergraduate degrees in Chemistry and Materials Science and Engineering from the University of Pennsylvania in 2008 and a Ph.D. in Chemistry from the Pennsylvania State University in 2014, where he worked with Tom Mallouk. After leaving Penn State he completed a postdoctoral appointment at Lawrence Berkeley National Lab as part of the Joint Center for Artificial Photosynthesis working with T. Don Tilley. After a second postdoctoral appointment at Yale University with Charlie Schmuttenmaer, he was appointed as an Associate Research Scientist with the Yale Energy Sciences Institute. In 2018, he joined the faculty at Binghamton University (SUNY) as an assistant professor. His research focuses on radical reactions initiated by photo- and electrochemical methods, with diverse applications from small molecule synthesis to the photodegradation of tattoo inks. He has received funding from the Doctoral New Investigator Award from the American Chemical Society Petroleum and an R15 grant from the National Institutes of Health. In 2021, he received an NSF CAREER Award. Inking of Immunity is made possible by all these humans: Chris Lynn - Executive Producer & Co-host Becci Owens - Associate Producer & Co-host Mike Smetana - Associate Producer & Co-host Kira Yancey - Production Manager Find us on social media on Facebook (inking.of.immunity), Twitter (@inking_immunity), and Instagram (@inking.of.immunity)

Ayse Asatekin is an associate professor and the Steve and Kristen Remondi Faculty Fellow in the Chemical and Biological Engineering Department at Tufts University. She is an entrepreneurial academic that keeps her home base in academia, but has been a founding scientist and inventor on two university spinouts in the water sector, the most recent of which, ZwitterCo, just raised $6M to continue commercializing their technology to use zwitterions to coat membranes that are resistant to fouling and can treat waste streams with fats, oils, grease and more. Ayse provided a ton of insight into conducting research with an eye towards scale and commercialization, including the importance of de-risking inventions at an early stage in anticipation of what investors and industry might ask. She spoke very candidly about her past experiences in spinning out technologies, what she has learned, what she would advise young inventors considering doing the same, the importance of the team, and much more. It's an encouraging episode for any listeners that want to invent for commercialization but still remain in academia, dedicated to research. Enjoy! More about Ayse Ayse Asatekin received her bachelor's degrees in chemical engineering and chemistry from the Middle East Technical University in Ankara, Turkey. She went on to receive her Ph.D. in chemical engineering through the Program in Polymer Science and Technology (PPST) at MIT. She pursued her post-doctoral work with Prof. Karen K. Gleason, also at MIT. She co-founded Clean Membranes, Inc., a start-up company that commercialized the polyacrylonitrile-based membrane technology that she began developing during her doctoral research, and worked as its Principal Scientist before joining the Tufts faculty in 2012. Novel membrane technologies developed in her lab are currently being commercialized at ZwitterCo, Inc., where she serves as the Senior Scientific Advisor. She is the recipient of the NSF CAREER Award, Massachusetts Clean Energy Council's Catalyst Award, and the Turkish American Scientists and Scholars Young Scholar Award. Her research interests are in developing novel membranes for clean water and energy-efficient separations. She is also interested in multi-functional membranes, controlling surface chemistry for biomedical applications, polymer science, and energy storage. To connect with Ayse, visit https://engineering.tufts.edu/chbe/people/faculty/ayse-asatekin More about ZwitterCo ZwitterCo is a national industrial membrane provider, delivering patented filtration technologies that are durable and fouling resistant. The company recently closed a $5.9m fundraising round led by Mann+Hummel Corporate Ventures, and has been recognized by the Department of Energy, the National Science Foundation, and the Massachusetts Clean Energy Center as a leader among clean water technologies. ZwitterCo's cutting-edge membrane chemistry, paired with the company's deep domain expertise, gives industrial processing facilities and their partners a pathway into the next generation of water treatment, precision separation, and resource recapture. For more information, visit https://www.zwitterco.com/. Join the Bountiful community today and realize your power to save the world. Don't forget to follow us on Twitter and LinkedIn if you haven't already.

In this episode, Geoff and Lauren discuss a range of causal concepts and analogies that we encounter in scientific work. Lauren is doing important work in philosophy of science, writing about casual explanations. She argues that the concept of a ‘mechanism' (an analogy to a machine) has been over-extended, particularly by new mechanist philosophers. Lauren points to the other causal concepts used by scientists: a ‘pathway' (an analogy to a roadway) and a ‘cascade' (an analogy to a waterfall or the snowball effect). The evidence points towards a diversity of causal concepts and causal structures. Geoff and Lauren discuss: analogies for causation (mechanism, pathway, cascade); distinguishing mechanism vs. pathway; distinguishing mechanism vs. cascade; making connection in different domains of life; analogies and other language for explaining; stories and visual imagery; causation and the goal of control; the observer in science; insights from cognitive science (in fields like causal cognition or cognitive metaphysics); investigating human biases; Lauren's background from medical school to HPS; big picture questions about medicine (biology, diseases, patient outcomes); applying medical training to HPS; causation as it features in different scientific disciplines; background conditions in biology vs. physics; three types of pluralism (structures, methods and the definition of causation); and other topics! *** Lauren Ross is an Associate Professor of Logic and Philosophy of Science at the University of California, Irvine. She has an MD from the School of Medicine at the University of California, Irvine and a PhD in History and Philosophy of Science from the University of Pittsburgh. This crossover influences a good deal of her work in philosophy of biology, philosophy of neuroscience and philosophy of medicine. Lauren's research focuses on causation and explanation in science. In her recent work, she analyses how scientists provide explanations by using various causal concepts and analogies, such as 'mechanism', ‘pathway' and ‘cascade'.  For this work she has received the NSF Career Award and the Humboldt Experienced Researcher Fellowship.  In other projects, Lauren has discussed causal explanation in neuroscience (neural connections in the brain), in psychiatry (psychiatric genetics) and in chemistry (the periodic table). https://www.lps.uci.edu/~rossl/ *** Follow Extrapolator on social media for all the latest news: instagram.com/extrapolatorpod facebook.com/extrapolatorpod linkedin.com/company/extrapolator

