Podcasts about Data science

Matei Zatreanu is the CEO and founder of System2, a data science firm that helps institutional investors make better decision. His family immigrated from Romania, but for the first few years, his parents had to leave him and his brother temporarily behind as they settled themselves in their new country.In our conversation, we explored how early hardship, like growing up separated by your parents, builds a “figure it out” mentality, why storytelling is one of the most underrated leadership skills, and how something as simple as sharing a meal can become the foundation of culture and trust inside an organization.We went deep into the meaning of profit, the tradeoffs entrepreneurs face when considering outside capital, and the importance of understanding your own motivations before chasing growth, money, or status.Contact Dino at: dino@al4ep.comWebsites:sstm2.comal4ep.comAdditional Guest Links:Podcast: on SpotifyOn YouTube: youtube.com/@system2podLinkedIn: linkedin.com/in/matei-zatreanu/Authentic Leadership For Everyday People / Dino CattaneoDino on LinkedIn: linkedin.com/in/dinocattaneoPodcast Instagram – @al4edp Podcast Twitter – @al4edpPodcast Facebook: facebook.com/al4edpMusicSusan Cattaneo: susancattaneo.bandcamp.com

Building a model for an academic paper is one thing. Building a model that has to work perfectly during the Cricket World Cup with millions watching is something else entirely. There's no room for the kind of errors that might be acceptable in research settings or even standard business applications.In this Value Boost episode, Prof. Steve Stern joins Dr. Genevieve Hayes to share practical lessons from deploying the Duckworth-Lewis-Stern method in high-pressure, real-time environments where mistakes have global consequences.You'll learn:Why model simplicity matters more than you think [02:04]The two types of errors you need to understand [03:21]How to test models for extreme situations [05:50]The balance between confidence and humility [07:37]Guest BioProf. Steve Stern is a Professor of Data Science at Bond University, and is the official custodian of the Duckworth-Lewis-Stern (DLS) cricket scoring system.LinksContact Steve at Bond UniversityConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

In this week's episode, Leslie Heaney sits down with Vasant Dhar—professor at NYU Stern School of Business and the Center for Data Science at New York University, founder of SCT Capital, and author of Thinking with Machines: The Brave New World of AI.Together, they explore how artificial intelligence evolved, why language prediction changed everything, and what it means now that machines can think alongside humans. The conversation examines the growing divide between those who use AI to sharpen judgment and those who rely on it to think for them, as well as the broader implications for work, education, power, and responsibility.This is a grounded, honest conversation about the power of AI—and how we choose to live with it.Hosted on Ausha. See ausha.co/privacy-policy for more information.

I was recently reading the January - February  2026 issue of "Science and Children," a publication of the National Science Teaching Association. In this issue, I read the section, "Science 101" written by Matt Bobrowsky. He wrote an article entitled "What is Data, and Is Data Science Really Science?" In the article, Matt addresses three misconceptions about data science: 1. Data refers only to numbers. 2. Data tells the whole story. 3. It's computers, not people, who do data science.

Topics covered in this episode: Better Python tests with inline-snapshot jolt Battery intelligence for your laptop Markdown code formatting with ruff act - run your GitHub actions locally Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 11am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Better Python tests with inline-snapshot Alex Hall, on Pydantic blog Great for testing complex data structures Allows you to write a test like this: from inline_snapshot import snapshot def test_user_creation(): user = create_user(id=123, name="test_user") assert user.dict() == snapshot({}) Then run pytest --inline-snapshot=fix And the library updates the test source code to look like this: def test_user_creation(): user = create_user(id=123, name="test_user") assert user.dict() == snapshot({ "id": 123, "name": "test_user", "status": "active" }) Now, when you run the code without “fix” the collected data is used for comparison Awesome to be able to visually inspect the test data right there in the test code. Projects mentioned inline-snapshot pytest-examples syrupy dirty-equals executing Michael #2: jolt Battery intelligence for your laptop Support for both macOS and Linux Battery Status — Charge percentage, time remaining, health, and cycle count Power Monitoring — System power draw with CPU/GPU breakdown Process Tracking — Processes sorted by energy impact with color-coded severity Historical Graphs — Track battery and power trends over time Themes — 10+ built-in themes with dark/light auto-detection Background Daemon — Collect historical data even when the TUI isn't running Process Management — Kill energy-hungry processes directly Brian #3: Markdown code formatting with ruff Suggested by Matthias Schoettle ruff can now format code within markdown files Will format valid Python code in code blocks marked with python, py, python3 or py3. Also recognizes pyi as Python type stub files. Includes the ability to turn off formatting with comment [HTML_REMOVED] , [HTML_REMOVED] blocks. Requires preview mode [tool.ruff.lint] preview = true Michael #4: act - run your GitHub actions locally Run your GitHub Actions locally! Why would you want to do this? Two reasons: Fast Feedback - Rather than having to commit/push every time you want to test out the changes you are making to your .github/workflows/ files (or for any changes to embedded GitHub actions), you can use act to run the actions locally. The environment variables and filesystem are all configured to match what GitHub provides. Local Task Runner - I love make. However, I also hate repeating myself. With act, you can use the GitHub Actions defined in your .github/workflows/ to replace your Makefile! When you run act it reads in your GitHub Actions from .github/workflows/ and determines the set of actions that need to be run. Uses the Docker API to either pull or build the necessary images, as defined in your workflow files and finally determines the execution path based on the dependencies that were defined. Once it has the execution path, it then uses the Docker API to run containers for each action based on the images prepared earlier. The environment variables and filesystem are all configured to match what GitHub provides. Extras Michael: Winter is coming: Frozendict accepted Django ORM stand-alone Command Book app announcement post Joke: Plug ‘n Paste

How do people become addicted to social media and what are the implications of such an addiction? [ dur: 30mins. ] Ofir Turel is Professor of Information Systems (IS) Management, IS group co-lead, University of Melbourne. He has published over 250 journal papers, two of those titles include The Benefits and Dangers of Enjoyment with Social Networking Websites and Followers Problematic Engagement with Influencers on Social Media and Attachment Theory Perspective. Most of our activity on the internet interacts with posts, memes and videos that are driven by algorithms. How might algorithms be biased, racist, or sexist, and how might they amplify those biases in us? [ dur: 28mins. ]  Full length of this interview can be found here. Tina Eliassi-Rad is a Professor of Computer Science at Northeastern University. She is also a core faculty member at Northeastern’s Network Science Institute and the Institute for Experiential AI. She is the author of Measuring Algorithmically Infused Societies and What Science Can Do for Democracy: A Complexity Science Approach. Damien Patrick Williams is Assistant Professor in Philosophy and Data Science at the University of North Carolina at Charlotte. He is the author of Why AI Research Needs Disabled and Marginalized Perspectives, Fitting the description: historical and sociotechnical elements of facial recognition and anti-black surveillance, and Constructing Situated and Social Knowledge: Ethical, Sociological, and Phenomenological Factors in Technological Design. Damien is a member of the Project Advisory Committee for the Center for Democracy and Technology’s Project on Disability Rights and Algorithmic Fairness, Bias, and Discrimination, and the Disability Inclusion Fund’s Tech & Disability Stream Advisory Committee. Henning Schulzrinne is Professor in the Department of Computer Science at Colombia University. He is the co-author of Mobility Protocols and Handover Optimization: Design, Evaluation and Application, Bridging communications and the physical world and Future internets escape the simulator. He was nominated as Internet Hall of Fame Innovator in 2013. He was Chief Technology Officer for the FCC under the Obama Administration. This program is produced by Doug Becker, Ankine Aghassian, Maria Armoudian, Anna Lapin and Sudd Dongre. Politics and Activism, Science / Technology, Computers and Internet, Racism 

