Episoder
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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/FqE4ROHBKpYPodbean: https://dataandsciencepodcast.podbean.com/e/keith-o-rourke-the-logic-of-statistics/
Topic List:
0:00 - The logic of statistics0:30 - What is scientific statistics?5:15 - The logic of statistics and CS Pierce9:15 - Role of representation in statistics: explicit vs implicit14:13 - Diagrammatic Reasoning18:45 - Why is modeling counterfactual?19:33 - How can statisticians become better scientists?28:40 - Science is hard31:24 - Computational approaches to learning42:00 - Learning through metaphor46:28 - Diagrammatic representations vs math48:40 - Is science too statistics-focussed? 59:35 - Is statistics sufficiently science-focussed? 1:08:40 - Scientific Debate
#statistics #datascience #science
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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/QBnk8ogL8NkPodbean: https://dataandsciencepodcast.podbean.com/e/jack-fitzsimons-evil-models-hiding-malware-in-neural-networks/
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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/yNaCudDVTkYPodbean: 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 learn11:15 - More people are expected to work with data & code12:50 - Design vs program vs estimators20:40 - Causation with zero correlation27:00 - Optimization make everything endogenous28:45 - The hospital example29:30 - Credible scientific discovery vs motivated discovery39:55 - Different meanings of causality43:30 - The impossible counterfactual 47:00 Counterfactual nihilism49:20 Social experiments / Defund the police53:35 - Skepticism about the science of social phenomena1:05:20 - The Italian crime example1:16:30 - Scientific debate
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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.
Topics0:00 - Coming up...Correlation vs Causation1:20 - Most important statistical ideas over the last 50 years6:10 - Counterfactual Causal Inference9:40 - Assumptions Change between Applied Domains21:10 - Propensity Score Methods25:15 - Transportability of Scientific Results 26:30 - People don't want generalizable results32:00 - Generic Computation Algorithms37:00 - Reweighting43:57 - Matching Methods58:20 - Medical Data is Higher Dimensional that we think.1:00:15 - Is a Trial Population Representative? 1:10:35 - Causal Models in the Future1:18:45 - Apostates Welcome1:21:45 - Scientific Debate
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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
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Ruda Zhang | Math-Science Duality
Watch it on...Youtube: https://youtu.be/GoDwen-RGZgPodbean: 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 List00:00 COMING UP...2:44 Ruda Zhang's compendium of cool ideas + a Gaussian process PSA7:08 Is intuition undervalued in scientific research?10:16 Mathematics vs observational science. Rigor vs intuition.14:07 Intuition & discovery precedes mathematical rigor21:58 Mathematics vs empirical science & the complexity of induction30:24 Abstract thinking & the cost/benefit of discovery37:25 The efficient frontier / Pareto Front of knowledge42:55 Pragmatism and competence50:24 Math /science dualism1:15:52 AI making scientific discoveries1:19:15 Statistical & scientific debate
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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 & Statisticians02:09 - Introduction - Integrating scientific knowledge into AI/ML06:08 - How much domain knowledge is sufficient?09:15 - Choosing which prior knowledge to integrate into a model14:49 - Black box & gray box optimization19:50 - Non-physics examples of integrating scientific theory into ML models22:45 - Scientific principles & modeling at different scales27:20 - Correlation is one just way of modeling linkage36:37 - Conditional independence & different-fidelity experiments39:40 - Innovation vs incorporation of known information in the model42:52 - Aortic stenosis example52:49 - Which mathematics can be used to represent scientific knowledge57:09 - How to acquire scientific domain knowledge1:02:45 - Complementary approaches to integrating science1:06:48 - Gaussian process & integrating priors over functions1:12:48 - 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
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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
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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 - Introduction6:20 - Data privacy and privacy enhancing technologies 13:00 - History of privacy enhancing technologies19:54 - Differential privacy: Hiding the influence of a single data point22:52 - Trading data utility for data privacy38:32 - Tracking algorithms and how they decide user preferences42:04 - Preserving privacy: Anonymizing data & VPNs50:17 - Exploration vs Exploitation: Combining best of multiple domains to tackle problems54:13 - Federated learning, input and output privacy of data58:45 - Balancing data privacy vs data-driven personalization1:05:50 - What should data scientists/statisticians debate?
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The piranha problem (too many large, independent effect sizes influence the same outcome) has received some attention on Andrew Gelman’s blog. But now it’s a paper! Chris Tosh (Memorial Sloan Kettering) talks about multiple views of the piranha problem and detecting the implausible scientific claims that are published. The butterfly effect makes an appearance.
If you enjoyed the science-vs-pseudoscience topics, you’ll enjoy this one.
0:00 - Coming up in the episode
2:35 - What is the Piranha Problem?