In this episode, I converse with Prof. Sujit Datta, an Assistant Professor of Chemical and Biological Engineering at Princeton University. Sujit earned a BA in Mathematics and Physics, and an MS in Physics, in 2008 from the University of Pennsylvania. He earned his PhD in Physics in 2013 from Harvard, where he studied fluid dynamics and instabilities in porous media and colloidal microcapsules with David Weitz after which he finished a postdoc in Chemical Engineering at Caltech, where he studied the biophysics of the gut with Rustem Ismagilov. He joined the faculty at Princeton in 2017 and been the recipient of multiple awards like the NSF CAREER Award, AIChE 35 Under 35 Award, ACS Unilever Award, APS Andreas Acrivos Award in Fluid Dynamics. Sujit's lab studies soft and living materials in complex settings, motivated by challenges like water remediation, carbon sequestration, oil/gas recovery, and targeted drug delivery, and their work integrates microscopy, microfluidics, soft materials chemistry, and biophysical characterisation. We indulge in an ebullient conversation on his wonderful journey through science and life; from early fascinations with economics and philosophy to pivoting to maths and physics; fascination with science and the fundamental role of curiosity-driven basic science research in helping shape the world; his terrific set of mentors; kickboxing the stress out of his life; the importance of diversity and inclusion in all walks of life; and many more things!!

Dr. Marie desJardins joined Simmons University as the Inaugural Dean of the College of Organizational, Computational, and Information Sciences in 2018. Previously, she was a member of the computer science faculty at the University of Maryland, Baltimore County, from 2001 to 2018, most recently as the Associate Dean for Academic Affairs in the College of Engineering and Information Technology. Before joining the faculty at UMBC, she was a Senior Computer Scientist at SRI International. She earned her A.B. in Engineering from Harvard University and her Ph.D. in Computer Science from the University of California, Berkeley.  Her research is in artificial intelligence, focusing on the areas of machine learning, multi-agent systems, decision making, and interactive AI. She was named one of the "Ten AI Researchers to Follow on Twitter" by TechRepublic and one of "14 Women in AI You Should Follow on Twitter" by craigconnects. She has published over 135 scientific papers on AI and CS education, and has been PI or co-PI on nearly $12,000,000 of external research funding, including a prestigious NSF CAREER Award. She has mentored 13 Ph.D. students, 27 M.S. students, and nearly 100 undergraduate researchers. She is known on campus and throughout her professional community for her dedication to mentoring, diversity, outreach, and innovative educational practices. Dr. desJardins is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and a Distinguished Member of the Association for Computing Machinery (ACM). She is a recipient of the Distinguished Alumni Award in Computer Science from UC Berkeley; the A. Richard Newton Educator ABIE Award from the Anita Borg Institute; the NCWIT Undergraduate Research Mentoring Award; and the CRA Undergraduate Research Mentoring Award. She was the 2014-2017 UMBC Distinguished Teaching Professor, was an inaugural UMBC Hrabowski Innovation Fellow, and was named one of UMBC's ten "Professors Not to Miss" in 2011.  Dr. desJardins is known nationally for her support of and commitment to improving student diversity, access, and quality of computer science courses at the high school level, and received multiple NSF awards to support her efforts in this area. She was the lead PI on the NSF-sponsored "CS Matters in Maryland" project, which created curriculum and trained high school teachers to teach the AP CS Principles course.  She built a statewide coalition in Maryland to increase access to K-12 CS education, with a focus on inclusion and diversity, and cofounded the Maryland Center for Computing Education, which received $5,000,000 in state funding for teacher preparation and advocacy. She was the Maryland team leader for the Exploring Computing Education Pathways (ECEP) Alliance and a founding member of the Maryland chapter of the Computer Science Teachers Association.

The interplay between learning goals, instructional support, and affordances of online technology can create a new learning environment for experimenting with what matters most and rethinking habits we have gotten into. Dr. Robin Adams talks to us about her personal experience of teaching engineering design online for the first time. She thoughtfully reflects on instructional decisions she had to make to help students navigate virtual teamwork workflows and successfully complete design challenges.Bio:Dr. Robin S. Adams  is a Professor in the School of Engineering Education at Purdue University. The recipient of a 2008 NSF Career Award, a Design Studies best paper award (2003), and the Journal of Engineering Education's Wickenden Award for best paper (2007), Dr. Adams is a national leader in researching interdisciplinary thinking and design learning, in connecting research and practice, and in building research capacity in engineering education. She leads the Institute for Scholarship on Engineering Education as part of the Center for the Advancement of Engineering Education and was an invited participant at the 2010 Frontiers of Engineering Education symposium.