You love building web apps with Python, and HTMX got you excited about the hypermedia approach -- let the server drive the HTML, skip the JavaScript build step, keep things simple. But then you hit that last 10%: You need Alpine.js for interactivity, your state gets out of sync, and suddenly you're juggling two unrelated libraries that weren't designed to work together. What if there was a single 11-kilobyte framework that gave you everything HTMX and Alpine do, and more, with real-time updates, multiplayer collaboration out of the box, and performance so fast you're actually bottlenecked by the monitor's refresh rate? That's Datastar. On this episode, I sit down with its creator Delaney Gillilan, core maintainer Ben Croker, and Datastar convert Chris May to explore how this backend-driven, server-sent-events-first framework is changing the way full-stack developers think about the modern web. Episode sponsors Sentry Error Monitoring, Code talkpython26 Command Book Talk Python Courses Links from the show Guests Delaney Gillilan: linkedin.com Ben Croker: x.com Chris May: everydaysuperpowers.dev Datastar: data-star.dev HTMX: htmx.org AlpineJS: alpinejs.dev Core Attribute Tour: data-star.dev data-star.dev/examples: data-star.dev github.com/starfederation/datastar-python: github.com VSCode: marketplace.visualstudio.com OpenVSX: open-vsx.org PyCharm/Intellij plugin: plugins.jetbrains.com data-star.dev/datastar_pro: data-star.dev gg: discord.gg HTML-ivating your Django web app's experience with HTMX, AlpineJS, and streaming HTML - Chris May: www.youtube.com Senior Engineer tries Vibe Coding: www.youtube.com 1 Billion Checkboxes: checkboxes.andersmurphy.com Game of life example: example.andersmurphy.com Watch this episode on YouTube: youtube.com Episode #537 deep-dive: talkpython.fm/537 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Rayid Ghani, a professor at Carnegie Mellon University, who was the chief scientist of the Obama 2012 campaign, talks with Host Llewellyn King and Co-host Adam Clayton Powell III about AI literacy, machine learning, and the potential for AI to improve various sectors such as healthcare, education, and employment. He also addresses concerns about job losses due to AI, the role of AI in politics and democracy, and the challenges of detecting fake images and videos. 

Shahar Goldboim is the Founder and CEO of Boom, an AI-enabled property management platform for short-term rental portfolios built by operators. Shahar is an entrepreneur and builder, and he launched Boom with his two sibblings after identifying real-world operational pain points inside a large South Florida property management company as it scaled. Under his leadership, Boom delivers comprehensive software that simplifies workflows, increases revenue, and reduces costs for property managers.(02:15) - Why Short-Term Rentals Are the Hardest Asset Class(02:44) - Fragmentation and the Review-Driven Revenue Trap(04:13) - The Spark: A Miami Airbnb Experiment(05:30) - From Airbnb Host to Property Manager(07:31) - Software Fragmentation in STR Ops(07:55) - From SaaS to Baas (Business-as-Software)(09:09) - Boom, the AI PMS(12:47) - Enabling Proactive Ops(13:51) - The $12M+ Fundraise(14:47) - Winning Investors with Hospitality & Tech Credibility(16:53) - Feature: Blueprint Vegas 2026(17:46) - STR Market Context and the Vacasa Lesson(21:03) - Replacing Point Solutions(23:47) - ROI, AI Moats, Future of STR Ops(32:06) - Collaboration Superpower: Tony Robbins (Wiki)

In this episode, Peter Swartz, Co-Founder and Chief Science Officer at Altana, reveals how the company's AI-powered supply chain knowledge graph has helped stop hundreds of millions of dollars in forced labor goods from crossing borders and contributed to some of the largest counter-narcotics seizures in investigators' careers. Peter shares the real-world impact Altana is making across both the public and private sectors.Peter breaks down how Altana's multi-tier supply chain visibility works to trace forced labor cotton through global networks, how dual-use chemicals are being diverted into fentanyl production, and how the platform helps governments and enterprises collaborate to avoid billions of dollars in trade disruptions while saving hundreds of millions in tariff fees.Key Topics Covered- How Altana blocked hundreds of millions of dollars in forced labor goods at U.S. borders- The role of AI knowledge graphs in mapping multi-tier global supply chains- How Altana supports CBP enforcement of the Uyghur Forced Labor Prevention Act- Product passports and how they expedite legitimate goods through customs- The difference between forced labor entering legit supply chains vs. legit goods entering illicit ones- How logistics companies use Altana to prevent their networks from being misused- Proactive vs. reactive approaches to supply chain risk using probabilistic AI models- Scenario modeling for geopolitical disruptions including Taiwan and global conflicts- Saving billions in supply chain disruptions and hundreds of millions in tariff feesEpisode Timestamps00:00 - Introduction and overview of Altana's real-world impact00:41 - Understanding forced labor as a multi-tier supply chain problem03:09 - Hundreds of millions in forced labor goods stopped at borders03:45 - How the AI knowledge graph maps global supply chain connections04:15 - Working with CBP on the Uyghur Forced Labor Prevention Act04:35 - Product passports and expediting goods through customs04:51 - Counter-narcotics and the dual-use chemical problem05:45 - Helping logistics companies stop network misuse06:27 - From alert to action and the system handoff process06:49 - Responsible AI and the role of human-in-the-loop decisions07:33 - Proactive vs. reactive supply chain intelligence08:08 - Scenario modeling for geopolitical disruptions and resiliencyAbout Peter SwartzPeter Swartz is Co-Founder and Chief Science Officer at Altana. He has spoken on global trade, supply chains, and machine learning at the World Trade Organization, the World Customs Organization, the U.S. Court of International Trade, and the National Academies of Medicine. Previously, Peter was Head of Data Science at Panjiva, listed as one of Fast Company's most innovative data science companies in 2018 and later acquired by S&P Global. He holds patents in machine learning and global trade, and completed his education at Yale, MIT, and EPFL.About AltanaAltana is the world's first Value Chain Management System, providing AI-powered supply chain intelligence to governments, enterprises, and logistics providers. The platform is built on a proprietary knowledge graph comprising more than 2.8 billion shipments, tracking over 500 million companies and 850 million facilities globally. Altana covers more than 50% of global trade, making it the most comprehensive and accurate supply chain map available.Resources Mentioned- Altana Atlas platform and AI knowledge graph- U.S. Customs and Border Protection (CBP)- Uyghur Forced Labor Prevention Act (UFLPA)- Product passports for cross-border compliance- Altana's disruption and tariff scenario modeling toolsPeter's Socials:LinkedIn — https://www.linkedin.com/in/pgswartz/Partner LinksBook Enterprise Training — https://www.upscaile.com/

In episode 31 of Recsperts, I sit down with Elisabeth Lex, Full Professor of Human-Computer Interfaces and Inclusive Technologies at Graz University of Technology and a leading researcher at the intersection of recommender systems, psychology, and human–computer interaction. Together, we explore how recommender systems can become truly human-centric by integrating cognitive, emotional, and personality-aware models into their design.Elisabeth begins by addressing a common reductionism in the field: treating users primarily as data points rather than as humans with goals, emotions, memories, and cognitive boundaries. We revisit the origins of psychology-informed recommendation, including the Grundy system -the first recommender system, built nearly 50 years ago - which framed book recommendation through stereotype modeling. From there, we discuss how the community's focus shifted toward solving recommendation mainly as an algorithmic optimization problem, often sidelining richer models of human decision-making.We then map out the three major branches of psychology-informed RecSys - cognition-inspired, affect-aware, and personality-aware - and dive into practical examples. Elisabeth walks us through her work on modeling music re-listening behavior using cognitive architectures such as ACT-R (Adaptive Control of Thought–Rational) and shows how cognitive constructs like memory decay, attention, and familiarity can meaningfully augment standard approaches like collaborative filtering. We also explore how hybrid systems that combine cognitive models with collaborative filtering can yield not just higher accuracy but also more novelty, diversity, and clearer explanations.Our conversation also turns to user-centric evaluation. Elisabeth argues that accuracy metrics alone cannot tell us whether a system is genuinely helpful. Instead, we must measure attitudes, perceptions, motivations, and emotional responses - while carefully accounting for cognitive biases, UI effects, and users' lived experiences.Towards the end, Elisabeth discusses emerging research directions such as hybrid AI (symbolic + sub-symbolic methods), the role of LLMs and agents, the risks of replacing human studies with automated evaluations, and the responsibility our community has to understand users beyond their clicks.Enjoy this enriching episode of RECSPERTS – Recommender Systems Experts.Don't forget to follow the podcast and please leave a review.(00:00) - Introduction (03:15) - About Elisabeth Lex (07:55) - Grundy, the first Recommender System (09:03) - Bridging the Gap between Psychology and Modern RecSys (17:21) - On how and when Elisabeth became a Researcher (21:39) - Survey on Psychology-Informed RecSys (39:29) - Personality-Aware Recommendation (49:43) - Affect- and Emotion-Aware Recommendation (01:01:37) - Cognition-Inspired Recommendation and the ACT-R Framework (01:14:39) - Combining Collaborative Filtering and ACT-R for Explainability (01:21:26) - Human-Centered Design (01:26:15) - Further Challenges and Closing Remarks Links from the Episode:Elisabeth Lex on LinkedInWebsite of ElisabethAI for Society LabFirst International Workshop on Recommender Systems for Sustainability and Social Good | co-located with RecSys 2024Second International Workshop on Recommender Systems for Sustainability and Social Good | co-located with RecSys 2025HyPer Workshop: Hybrid AI for Human-Centric PersonalizationTutorial on Psychology-Informed RecSysACT-R: Adaptive Control of Thought-RationalPOPROX: Platform for OPen Recommendation and Online eXperimentationPapers:Elaine Rich (1979): User Modeling via StereotypesLex et al. (2021): Psychology-informed Recommender SystemsReiter-Haas et al. (2021): Predicting Music Relistening Behavior Using the ACT-R FrameworkMoscati et al. (2023): Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music RecommendationTran et al. (2024): Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session RecommendationGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website

