Jack Fitzsimons | Evil Models: Hiding Malware in Neural Networks

Keith O'Rourke | The Logic of Statistics Dr. Keith O'Rourke talks about the logical reasoning behind statistical modeling. Topics include mathematical vs scientific reasoning, whether science has become too stats focused, and vice versa. Watch it on... Youtube: https://youtu.be/FqE4ROHBKpY Podbean: https://dataandsciencepodcast.podbean.com/e/keith-o-rourke-the-logic-of-statistics/   Topic List: 0:00 - The logic of statistics 0:30 - What is scientific statistics? 5:15 - The logic of statistics and CS Pierce 9:15 - Role of representation in statistics: explicit vs implicit 14:13 - Diagrammatic Reasoning 18:45 - Why is modeling counterfactual? 19:33 - How can statisticians become better scientists? 28:40 - Science is hard 31:24 - Computational approaches to learning 42:00 - Learning through metaphor 46:28 - Diagrammatic representations vs math 48:40 - Is science too statistics-focussed?  59:35 - Is statistics sufficiently science-focussed?  1:08:40 - Scientific Debate   #statistics #datascience #science 

Jack Fitzsimons | Evil Models: Hiding Malware in Neural Networks Did you know that it's possible to hide malware in neural networks? Actually, you can hide malware in many statistical models. This is the subject of two recently-published papers (aptly titled "EvilModel" & "EvilModel 2.0"). Dr. Jack Fitzsimons makes it easy to understand how this is done, using techniques that began long before computers.     Watch or listen on...  Youtube: https://youtu.be/QBnk8ogL8Nk Podbean: https://dataandsciencepodcast.podbean.com/e/jack-fitzsimons-evil-models-hiding-malware-in-neural-networks/

Scott Cunningham | Causal Inference (The Mixtape) Scott Cunningham (Baylor University) discusses the ideas of his book "Causal Inference: The Mixtape". Topics include trusting inference in the absence of counterfactuals and the challenges of apply scientific methods to social phenomena.  Watch it on... YouTube: https://youtu.be/yNaCudDVTkY Podbean: https://dataandsciencepodcast.podbean.com/e/scott-cunningham-causal-inference-the-mixtape/ 0:00 - COMING UP... 0:35 - What makes it into the mixed tape? 7:10 - Coding to learn 11:15 - More people are expected to work with data & code 12:50 - Design vs program vs estimators 20:40 - Causation with zero correlation 27:00 - Optimization make everything endogenous 28:45 - The hospital example 29:30 - Credible scientific discovery vs motivated discovery 39:55 - Different meanings of causality 43:30 - The impossible counterfactual  47:00 Counterfactual nihilism 49:20 Social experiments / Defund the police 53:35 - Skepticism about the science of social phenomena 1:05:20 - The Italian crime example 1:16:30 - Scientific debate  

Eric Daza | Important Ideas in Causal Inference YouTube: https://youtu.be/K5nsSMJVIT0 Andrew Gelman and Aki Vehtari wrote a paper titled, "What are the most important statistical ideas of the past 50 years?". The first idea in the list is "counterfactual causal inference". Eric Daza (Evidation Health) walks us through the main ideas of the Gelman & Vehtari paper, drawing examples from several fields, including medical & healthcare statistics.  Topics 0:00 - Coming up...Correlation vs Causation 1:20 - Most important statistical ideas over the last 50 years 6:10 - Counterfactual Causal Inference 9:40 - Assumptions Change between Applied Domains 21:10 - Propensity Score Methods 25:15 - Transportability of Scientific Results  26:30 - People don't want generalizable results 32:00 - Generic Computation Algorithms 37:00 - Reweighting 43:57 - Matching Methods 58:20 - Medical Data is Higher Dimensional that we think. 1:00:15 - Is a Trial Population Representative?  1:10:35 - Causal Models in the Future 1:18:45 - Apostates Welcome 1:21:45 - Scientific Debate    

Wenting and Weidong discuss how the statistical challenges in the biopharm industry have proliferated with the unique demands of biotech and related life science industries.