19:54 - Confusing effect sizes
23:11 - The "words & walking speed" study
26:22 - Declaration of independent variables
30:58 - Piranha theorems for correlations
37:07 - Piranha theorems for linear regression
40:37 - Piranha Theorems for mutual information
44:13 - Bounds on the independence of the covariates
46:12 - Applying the piranha theorem to real data
50:12 - Applying the piranha theorem across studies
54:05 - A Bayesian detour
1:00:12 - The butterfly effect & chaos
1:04:26 - Applying the piranha theorem to cancer research
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Chris Holmes is Professor of Biostatistics at the University of Oxford and Programme Director for Health and Medical Sciences at The Alan Turing Institute. Chris’ research interests include Bayesian nonparametrics (which is the right kind of nonparametrics), statistical machine learning, genomics, and genetic epidemiology.
0:00 - Intro1:38 - Chris Holmes, Professor of Biostatistics at Oxford University3:28 - UK Biobank & designing a valuable dataset8:42 - Healthcare charities in the UK11:16 - Digital Health: prioritizing research questions19:55 - Bayes, nonparametrics, and Bayesian nonparametrics23:30 - Model prediction is at the heart of Bayesian inference28:00 - Prioritization in model building for biology33:09 - Model constraints to generate valid inference37:34 - Hypothesis driven science in statistical learning versus deep learning43:30 - Developing models in genomics & clinical informatics48:37 - Building stable, generalizable and robust models52:41 - Important questions to think about 54:05 - Causal reasoning and clinical risk prediction57:50 - What topic should the statistical community debate?
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Philosophy of Data Science Series Keynote with Deborah MayoEpisode 1: Revolutions, Reforms, and Severe Testing in Statistical Thinking
In the first keynote of the Philosophy of Data Science Series we have a 2-part interview with Deborah Mayo (Virginia Tech).In the first part of our keynote with Deborah Mayo we cover...- The role of scientific revolution and its implications for statistics and data scientist.- The necessity of statistical reforms and why philosophy will play a role.- The value of severe testing of scientific claims.
Watch it on... YouTube: https://youtu.be/S4VAEShM3BUPodbean:
You can join our mail list at: https://www.podofasclepius.com/mail-list
We're always happy to hear your feedback and ideas - just post it in the YouTube comment section to start a conversation.
Thank you for your time and support of the series!
Topics:
0:00 - Preface to First Keynote Interview2:00 - Welcome Deborah Mayo!5:05 - What is the Philosophy of Statistics?8:15 - What does philosophy add to data science?16:10 - Scientific revolution in statistics20:10 - Statistical reforms24:25 - Replication & hypothesis pre-specification31:00 - Failure is severe testing37:25 - Error statistics48:00 - Scientific progress and closing remarks
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Charlotte Deane | Bioinformatics, Deepmind's AlphaFold 2, and Llamas#datascience #ai
Charlotte Deane (Oxford University) talks about statistical approaches to bioinformatics, the evolution of Google Deepmind's AlphaFold 2 & its place in protein informatics deep learning landscape. She also describes humanizing antibodies, and the increasing role of software engineers in statistical research groups. The topic of llamas, camels, and alpacas (and their unique place in proteomics research) makes a surprise visit.
[Note: This episode was originally published in January 2022, but the file contained a buffering error, which prevented the full interview from being played. This version, published Feb 1, 2022 contains the full interview.]
Topics0:00 Intro / An important topic to debate3:50 What is a protein? Why are proteins foundational?13:32 Immunotherapies, humanizing antibodies, & creating an scientific databases16:04 Translating in silico research into immunotherapies21:03 Nanobodies, camels, alpacas, & llamas. 25:05:00 Databases and data knowledge bases33:21:00 Targeted therapies39:45:00 Statistical modeling in proteomics45:40:00 DeepMind AlphaFold's evolution55:28:00 Software engineers in academic research groups1:03:21 The adventure of science1:07:42 Oxford Blues hockey & scientific debate
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The philosophical community continuously aims to reconcile differing views on first person data and the consciousness of the mind. Is it possible to live without consciousness? Can one conceive thoughts without matching images to them? In this episode, Eric Schwitzgebel of the University of California tries to dissect such topics and questions to help us better understand the philosophical world.
Keywords: philosophy, epistemic data, first person data, stimulus error, imageless thought, consciousness
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Starting a Statistics Consultancy | Janet Wittes
The following interview was a keynote fireside chat with Janet Wittes (Statistics Collaborative, Inc.) titled "Statisticians as Entrepreneurs". It was recorded for the BBSW 2021 Conference (Nov 3 - 5 in Foster City, CA).