Wie funktioniert datengetriebene Marktforschung, wenn es keinen fertigen Datensatz gibt? In dieser Folge spricht Jonas Rashedi mit Kaba Barsch – Head of Data-Driven Solutions bei Statista Plus – über die Rolle von Data Science, AI-Integration, scrapingbasierte Recherche und kontextsensitives Prompt Engineering. Kaba erklärt anhand realer Projekte, wie ihr Team aus Text-Chaos qualitativ hochwertige Datensätze erstellt, wie LLMs systematisch verifiziert und angereichert werden – und wie durch die "Statista SynthiePop" (synthetische Bevölkerung) ganze Marktprognosen modellierbar werden. Dabei wird klar: AI ist kein Selbstläufer – ohne menschliches Marktverständnis und Kontextmodellierung läuft nichts. Eine Folge über operative Exzellenz, AI-Learnings aus der Praxis und den Unterschied zwischen Technikhype und echter Innovation. MY DATA IS BETTER THAN YOURS ist ein Projekt von BETTER THAN YOURS, der Marke für richtig gute Podcasts. Du möchtest gezielt Werbung im Podcast MY DATA IS BETTER THAN YOURS schalten? Zum Kontaktformular: https://2frg6t.share-eu1.hsforms.com/2ugV0DR-wTX-mVZrX6BWtxg Zum Linkedin Profil von Kaba: https://www.linkedin.com/in/kaba-barsch-2181631a7/ Zur Homepage von Statista: https://de.statista.com/ Zu allen Links rund um Jonas: https://linktr.ee/jonas.rashedi

For most data scientists, the idea of impacting the world through your work seems impossible. You may be developing technically brilliant solutions within your organisation, but seeing them become industry standards or influence global decisions feels completely out of reach.In this episode, Prof. Steve Stern joins Dr Genevieve Hayes to share how he transformed a mathematical critique of a cricket scoring system into becoming the custodian of the globally adopted Duckworth-Lewis-Stern method - all from an office in Canberra, Australia.This episode reveals:How a single email response changed everything [05:24]Why principles build trust where mathematics can't [13:19]The "error whack-a-mole" problem that destroys credibility [16:00]The real secret to creating work with impact [30:29]Guest BioProf. Steve Stern is a Professor of Data Science at Bond University, and is the official custodian of the Duckworth-Lewis-Stern (DLS) cricket scoring system.LinksContact Steve at Bond UniversityConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Vaishali shares her experience leading global data teams, partnering with executive leadership, and building strategies that connect cutting-edge technology to real business value. We explore her insights on operationalizing AI, scaling analytics across enterprises, and overcoming challenges in data governance, stakeholder alignment, and innovation adoption.Key Highlights:Bridging Tech and Business: How Vaishali connects AI and analytics innovations to organizational strategy and measurable outcomes.Global Team Leadership: Lessons from managing cross-functional, geographically distributed teams and driving collaboration.Operational Optimization: Examples of initiatives that reduced operational complexity while improving efficiency.Scaling Analytics and AI: Best practices for governance, workflow, and embedding AI into enterprise decision-making.Emerging Trends: Vaishali's perspective on the next wave of AI, analytics, and enterprise data strategies.Tune in to Episode 61 to learn how Vaishali Lambe drives data-driven transformation, operational excellence, and AI innovation across global enterprises.Be sure to mark your calendars for the 10th annual ALD NYC on May 13, where we will focus on GENAI AND INTELLIGENT AGENTS IN THE FINANCE AND BANKING. Join us to hear from experts on how AI is shaping the future of the enterprise. https://www.datascience.salon/new-york/

Today on the Driven by Data Dilemmas Show, host Catherine Dowden-King is joined by Irina Mihai, Director of Data Foundations, Decision & Data Science function, adidas. In this episode, they discuss three key stories surrounding an AI Adoption or Anarchy. They discuss the how behind adoption, the wins organisations are seeing in real-time, and the final story offers an alternative perspective on the mass layoff fears.Irina offers her candid views on how to tame anarchy and lays out some of her plans for adidas' AI adoption.Remember, you can send your own dilemmas to community@orbitiongroup.com, and Catherine will gladly read them to our expert guests to answer and provide their own insight on.Driven by Data Dilemmas is the spin-off show from the Driven by Data Podcast! Catherine Dowden-King is joined by some of our best-loved senior leaders in the data, analytics and AI space to ask them to share their experiences, advice and thoughts on data dilemmas.

What does inclusion actually look like inside one of the world's biggest payments organisations?In this episode of FinTech's DEI Discussions, Nadia sits down with Konstantina Kapetanidi, VP, Global Data Solutions & Head of Data Science, Europe (VCA) at Visa, to unpack what it really means to build belonging at scale.From her journey through financial services to leading data science in payments, Konstantina shares why:Talent is everywhere. Opportunity is not.Inclusion isn't an agenda item — it's BAU.And listening with intention is one of the most underrated leadership skills in FinTech today.They explore psychological safety, sponsorship, legacy, purpose-driven leadership, and how data leaders can create real human impact across billions of people.FinTech's DEI Discussions is powered by Harrington Starr, global leaders in Financial Technology Recruitment. For more episodes or recruitment advice, please visit our website www.harringtonstarr.com

Et si l'on pouvait sentir l'odeur des phénomènes physiques ? Détecter une anomalie avant qu'elle ne devienne critique, anticiper l'emballement d'une batterie ou suivre précisément les étapes de fermentation ?Dans ce nouvel épisode du Lab, Émilie Bialic, Data Scientist Senior chez Capgemini, nous explique comment le nez électronique, un dispositif inspiré de notre odorat et amplifié par l'IA, parvient à décoder l'empreinte olfactive des phénomènes physiques. Les applications sont nombreuses et prometteuses dans la santé, l'industrie ou encore l'agroalimentaire. À la croisée de la physique, de la modélisation mathématique et de l'intelligence artificielle, cette technologie apprend à reconnaître des signatures olfactives complexes pour révéler ce que le monde réel émet sans que nous ne puissions le percevoir.