Ruda Zhang | Gaussian Process Subspace Regression Ruda Zhang (Duke University) walks us through "Gaussian Process Subspace Regression for Model Reduction" by Zhang, Mak, and Dunson. To keep the topic interesting for both the early career & advanced audience we recap key points at a high level so that no one gets lost.   This episode involves a presentation, so you may prefer to watch the YouTube version here: https://youtu.be/IPtqUUG4XcY   Ruda's website: https://ruda.city/ The paper: https://arxiv.org/abs/2107.04668

Ruda Zhang | Math-Science Duality Watch it on... Youtube: https://youtu.be/GoDwen-RGZg Podbean: https://dataandsciencepodcast.podbean.com/e/ruda-zhang-math-science-duality/ Statistics is thought to reside at the interface of science and mathematics. Ruda Zhang (Duke University) discusses the friction at this interface and the role that both mathematical formalism & observational/data-driven intuition play in scientific discovery. A great topic for anyone interested in statistics' role in scientific discovery. #datascience #ai #science #mathematics Topic List 00:00 COMING UP... 2:44 Ruda Zhang's compendium of cool ideas + a Gaussian process PSA 7:08 Is intuition undervalued in scientific research? 10:16 Mathematics vs observational science. Rigor vs intuition. 14:07 Intuition & discovery precedes mathematical rigor 21:58 Mathematics vs empirical science & the complexity of induction 30:24 Abstract thinking & the cost/benefit of discovery 37:25 The efficient frontier / Pareto Front of knowledge 42:55 Pragmatism and competence 50:24 Math /science dualism 1:15:52 AI making scientific discoveries 1:19:15 Statistical & scientific debate

Simon Mak | Integrating Science into Stats Models #statistics #science #ai It’s a common dictum that statisticians need to incorporate domain knowledge into their modeling and the interpretation of their results. But how deeply can scientific principles be embedded into statistical models? Prof. Simon Mak (Duke University) is pushing this idea to the limit by integrating fundamental physics, physiology, and biology into both the models and model inference. This includes Simon’s joint work with Profs. David Dunson and Ruda Zhang (also of Duke University). Scientific reasoning AND stats. What more could we ask for? Enjoy! Watch it on.... YouTube: https://youtu.be/bUbZO7R4z40 Podbean: https://dataandsciencepodcast.podbean.com/e/simon-mak-integrating-science-into-stats-models/   00:00 - COMING UP….Scientists & Statisticians 02:09 - Introduction - Integrating scientific knowledge into AI/ML 06:08 - How much domain knowledge is sufficient? 09:15 - Choosing which prior knowledge to integrate into a model 14:49 - Black box & gray box optimization 19:50 - Non-physics examples of integrating scientific theory into ML models 22:45 - Scientific principles & modeling at different scales 27:20 - Correlation is one just way of modeling linkage 36:37 - Conditional independence & different-fidelity experiments 39:40 - Innovation vs incorporation of known information in the model 42:52 - Aortic stenosis example 52:49 - Which mathematics can be used to represent scientific knowledge 57:09 - How to acquire scientific domain knowledge 1:02:45 - Complementary approaches to integrating science 1:06:48 - Gaussian process & integrating priors over functions 1:12:48 - A topic for statisticians and scientists to debate:science-based vs data-based learning. Simon Mak's Webpage: https://sites.google.com/view/simonmak/home  

Martin Goodson | Practical Data Science & The UK's AI Roadmap #ai #datascience #startups Martin Goodson (Evolution AI) describes the key aspects of the UK's AI Roadmap & responses to the document by members of the Royal Statistical Society. In particular, Martin describes the disconnect between the priorities of AI startups and industry practitioners on one side, and government and academia on the other. Martin also outlines which skills early career data scientists should focus on while in school versus after entering the workforce. Also available on.... YouTube: https://youtu.be/T9qRl6Hclhg   Topic List 0:00 COMING UP: Scientific culture & AI 1:25 The UK AI Roadmap 8:44 Who is a data science “practitioner”?  12:53 Data science in AI startups 20:36 Is there a disconnect between practitioners & academia? 25:09 Key skills for new data science graduates 32:03 Coding & production level data science 39:30 Learning the right data analysis skills at the course-level.  45:32 AI leadership 58:40 AI from academia & OpenSource initiatives 1:05:37 Large institutions' impact on the AI field 1:08:24 Back to the UK AI roadmap   1:12:16 Building an AI community  1:13:15 AI in our lifetime: Moonshots & realistic goals 1:14:31 Scientific debate

Dr. Jack Fitzsimons (Oblivious AI) gives a high-level introduction to the technologies that can either exploit or protect your data privacy. If you'd like to survey the landscape of data privacy-preserving technologies (from someone who's building the tech) this is a good place to start! #datascience #privacy #ai   0:00 - Coming up... 3:24 - Introduction 6:20 - Data privacy and privacy enhancing technologies   13:00 - History of privacy enhancing technologies 19:54 - Differential privacy: Hiding the influence of a single data point 22:52 - Trading data utility for data privacy 38:32 - Tracking algorithms and how they decide user preferences 42:04 - Preserving privacy: Anonymizing data & VPNs 50:17 - Exploration vs Exploitation: Combining best of multiple domains to tackle problems 54:13 - Federated learning, input and output privacy of data 58:45 - Balancing data privacy vs data-driven personalization 1:05:50 - What should data scientists/statisticians debate?