Data & Science with Glen Wright Colopy Glen Wright Colopy

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 

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/

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
 

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
 
 

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