Guest Full Name: Dr. R. Stacy Lindborg, PhDGuest Title: President, Chief Executive Officer, and Board DirectorCompany: IMUNONTicker: IMNNWebsite: https://imunon.com/Guest Bio:Stacy R. Lindborg, PhD, was appointed President and Chief Executive Officer of IMUNON in May 2024. Dr. Lindborg has served on IMUNON's Board of Directors since June 2021. She has nearly 30 years of experience in the pharmaceutical and biotech industries, with a particular focus on R&D, regulatory affairs, executive management, and strategy development. She has designed, hired, and led global teams, guiding long-term visions for growth through analytics and stimulating innovative development platforms to increase productivity.Prior to joining IMUNON, Dr. Lindborg was Executive Vice President and Co-Chief Executive Officer at BrainStorm Cell Therapeutics, where she remains a member of the company's Board of Directors. At BrainStorm, she was accountable for creating and executing clinical development strategies through registration and launch and progressed its novel cell therapy for ALS through a positive Phase 3 Special Protocol Assessment (SPA) study with the U.S. Food and Drug Administration. She frequently interacted with investors and analysts, represented the company in the scientific community and with the media, and played an active role in discussions with potential business partners.Dr. Lindborg previously was Vice President and Head of Global Analytics and Data Sciences, responsible for R&D and marketed products at Biogen. She began her biopharmaceutical career at Eli Lilly and Company, where, over the course of 16 years, she assumed positions of increasing responsibility, including Head of R&D strategy.Dr. Lindborg received an MA and PhD in statistics, and a BA in psychology and math from Baylor University. She has authored more than 200 presentations and 90 manuscripts that have been published in peer-reviewed journals, including 20 first-authored. She has held numerous positions within the International Biometric Society and American Statistical Association and was elected Fellow in 2008.Company Bio:IMUNON is a clinical-stage biotechnology company focused on advancing a portfolio of innovative treatments that harness the body's natural mechanisms to generate safe, effective, and durable responses across a broad array of diseases. IMUNON is developing its non-viral DNA technology across its modalities. The first modality, TheraPlas®, is developed for the gene-based delivery of cytokines and other therapeutic proteins in the treatment of solid tumors where an immunological approach is deemed promising. The second modality, PlaCCine®, is developed for the gene delivery of viral antigens that can elicit a strong immunological response.IMUNON's lead clinical program, IMNN-001, is a DNA-based immunotherapy for the localized treatment of advanced ovarian cancer. IMNN-001 is the first therapy to achieve a clinically effective response in advanced (stage IIIC/IV) ovarian cancer including benefits in both progression-free survival (PFS) and overall survival (OS) in a first-line treatment setting when used with standard of care chemotherapy. IMUNON has completed multiple clinical trials evaluating the potential of IMNN-001, including one Phase 2 clinical trial (OVATION 2), and is currently conducting a Phase 3 clinical trial (OVATION 3). The first patient was dosed in the Phase 3 study in the third quarter of 2025. IMNN-001 works by instructing the body to produce safe and durable levels of powerful cancer-fighting molecules, such as IL-12 and interferon gamma, at the tumor site. Additionally, the Company has completed dosing in a first-in-human study of its COVID-19 booster vaccine (IMNN-101).

Check out host Bidemi Ologunde's new show: The Work Ethic Podcast, available on Spotify and Apple Podcasts.Email: bidemiologunde@gmail.comIn this episode, host Bidemi Ologunde sits down with Damilola "Dammy" Gbenro, a Data Analytics and Machine Learning professional and talked about what it really means to build and maintain an online presence in the age of algorithms and AI. How do you utilize the benefits of social media without letting it consume you? What does online safety look like when your life is also your brand? And as AI reshapes trust, attention, and creativity, how do we protect our identities and our peace?Quick question: when you buy something handmade, do you ever wonder who made it, and where your money really goes? Lembrih is building a marketplace where you can shop Black and African-owned brands and learn the story behind the craft. And the impact is built in: buyers can support vendors directly, and Lembrih also gives back through African-led charities, including $1 per purchase. They're crowdfunding on Kickstarter now. Back Lembrih at lembrih.com, or search “Lembrih” on Kickstarter.Support the show

Tiffany Yeh, MD is the CEO and Co-Founder of Eztia Materials, a climate-tech venture developing energy-efficient cooling materials to protect people from extreme heat. With a mission to advance hard tech solutions at the climate-health nexus, Tiffany draws on her unique background as a physician, engineer, and public health advocate to build technologies that improve global health in a warming world.(01:13) - Dr. Ye's Background & Inspiration (01:52) - The Heat Challenge(05:20) - Singapore and the Power of Cooling(06:32) - Why Construction Has Been Slow to Adapt (07:22) - The Human Factor(08:14) - HydroVolt Technology(09:29) - Business Model, Distribution & Competition(11:19) - Worker Comfort (15:32) - Hidden Productivity Crisis Brewing(18:18) - Feature: Blueprint: The Future of Real Estate 2026 in Vegas on Sep. 22-24 (19:21) - The Secret Sauce Behind HydroVolt (20:31) - Prototyping & Real-World Applications (21:32) - Measuring Impact & ROI (23:34) - Pitching to VCs & Investors(25:31) - Product Roadmap(29:08) - Collaboration Superpower: Lionel Messi

Adatépítész -az első magyar datapodcast Minden ami hír, érdekesség, esemény vagy tudásmorzsa az  adat, datascience, adatbányászat és hasonló kockaságok világából. Become a Patron! Eurostat Anthropic elemzés

Feb 10, 2026 – This year marks a turning point, as deepfakes reach new heights in realism and influence. FS Insider interviews Dr. Siwei Lyu, director of the Institute for AI and Data Sciences, about the rapid evolution and growing dangers of deepfakes...

You've built your FastAPI app, it's running great locally, and now you want to share it with the world. But then reality hits -- containers, load balancers, HTTPS certificates, cloud consoles with 200 options. What if deploying was just one command? That's exactly what Sebastian Ramirez and the FastAPI Cloud team are building. On this episode, I sit down with Sebastian, Patrick Arminio, Savannah Ostrowski, and Jonathan Ehwald to go inside FastAPI Cloud, explore what it means to build a "Pythonic" cloud, and dig into how this commercial venture is actually making FastAPI the open-source project stronger than ever. Episode sponsors Command Book Python in Production Talk Python Courses Links from the show Guests Sebastián Ramírez: github.com Savannah Ostrowski: github.com Patrick Arminio: github.com Jonathan Ehwald: github.com FastAPI labs: fastapilabs.com quickstart: fastapicloud.com an episode on diskcache: talkpython.fm Fastar: github.com FastAPI: The Documentary: www.youtube.com Tailwind CSS Situation: adams-morning-walk.transistor.fm FastAPI Job Meme: fastapi.meme Migrate an Existing Project: fastapicloud.com Join the waitlist: fastapicloud.com Talk Python CLI Talk Python CLI Announcement: talkpython.fm Talk Python CLI GitHub: github.com Command Book Download Command Book: commandbookapp.com Announcement post: mkennedy.codes Watch this episode on YouTube: youtube.com Episode #536 deep-dive: talkpython.fm/536 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Topics covered in this episode: Command Book App uvx.sh: Install Python tools without uv or Python Ending 15 years of subprocess polling monty: A minimal, secure Python interpreter written in Rust for use by AI Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: Command Book App New app from Michael Command Book App is a native macOS app for developers, data scientists, AI enthusiasts and more. This is a tool I've been using lately to help build Talk Python, Python Bytes, Talk Python Training, and many more applications. It's a bit like advanced terminal commands or complex shell aliases, but hosted outside of your terminal. This leaves the terminal there for interactive commands, exploration, short actions. Command Book manages commands like "tail this log while I'm developing the app", "Run the dev web server with true auto-reload", and even "Run MongoDB in Docker with exactly the settings I need" I'd love it if you gave it a look, shared it with your team, and send me feedback. Has a free version and paid version. Build with Swift and Swift UI Check it out at https://commandbookapp.com Brian #2: uvx.sh: Install Python tools without uv or Python Tim Hopper Michael #3: Ending 15 years of subprocess polling by Giampaolo Rodola The standard library's subprocess module has relied on a busy-loop polling approach since the timeout parameter was added to Popen.wait() in Python 3.3, around 15 years ago The problem with busy-polling CPU wake-ups: even with exponential backoff (starting at 0.1ms, capping at 40ms), the system constantly wakes up to check process status, wasting CPU cycles and draining batteries. Latency: there's always a gap between when a process actually terminates and when you detect it. Scalability: monitoring many processes simultaneously magnifies all of the above. + L1/L2 CPU cache invalidations It's interesting to note that waiting via poll() (or kqueue()) puts the process into the exact same sleeping state as a plain time.sleep() call. From the kernel's perspective, both are interruptible sleeps. Here is the merged PR for this change. Brian #4: monty: A minimal, secure Python interpreter written in Rust for use by AI Samuel Colvin and others at Pydantic Still experimental “Monty avoids the cost, latency, complexity and general faff of using a full container based sandbox for running LLM generated code. “ “Instead, it lets you safely run Python code written by an LLM embedded in your agent, with startup times measured in single digit microseconds not hundreds of milliseconds.” Extras Brian: Expertise is the art of ignoring - Kevin Renskers You don't need to master the language. You need to master your slice. Learning everything up front is wasted effort. Experience changes what you pay attention to. I hate fish - Rands (Michael Lopp) Really about productivity systems And a nice process for dealing with email Michael: Talk Python now has a CLI New essay: It's not vibe coding - Agentic engineering GitHub is having a day Python 3.14.3 and 3.13.12 are available Wall Street just lost $285 billion because of 13 markdown files Joke: Silence, current side project!