References:
BBSW 2021 Conference: https://www.bbsw.org/bbsw2021
Topics:
0:00 Janet's background prior to founding Statistics Collaborative, Inc.3:00 Janet's initial research interest as a consultant4:10 Why did Janet start her own business as opposed to joining a company or university. 5:45 Who were Janet's first clients?8:00 What did Janet want to instill in her company?15:50 Earning enough money to hire people18:55 Initial ratio of clients to employees22:42 Janet's company's statistical tech stack25:00 Different challenges at different stages of the company27:28 Growing a company but not taking on every possible client or project28:13 Statisticians as entrepreneurs37:00 Choosing the right people
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Jingyi Jessica Li | Advancing Statistical Genomics
Watch it on…. YouTube Podbean
Jingyi Jessica Li (UCLA) describes common statistical pitfalls in genomic data analysis & the statistical reasoning required to correct these mistakes.
Common themes throughout include:
Hypothesis-driven science & critical scientific reasoning over datap-values and non-sensical null hypotheses/distributionsthe value of appearing statistically rigorousresearchers cutting intellectual corners & digging themselves into local minimaEpisode Topics
0:00 A major advancement in genomic data leads to new statistical techniques
2:15 Hypothesis-driven science & hypothesis-free data analysis
2:55 A ChIP Seq Example
8:00 Misformulation of sampling variability
16:55 A false analogy: the permutation test
19:03 Losing my p-value religion: the value of statistical packaging
24:30 The Clipper Framework for false discovery rate control
31:50 Non-parametric developments
37:55 Inferred covariates
46:00 PseudotimeDE: inferences of differential gene expression along cell pseudotime
47:10 Selective inference
49:25 What biological/physiological data will be incorporated in the future?
52:30 Statistics, computer science, data science, ML, biology
57:05 Machine learning and prediction
1:01:30 Sophisticated models vs sophisticated research
1:07:45 Peer review in science
1:13:05 Hypothesis-driven science vs cutting intellectual corners
1:18:12 What topic should the statistics community debate?
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Mine Çetinkaya-Rundel | Advancing Open Access Data Science Education#datascience #statistics #education
Mine Çetinkaya-Rundel (Duke University) describes the current and future states of statistics and data science education. Then she discusses the process of building open access learning material.
0:00 - Introduction1:40 - Prioritizing topics in curricula9:07 - Teaching with intent to test11:22 - Statistics without computing17:52 - What should be taught? How do we teach it?19:07 - Computational thinking is valuable (to 31:45)23:47 - Self reinforcing academics / positive feedback (to 31:45)31:08 - Data science vs statistics (the computing angle)37:55 - Statistical collaboration / technical collaboration39:45 - Common language / imputation under ignorance41:12 - Are some topics better for hands on or computational learning?45:32 - Learning computation through visualization52:40 - Video cut option before she gives an example52:42 - Let them eat cake first.56:08 - What is open source education? Open source vs open access.59:36 - Advancing open source text books1:03:55 - Economics of open source1:07:55 - The open education ecosystem1:12:17 - Modularizing & parallelizing learning topics1:16:52 - Favorite dataset on OpenIntro.Org?1:18:14 - What topic should the statistics community debate?
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Jingyi Jessica Li | Statistical Hypothesis Testing versus Machine Learning Binary Classification
Jingyi Jessica Li (UCLA) discusses her paper "Statistical Hypothesis Testing versus Machine Learning Binary Classification". Jingyi noticed several high-impact cancer research papers using multiple hypothesis testing for binary classification problems. Concerned that these papers had no guarantee on their claimed false discovery rates, Jingyi wrote a perspective article about clarifying hypothesis testing and binary classification to scientists.
#datascience #science #statistics
0:00 – Intro1:50 – Motivation for Jingyi's article3:22 – Jingyi's four concepts under hypothesis testing and binaryclassification8:15 – Restatement of concepts12:25 – Emulating methods from other publications13:10 – Classification vs hypothesis test: features vs instances21:55 - Single vs multiple instances23:55 - Correlations vs causation24:30 - Jingyi’s Second and Third Guidelines30:35 - Jingyi’s Fourth Guideline36:15 - Jingyi’s Fifth Guideline39:15 – Logistic regression: An inference method & a classification method42:15 – Utility for students44:25 – Navigating the multiple comparisons problem (again!)51:25 – Right side, show bio-arxiv paper
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Gualtiero Piccinini | What Are First-Person Data?
First-person methods (and its associated data) have been scientifically and philosophically contentious. Are they pseudoscientific? Or simply pushing the bounds of scientific methodology? Obviously, I have no idea… so Prof. Gualtiero Piccinini (University of Missouri – St. Louis) provides a helpful introduction to the topic covering the key points of its history and the philosophical/scientific debate.
0:00 Why cover first-person methods & data?2:26 First-person methods vs first-person data?7:10 Are first-person data legitimate at all?11:50 Phenomenology13:26 First-person data is extracted from human behavior18:25 Skepticism & arguments against first-person data25:40 Psychophysics, introspectionists, behavioralists, cognitivists, and the origins of first-person data35:20 Using new instruments & methods in science46:00 Is this where the philosophers roam?
#datascience #statistics #science
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