Dr. Ben Zweig joins the podcast from NYU's Stern School of Business to discuss what is wrong with the world of work and how to fix it. Ben is the author of the book “Job Architecture: Building a Language for Workforce Intelligence,” professor of Economics, and CEO of Revelio Labs. In this conversation, Ben discusses the challenges created when a new employee finds out after working a few months that the job that was described to them is different than what they are doing. Ben says this lack of clarity results in pendulum swings between rapid job expansions and mass layoffs. He also discusses how work can be better designed to be a source of dignity and purpose. Ben believes that management is about job reconfiguration in order to keep employees relevant so those employees are able to meet current and future needs at their organizations. Ben also shares his opinion on whether or not work - in an augmented world of robots and AI - should be saved. The interview finishes with a conversation about the future of work, how artificial intelligence will augment every job, and the likelihood AI and robots will be taxed in order to generate revenue to pay for universal basic income. Dr. Ben Zweig is the CEO of Revelio Labs, a workforce intelligence company that leverages the latest advances in AI research to create a universal HR database from public sources. Ben teaches courses on Data Science and The Future of Work at NYU Stern. His first book is “Job Architecture: Building a Language for Workforce Intelligence.”

Regulierung als Chance? Hersteller vernetzter Geräte müssen durch den EU Data Act Dateninventare bereitstellen – ein Aufwand, der erstmal Frust erzeugt. Sven Johann und Stefan Negele sprechen im INNOQ Podcast darüber, warum Schwierigkeiten beim Erstellen solcher Inventare oft auf grundlegende Probleme im eigenen Datenhaushalt hinweisen. Es geht um moderne Datenarchitekturen, Data Contracts, Data Literacy und die Frage, wie Unternehmen die Regulierung als Anstoß nutzen können, ihre Datenorganisation grundsätzlich zu verbessern.

Breaking into Cybersecurity with Shadya MaldonadoIn this episode of Breaking into Cybersecurity, Shadya Maldonado, Founder and CEO of ArcQubit, shares her journey and extensive experience in the field. With 16 years in security operations, technology modernization, and risk management, Shadya discusses her transition from a military analyst to a leader in cybersecurity and AI. She highlights her work with organizations such as CISA, DARPA, DOE, and NASA, as well as her passion for developing tools to make quantum computing accessible. Shadya also offers valuable advice for individuals looking to grow their careers in cybersecurity.00:00 Introduction and Guest Welcome01:16 Shaday's Unconventional Path to Cybersecurity01:48 From Military to Cybersecurity02:50 Exposure to Data Science and Cybersecurity03:43 Immersion in Cybersecurity and SANS Conference04:45 Founding Arc Qubit and Quantum Computing06:49 Developing Quantum-Ready Talent14:02 The Importance of Cybersecurity Knowledge21:06 Shaday's Leadership Journey24:24 Advice for Aspiring Cybersecurity Professionals26:09 Closing RemarksSponsored by CPF Coaching LLC - http://cpf-coaching.comThe Breaking into Cybersecurity: It's a conversation about what they did before, why they pivoted into cyber, what the process was they went through, how they keep up, and advice/tips/tricks along the way.The Breaking into Cybersecurity Leadership Series is an additional series focused on cybersecurity leadership and hearing directly from different leaders in cybersecurity (high and low) on what it takes to be a successful leader. We focus on the skills and competencies associated with cybersecurity leadership, as well as tips/tricks/advice from cybersecurity leaders.Check out our books:The Cybersecurity Advantage - https://leanpub.com/the-cybersecurity-advantageDevelop Your Cybersecurity Career Path: How to Break into Cybersecurity at Any Level https://amzn.to/3443AUIHack the Cybersecurity Interview: Navigate Cybersecurity Interviews with Confidence, from Entry-level to Expert roleshttps://www.amazon.com/Hack-Cybersecurity-Interview-Interviews-Entry-level/dp/1835461298/Hacker Inc.: Mindset For Your Careerhttps://www.amazon.com/Hacker-Inc-Mindset-Your-Career/dp/B0DKTK1R93/About the hosts:Renee Small is the CEO of Cyber Human Capital, one of the leading human resources business partners in the field of cybersecurity, and author of the Amazon #1 best-selling book, Magnetic Hiring: Your Company's Secret Weapon to Attracting Top Cyber Security Talent. She is committed to helping leaders close the cybersecurity talent gap by hiring from within and encouraging more people to enter the lucrative cybersecurity profession. https://www.linkedin.com/in/reneebrownsmall/Download a free copy of her book at magnetichiring.com/bookChristophe Foulon focuses on helping secure people and processes, drawing on a solid understanding of the technologies involved. He has over ten years of experience as an Information Security Manager and Cybersecurity Strategist. He is passionate about customer service, process improvement, and information security. He has significant expertise in optimizing technology use while balancing its implications for people, processes, and information security, through a consultative approach.https://www.linkedin.com/in/christophefoulon/Find out more about CPF-Coaching at https://www.cpf-coaching.comWebsite: https://www.cyberhubpodcast.com/breakingintocybersecurityPodcast: https://podcasters.spotify.com/pod/show/breaking-into-cybersecuriYouTube: https://www.youtube.com/c/BreakingIntoCybersecurityLinkedin: https://www.linkedin.com/company/breaking-into-cybersecurity/

Frank Rohde is the Founder and CEO of Ownify, a fractional homeownership platform pairing institutional and impact investors with qualified first-time buyers to make homeownership more accessible. With a 20+ year career at the intersection of finance, credit analytics, and technology, Frank previously led Nomis Solutions, scaling it into a global mortgage pricing engine used by top banks. Earlier roles include leadership at FICO, founding the early online insurer eCoverage, and launching AI models before it was trendy. Born in Germany, Frank is a former national whitewater kayaking champion, marathon runner on all seven continents, and lifelong reader—now channeling that energy into building a path between renting and owning, one Brick by Brick™.(01:51) - Why Homeownership Is Broken(04:10) - Ownify model(06:03) - How Fractional Ownership Works(13:08) - Ownify Benefits for First-time Homebuyers(16:11) - Homeowner & Investor Alignment(23:21) - Feature: CREtech New York Oct. 20–21(24:09) - Event Opportunities(25:38) - All-Cash Offers Explained(32:40) - Underwriting & Risk Management(35:47) - Investor Returns(38:48) - Market Expansion(41:37) - Policy & Regulatory Headwinds(44:04) - Collaboration Superpower: Elon Musk

Topics covered in this episode: django-bolt: Faster than FastAPI, but with Django ORM, Django Admin, and Django packages pyleak More Django (three articles) Datastar Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 11am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: django-bolt : Faster than FastAPI, but with Django ORM, Django Admin, and Django packages Farhan Ali Raza High-Performance Fully Typed API Framework for Django Inspired by DRF, FastAPI, Litestar, and Robyn Django-Bolt docs Interview with Farhan on Django Chat Podcast And a walkthrough video Michael #2: pyleak Detect leaked asyncio tasks, threads, and event loop blocking with stack trace in Python. Inspired by goleak. Has patterns for Context managers decorators Checks for Unawaited asyncio tasks Threads Blocking of an asyncio loop Includes a pytest plugin so you can do @pytest.mark.no_leaks Brian #3: More Django (three articles) Migrating From Celery to Django Tasks Paul Taylor Nice intro of how easy it is to get started with Django Tasks Some notes on starting to use Django Julia Evans A handful of reasons why Django is a great choice for a web framework less magic than Rails a built-in admin nice ORM automatic migrations nice docs you can use sqlite in production built in email The definitive guide to using Django with SQLite in production I'm gonna have to study this a bit. The conclusion states one of the benefits is “reduced complexity”, but, it still seems like quite a bit to me. Michael #4: Datastar Sent to us by Forrest Lanier Lots of work by Chris May Out on Talk Python soon. Official Datastar Python SDK Datastar is a little like HTMX, but The single source of truth is your server Events can be sent from server automatically (using SSE) e.g yield SSE.patch_elements( f"""{(#HTML#)}{datetime.now().isoformat()}""" ) Why I switched from HTMX to Datastar article Extras Brian: Django Chat: Inverting the Testing Pyramid - Brian Okken Quite a fun interview PEP 686 – Make UTF-8 mode default Now with status “Final” and slated for Python 3.15 Michael: Prayson Daniel's Paper tracker Ice Cubes (open source Mastodon client for macOS) Rumdl for PyCharm, et. al cURL Gets Rid of Its Bug Bounty Program Over AI Slop Overrun Python Developers Survey 2026 Joke: Pushed to prod

Dushyanth shares his journey into AI, the challenges of building complex pipelines, and how to integrate responsible and ethical practices into machine learning workflows.Key Highlights:Scaling AI Systems: How to design and deploy pipelines that handle real-time inference, multimodal data, and production-level demands.Model Interpretability & Explainability: Strategies for making complex models understandable and accountable.Optimizing AI for Real-World Impact: Balancing performance, robustness, and human oversight in AI systems.Responsible AI Practices: Embedding ethics, fairness, and transparency in machine learning workflows.

Moltbook is a new social network built exclusively for AI agents. We, the humans, can watch of course, but it's meant for robots to essentially interact with each other. What is it and should we be concerned about it? We asked Dr. Vahid Behzadan, our AI expert from University of New Haven. Behzadan is an Associate Professor in Computer Science and Data Science. He is also the founder and director of the Secure and Assured Intelligent Learning research group, and co-founder of the state's Connecticut Artificial Intelligence Alliance.

Episode Notes James Torgerson, WXTJ co-event director and second-year Data Science student, discusses WXTJ's history, community and house shows.

En esta entrevista en Capital Radio, Laura Blanco conversa con Javier Ferraz, profesor de Operaciones, Innovación y Data Science en ESADE, sobre el impacto de la inteligencia artificial en la economía global y el liderazgo empresarial. Se discuten las inversiones millonarias en IA, el papel de gigantes tecnológicos como Apple, Microsoft y Meta, y los desafíos de suministro en tecnología. Ferraz analiza cómo la IA está transformando sectores y la importancia de que los directivos comprendan su potencial. También se aborda el papel de Google en la carrera tecnológica y el uso de datos corporativos para obtener ventajas competitivas.

Owen Barrett is the CEO and Co-Founder of Shine, a cleantech company helping multifamily property owners maximize NOI through onsite solar. With over 20 years of experience in sustainability and clean energy, Owen previously managed $60M in projects and launched a successful energy venture for schools before founding Shine to solve the split incentive problem in solar. Shine's turnkey solution targets tenant electricity—95% of a building's usage—enabling owners to generate new income while cutting tenant costs. With 36,500+ panels installed and a recent $5M seed round, Owen is leading Shine's national expansion to transform how real estate decarbonizes.(01:31) - Owen's Journey from Finance to Clean Energy(04:27) - Multifamily Solar Challenges & Solution(09:43) - Solar NOI for Multifamily(15:16) - Installation and Maintenance(17:51) - Feature: CREtech New York 2026 (19:10) - Overcoming Industry Misconceptions(20:46) - Convincing Asset Managers(23:15) - Shine's New Solar Analysis Tool(25:31) - Targeting New and Existing Buildings(26:32) - Fundraising and Growth Strategies (27:59) - Building a Remote Team(29:43) - Collaboration Superpower: Paul Sween (Dominium Board Chairman)

Anna Anisin is a seasoned entrepreneur, ecosystem builder, and business owner with deep roots in the tech world and a passion for creativity.Starting her entrepreneurial journey at 16, Anna has since achieved multiple successful exits and built a career around scaling brands, building communities, and pioneering new paths in marketing innovation.Today, Anna leads DataScience.Salon, one of the most trusted communities in AI and machine learning, and runs FormulatedBy, a boutique B2B marketing firm specializing in demand generation, experiential strategy, and AI-driven marketing. Under her leadership, FormulatedBy has served over 100 brands including AWS, IBM, Databricks, Oracle, and many of the most influential startups in AI/ML and deep tech.Most recently, Anna launched the

Brant Peterson, Vice President & Fellow at Valo Health, joins Data in Biotech to explore how his team leverages real-world data, genetic insights, and machine learning to de-risk drug discovery. From building causal DAGs to identifying patient subtypes in neurodegenerative diseases like Parkinson's, this episode dives deep into a patient-first, data-driven approach to biomedical innovation. What You'll Learn in This Episode: >> How Valo Health uses real-world evidence and EHR data to prioritize drug targets earlier in the development pipeline. >> Why integrating wet lab experiments and causal DAGs accelerates therapeutic validation. >> The importance of genetic pleiotropy and Mendelian randomization in refining disease hypotheses. >> How Valo Health identifies high-impact patient subgroups in neurodegenerative diseases like Parkinson's and Alzheimer's. >> Where machine learning models succeed and fall short, in uncovering mechanisms of disease from sparse longitudinal data. Meet Our Guest Brant Peterson is Vice President & Fellow in Data Science at Valo Health. He brings deep expertise in genetics, computational biology, and biomedical innovation. Formerly a Distinguished Data Scientist at Valo and Computational Biologist at Novartis, Brant focuses on leveraging patient-centric data to drive causal discovery in drug development. About The Host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Sponsor: CorrDyn, a data consultancyConnect with Brant Peterson on LinkedIn  Connect with Us: Follow the podcast for more insightful discussions on the latest in biotech and data science.Subscribe and leave a review if you enjoyed this episode!Connect with Ross Katz on LinkedIn Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn.

Large-scale optimization and machine learning shape modern data science, and Courtney Paquette, Ph.D., McGill University, studies how to design and analyze algorithms for large-scale optimization problems motivated by applications and data science. Paquette draws on probability, complexity theory, and convex and non-smooth optimization, and examines scaling limits of stochastic algorithms. Speaking with Saura Naderi, UC San Diego, Paquette describes an unconventional path from finance to pure mathematics and explains how persistence and comfort with uncertainty support long-term research. She highlights the challenge of building missing foundations while advancing through graduate training, and she connects that experience to the realities of doing original work. Paquette also reflects on rapid progress in machine learning and frames AI systems as tools that can be used thoughtfully. Series: "Science Like Me" [Science] [Show ID: 41119]

Large-scale optimization and machine learning shape modern data science, and Courtney Paquette, Ph.D., McGill University, studies how to design and analyze algorithms for large-scale optimization problems motivated by applications and data science. Paquette draws on probability, complexity theory, and convex and non-smooth optimization, and examines scaling limits of stochastic algorithms. Speaking with Saura Naderi, UC San Diego, Paquette describes an unconventional path from finance to pure mathematics and explains how persistence and comfort with uncertainty support long-term research. She highlights the challenge of building missing foundations while advancing through graduate training, and she connects that experience to the realities of doing original work. Paquette also reflects on rapid progress in machine learning and frames AI systems as tools that can be used thoughtfully. Series: "Science Like Me" [Science] [Show ID: 41119]

Send us a textAs loyalty leaders look toward 2026, the conversation is shifting away from feature sets and toward fundamentals: trust, relevance, and human connection. While artificial intelligence is accelerating what brands can do with data and personalization, it is also forcing a reassessment of what customers actually value in their relationships with brands. In a recent conversation with Denise Holt, Senior Vice President and Head of Strategy, Experience, Research, and Insights at Phaedon, and Emily Merkle, Senior Vice President of Analytics and Data Science, they shared their thoughts about how brands are recalibrating loyalty strategies after a year of rapid experimentation, and what that means for the future of customer engagement. Rather than viewing loyalty as a standalone program, both leaders argue the next phase of loyalty will be defined by systems that adapt to individual preferences, empower human interaction, and deliver value quickly and transparently. 

In this episode, we explore the fascinating world of recommender systems and algorithmic fairness with David Liu, Assistant Research Professor at Cornell University's Center for Data Science for Enterprise and Society. David shares insights from his research on how machine learning models can inadvertently create unfairness, particularly for minority and niche user groups, even without any malicious intent. We dive deep into his groundbreaking work on Principal Component Analysis (PCA) and collaborative filtering, examining why these fundamental techniques sometimes fail to serve all users equally. David introduces the concept of "power niche users" - highly active users with specialized interests who generate valuable data that can benefit the entire platform. We discuss his paper "When Collaborative Filtering Is Not Collaborative," which reveals how PCA can over-specialize on popular content while neglecting both niche items and even failing to properly recommend popular artists to new potential fans. David presents solutions through item-weighted PCA and thoughtful data upweighting strategies that can improve both fairness and performance simultaneously, challenging the common assumption that these goals must be in tension. The conversation spans from theoretical insights to practical applications at companies like Meta, offering a comprehensive look at the future of personalized recommendations.  

Topics covered in this episode: GreyNoise IP Check tprof: a targeting profiler TOAD is out Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 11am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: GreyNoise IP Check GreyNoise watches the internet's background radiation—the constant storm of scanners, bots, and probes hitting every IP address on Earth. Is your computer sending out bot or other bad-actor traffic? What about the myriad of devices and IoT things on your local IP? Heads up: If your IP has recently changed, it might not be you (false positive). Brian #2: tprof: a targeting profiler Adam Johnson Intro blog post: Python: introducing tprof, a targeting profiler Michael #3: TOAD is out Toad is a unified experience for AI in the terminal Front-end for AI tools such as OpenHands, Claude Code, Gemini CLI, and many more. Better TUI experience (e.g. @ for file context uses fuzzy search and dropdowns) Better prompt input (mouse, keyboard, even colored code and markdown blocks) Terminal within terminals (for TUI support) Brian #4: FastAPI adds Contribution Guidelines around AI usage Docs commit: Add contribution instructions about LLM generated code and comments and automated tools for PRs Docs section: Development - Contributing : Automated Code and AI Great inspiration and example of how to deal with this for popular open source projects “If the human effort put in a PR, e.g. writing LLM prompts, is less than the effort we would need to put to review it, please don't submit the PR.” With sections on Closing Automated and AI PRs Human Effort Denial of Service Use Tools Wisely Extras Brian: Apparently Digg is back and there's a Python Community there Why light-weight websites may one day save your life - Marijke LuttekesHome Michael: Blog posts about Talk Python AI Integrations Announcing Talk Python AI Integrations on Talk Python's Blog Blocking AI crawlers might be a bad idea on Michael's Blog Already using the compile flag for faster app startup on the containers: RUN --mount=type=cache,target=/root/.cache uv pip install --compile-bytecode --python /venv/bin/python I think it's speeding startup by about 1s / container. Biggest prompt yet? 72 pages, 11, 000 Joke: A date via From Pat Decker

Building on the web is like working with the perfect clay. It's malleable and can become almost anything. But too often, frameworks try to hide the web's best parts away from us. Today, we're looking at PyView, a project that brings the real-time power of Phoenix LiveView directly into the Python world. I'm joined by Larry Ogrodnek to dive into PyView. Episode sponsors Talk Python Courses Python in Production Links from the show Guest Larry Ogrodnek: hachyderm.io pyview.rocks: pyview.rocks Phoenix LiveView: github.com this section: pyview.rocks Core Concepts: pyview.rocks Socket and Context: pyview.rocks Event Handling: pyview.rocks LiveComponents: pyview.rocks Routing: pyview.rocks Templating: pyview.rocks HTML Templates: pyview.rocks T-String Templates: pyview.rocks File Uploads: pyview.rocks Streams: pyview.rocks Sessions & Authentication: pyview.rocks Single-File Apps: pyview.rocks starlette: starlette.dev wsproto: github.com apscheduler: github.com t-dom project: github.com Watch this episode on YouTube: youtube.com Episode #535 deep-dive: talkpython.fm/535 Episode transcripts: talkpython.fm Theme Song: Developer Rap

In this episode of the Crazy Wisdom podcast, host Stewart Alsop welcomes Roni Burd, a data and AI executive with extensive experience at Amazon and Microsoft, for a deep dive into the evolving landscape of data management and artificial intelligence in enterprise environments. Their conversation explores the longstanding challenges organizations face with knowledge management and data architecture, from the traditional bronze-silver-gold data processing pipeline to how AI agents are revolutionizing how people interact with organizational data without needing SQL or Python expertise. Burd shares insights on the economics of AI implementation at scale, the debate between one-size-fits-all models versus specialized fine-tuned solutions, and the technical constraints that prevent companies like Apple from upgrading services like Siri to modern LLM capabilities, while discussing the future of inference optimization and the hundreds-of-millions-of-dollars cost barrier that makes architectural experimentation in AI uniquely expensive compared to other industries.Timestamps00:00 Introduction to Data and AI Challenges03:08 The Evolution of Data Management05:54 Understanding Data Quality and Metadata08:57 The Role of AI in Data Cleaning11:50 Knowledge Management in Large Organizations14:55 The Future of AI and LLMs17:59 Economics of AI Implementation29:14 The Importance of LLMs for Major Tech Companies32:00 Open Source: Opportunities and Challenges35:19 The Future of AI Inference and Hardware43:24 Optimizing Inference: The Next Frontier49:23 The Commercial Viability of AI ModelsKey Insights1. Data Architecture Evolution: The industry has evolved through bronze-silver-gold data layers, where bronze is raw data, silver is cleaned/processed data, and gold is business-ready datasets. However, this creates bottlenecks as stakeholders lose access to original data during the cleaning process, making metadata and data cataloging increasingly critical for organizations.2. AI Democratizing Data Access: LLMs are breaking down technical barriers by allowing business users to query data in plain English without needing SQL, Python, or dashboarding skills. This represents a fundamental shift from requiring intermediaries to direct stakeholder access, though the full implications remain speculative.3. Economics Drive AI Architecture Decisions: Token costs and latency requirements are major factors determining AI implementation. Companies like Meta likely need their own models because paying per-token for billions of social media interactions would be economically unfeasible, driving the need for self-hosted solutions.4. One Model Won't Rule Them All: Despite initial hopes for universal models, the reality points toward specialized models for different use cases. This is driven by economics (smaller models for simple tasks), performance requirements (millisecond response times), and industry-specific needs (medical, military terminology).5. Inference is the Commercial Battleground: The majority of commercial AI value lies in inference rather than training. Current GPUs, while specialized for graphics and matrix operations, may still be too general for optimal inference performance, creating opportunities for even more specialized hardware.6. Open Source vs Open Weights Distinction: True open source in AI means access to architecture for debugging and modification, while "open weights" enables fine-tuning and customization. This distinction is crucial for enterprise adoption, as open weights provide the flexibility companies need without starting from scratch.7. Architecture Innovation Faces Expensive Testing Loops: Unlike database optimization where query plans can be easily modified, testing new AI architectures requires expensive retraining cycles costing hundreds of millions of dollars. This creates a potential innovation bottleneck, similar to aerospace industries where testing new designs is prohibitively expensive.

Generative AI is moving fast—and in pharma, it's no longer just a buzzword. In this episode of The Effective Statistician Podcast, I speak with Manuel Cossio about how Generative AI is already being applied in real-world pharma settings, where it's delivering value today, and what still needs careful consideration in regulated environments. Manuel brings a unique hybrid background, combining molecular biology, genetics, pharma experience, and deep AI engineering expertise. He works at the cutting edge of AI in clinical development, including agentic systems, human-in-the-loop approaches, and large-scale document automation. This conversation goes well beyond theory. We focus on practical use cases, real limitations, and how statisticians, programmers, and data scientists can responsibly use GenAI to become more effective.

Topics covered in this episode: Better Django management commands with django-click and django-typer PSF Lands a $1.5 million sponsorship from Anthropic How uv got so fast PyView Web Framework Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 11am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Better Django management commands with django-click and django-typer Lacy Henschel Extend Django manage.py commands for your own project, for things like data operations API integrations complex data transformations development and debugging Extending is built into Django, but it looks easier, less code, and more fun with either django-click or django-typer, two projects supported through Django Commons Michael #2: PSF Lands a $1.5 million sponsorship from Anthropic Anthropic is partnering with the Python Software Foundation in a landmark funding commitment to support both security initiatives and the PSF's core work. The funds will enable new automated tools for proactively reviewing all packages uploaded to PyPI, moving beyond the current reactive-only review process. The PSF plans to build a new dataset of known malware for capability analysis The investment will sustain programs like the Developer in Residence initiative, community grants, and infrastructure like PyPI. Brian #3: How uv got so fast Andrew Nesbitt It's not just be cause “it's written in Rust”. Recent-ish standards, PEPs 518 (2016), 517 (2017), 621 (2020), and 658 (2022) made many uv design decisions possible And uv drops many backwards compatible decisions kept by pip. Dropping functionality speeds things up. “Speed comes from elimination. Every code path you don't have is a code path you don't wait for.” Some of what uv does could be implemented in pip. Some cannot. Andrew discusses different speedups, why they could be done in Python also, or why they cannot. I read this article out of interest. But it gives me lots of ideas for tools that could be written faster just with Python by making design and support decisions that eliminate whole workflows. Michael #4: PyView Web Framework PyView brings the Phoenix LiveView paradigm to Python Recently interviewed Larry on Talk Python Build dynamic, real-time web applications using server-rendered HTML Check out the examples. See the Maps demo for some real magic How does this possibly work? See the LiveView Lifecycle. Extras Brian: Upgrade Django, has a great discussion of how to upgrade version by version and why you might want to do that instead of just jumping ahead to the latest version. And also who might want to save time by leapfrogging Also has all the versions and dates of release and end of support. The Lean TDD book 1st draft is done. Now available through both pythontest and LeanPub I set it as 80% done because of future drafts planned. I'm working through a few submitted suggestions. Not much feedback, so the 2nd pass might be fast and mostly my own modifications. It's possible. I'm re-reading it myself and already am disappointed with page 1 of the introduction. I gotta make it pop more. I'll work on that. Trying to decide how many suggestions around using AI I should include. It's not mentioned in the book yet, but I think I need to incorporate some discussion around it. Michael: Python: What's Coming in 2026 Python Bytes rewritten in Quart + async (very similar to Talk Python's journey) Added a proper MCP server at Talk Python To Me (you don't need a formal MCP framework btw) Example one: latest-episodes-mcp.png Example two: which-episodes-mcp.webp Implmented /llms.txt for Talk Python To Me (see talkpython.fm/llms.txt ) Joke: Reverse Superman

Maor Greenberg is the co-founder and CEO of Spacial, the AI-powered engineering partner delivering coordinated, permit-ready structural, MEP, and energy plans for residential construction. With over 19 years of experience as a builder and founder, Maor previously scaled Greenberg Construction, Greenberg Design Gallery, and VRchitects, earning Inc. 5000 honors and multiple design awards. At Spacial, he combines deep field experience with cutting-edge AI to reduce permitting friction and accelerate housing delivery. His work has been featured in Forbes, TechCrunch, and CTech, and he actively invests in forward-thinking AEC and AI startups.(01:33) - Maor's Journey to the US (02:54) - Challenges in Architectural & Engineering Processes(04:05) - The Pain Points Leading to Spatial AI (05:31) - Permitting Bottlenecks in Construction (06:05) - Design & Construction Integration Issues (08:24) - AI's Role in Streamlining Processes (09:29) - Success Stories & Milestones(15:07) - Shoutout: AmTrustRE's $217M Office Acquisition of 260 Madison(15:54) - Feature: Blueprint - The Future of Real Estate - Register for 2026 (17:02) - Standardized Pricing & Adoption (18:55) - Speed vs. Quality in Engineering (24:53) - Modular Housing (28:25) - Future Vision for Spatial AI (29:09) - Collaboration Superpower: Elon Musk

Your cloud SSD is sitting there, bored, and it would like a job. Today we're putting it to work with DiskCache, a simple, practical cache built on SQLite that can speed things up without spinning up Redis or extra services. Once you start to see what it can do, a universe of possibilities opens up. We're joined by Vincent Warmerdam to dive into DiskCache. Episode sponsors Talk Python Courses Python in Production Links from the show diskcache docs: grantjenks.com LLM Building Blocks for Python course: training.talkpython.fm JSONDisk: grantjenks.com Git Code Archaeology Charts: koaning.github.io Talk Python Cache Admin UI: blobs.talkpython.fm Litestream SQLite streaming: litestream.io Plash hosting: pla.sh Watch this episode on YouTube: youtube.com Episode #534 deep-dive: talkpython.fm/534 Episode transcripts: talkpython.fm Theme Song: Developer Rap

Host Sima Vasa welcomes Carol Sue Haney, Head of Research and Data Science, Engineering at Qualtrics, to discuss the transformative role of AI and data science in the market research industry.  Carol Sue explains Qualtrics’ early bet on generative AI and the development of proprietary LLMs, moving into agentic work and synthetic sampling, which she predicts will rival non-probability human sampling for quick-turn research. She emphasizes the challenges CMOs face with data overload and the fundamental importance of using regression analysis to link customer experience (CX) data, including the surprising weight of marketing messages, to crucial business outcomes like renewal and revenue growth.  Key Takeaways: 00:00 Introduction.03:12 Data research careers spanned decades before computers existed.06:35 Early generative AI investment provides significant competitive advantages.09:20 Synthetic research boosts accuracy using rich, proven seed data.13:02 AI models instantly incorporate new information for continuous improvement.17:09 Regression remains essential for identifying true business drivers.20:42 Curated data and guided AI make regression faster and reliable.24:18 Financial independence through careers empowers women in critical ways.25:42 Mentorship and knowledge sharing strengthen the entire research industry. Resources Mentioned: Qualtrics | Website #Analytics #MA #Data #Strategy #Innovation #Acquisitions #MRX #Restech

Topics covered in this episode: port-killer How we made Python's packaging library 3x faster CodSpeed Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Training The Complete pytest Course Patreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky) Brian: @brianokken@fosstodon.org / @brianokken.bsky.social Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 11am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: port-killer A powerful cross-platform port management tool for developers. Monitor ports, manage Kubernetes port forwards, integrate Cloudflare Tunnels, and kill processes with one click. Features:

Today on Talk Python, the creators behind FastAPI, Flask, Django, Quart, and Litestar get practical about running apps based on their framework in production. Deployment patterns, async gotchas, servers, scaling, and the stuff you only learn at 2 a.m. when the pager goes off. For Django, we have Carlton Gibson and Jeff Triplet. For Flask, we have David Lord and Phil Jones, and on team Litestar we have Janek Nouvertné and Cody Fincher, and finally Sebastián Ramírez from FastAPI is here. Let's jump in. Episode sponsors Talk Python Courses Python in Production Links from the show Carlton Gibson - Django: github.com Sebastian Ramirez - FastAPI: github.com David Lord - Flask: davidism.com Phil Jones - Flask and Quartz(async): pgjones.dev Yanik Nouvertne - LiteStar: github.com Cody Fincher - LiteStar: github.com Jeff Triplett - Django: jefftriplett.com Django: www.djangoproject.com Flask: flask.palletsprojects.com Quart: quart.palletsprojects.com Litestar: litestar.dev FastAPI: fastapi.tiangolo.com Coolify: coolify.io ASGI: asgi.readthedocs.io WSGI (PEP 3333): peps.python.org Granian: github.com Hypercorn: github.com uvicorn: uvicorn.dev Gunicorn: gunicorn.org Hypercorn: hypercorn.readthedocs.io Daphne: github.com Nginx: nginx.org Docker: www.docker.com Kubernetes: kubernetes.io PostgreSQL: www.postgresql.org SQLite: www.sqlite.org Celery: docs.celeryq.dev SQLAlchemy: www.sqlalchemy.org Django REST framework: www.django-rest-framework.org Jinja: jinja.palletsprojects.com Click: click.palletsprojects.com HTMX: htmx.org Server-Sent Events (SSE): developer.mozilla.org WebSockets (RFC 6455): www.rfc-editor.org HTTP/2 (RFC 9113): www.rfc-editor.org HTTP/3 (RFC 9114): www.rfc-editor.org uv: docs.astral.sh Amazon Web Services (AWS): aws.amazon.com Microsoft Azure: azure.microsoft.com Google Cloud Run: cloud.google.com Amazon ECS: aws.amazon.com AlloyDB for PostgreSQL: cloud.google.com Fly.io: fly.io Render: render.com Cloudflare: www.cloudflare.com Fastly: www.fastly.com Watch this episode on YouTube: youtube.com Episode #533 deep-dive: talkpython.fm/533 Episode transcripts: talkpython.fm Theme Song: Developer Rap