Data & Science with Glen Wright Colopy podofasclepius

 Technology

The Pod of Asclepius is a healthcare technology podcast for the technical crowd.
No fluff, no sales pitches, just important health tech ideas (described well!) to help everyone keep learning and becoming more of an expert in the field.
Our guests are top researchers (from academia and industry), entrepreneurs, and regulatory experts. They will talk about cool technology, from data science to engineering, but also share insights on practical concerns of bridging the gap between technical innovation and a clinical solution.

 video
Jingyi Jessica Li  Statistical Hypothesis Testing vs Machine Learning Binary Classification
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 highimpact 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 – Intro
1:50 – Motivation for Jingyi's article
3:22 – Jingyi's four concepts under hypothesis testing and binary
classification
8:15 – Restatement of concepts
12:25 – Emulating methods from other publications
13:10 – Classification vs hypothesis test: features vs instances
21:55  Single vs multiple instances
23:55  Correlations vs causation
24:30  Jingyi’s Second and Third Guidelines
30:35  Jingyi’s Fourth Guideline
36:15  Jingyi’s Fifth Guideline
39:15 – Logistic regression: An inference method & a classification method
42:15 – Utility for students
44:25 – Navigating the multiple comparisons problem (again!)
51:25 – Right side, show bioarxiv paper 
 video
Gualtiero Piccinini  What Are FirstPerson Data?  Philosophy of Data Science
Gualtiero Piccinini  What Are FirstPerson Data?
Firstperson 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 firstperson methods & data?
2:26 Firstperson methods vs firstperson data?
7:10 Are firstperson data legitimate at all?
11:50 Phenomenology
13:26 Firstperson data is extracted from human behavior
18:25 Skepticism & arguments against firstperson data
25:40 Psychophysics, introspectionists, behavioralists, cognitivists, and the origins of firstperson data
35:20 Using new instruments & methods in science
46:00 Is this where the philosophers roam?
#datascience #statistics #science 
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David Dunson  Advancing Statistical Science  Philosophy of Data Science
David Dunson  Advancing Statistical Science  Philosophy of Data Science Series
A fundamental question in the philosophy of science is "what does it mean to make scientific progress?" We will have a series of episodes centered around this question for statistics and data science. In our first episode in the series, David Dunson (Duke University) discusses important advances in Bayesian analysis, big data, uncertainty, and scientific discovery.
Topic Timestamps
0:00 Intro to David Dunson
1:54 What does it mean to advance data science and statistics?
6:14 Industry & Optimization, Science & Uncertainty
8:14 Prediction & Discovery / Bayesian Modeling
14:13 What is “complex” data?
22:49 Big Data, Bayes, and Nonparametrics
33:50 Ad hoc approaches vs principled methods
37:08 Should Machine Learning Publications Refocus on Scientific Discovery?
39:50 Mathematically principled data science & statistics
51:40 Do Bayesians just use priors as regularizers?
55:16 Bayesian Priors and Tuning Inference Methods
1:00:00 Prioritize the Most Important Work in Data Science
1:07:07 Good Practices of Star Grad Students
1:13:17 The Science in Statistical *Science*
#datascience #science #statistics 
 video
Martin Kuldorff  Spatiotemporal Models of Disease Outbreaks
Note: This conversation was recorded June 25, 2021.
Martin Kuldorff  Spatiotemporal Models of Outbreaks
Martin Kuldorff (Harvard Medical School) talks about the integration of biological & demographic information (and general reality) in the spatiotemporal models used to detect disease outbreaks. He also discusses how these methods can be applied to noninfectious diseases like cancer.
0:00  Spatiotemporal modeling of outbreaks
6:02  Important features of spatiotemporal outbreak models
12:20  Which diseases wouldn't you track for modeling?
19:02  Multiple comparison adjustments of alarms
25:15  Domain knowledge of outbreak features
29:30 Competing hazards & risks
34:30 Comparing hemispheres
37:00  Bridging the gap for infectious diseases to cancer
45:10  Retrospective data correction / changing monitoring
57:00  Competing risks & statistics
1:01:30  Deducing risks & affects through knowledge of immunological mechanisms
1:09:00  Future scientific convos
#datascience #science 
 video
Jason Costello  Data Science vs Software, Academia vs Industry
Interested in Data Science? Learn Data Science and Statistics from experts as they cover key topics in the field. The Data & Science podcast focusses on teaching data scientists how to think critically in order to solve data analysis problems across various scientific domains.
Jason Costello  Data Science vs Software, Academia vs Industry Jason Costello (Hypervector) describes his (nontrivial) transition from academic research into big tech and then the healthcare industry. He outlines a strategy to find the cool research problems that you get in academia while still delivering value to your company. We then talk about the interface of data science / machine learning and software.
0:00 Deploying Data Science into the Real World
8:24 Transitioning from Academic to Industrial Data Science
16:56 First step to delivering value to industry
21:38 Toy example of high value data science
25:28 Deep technical challenges are real and useful too!
29:59 Formalized logic in machine learning solutions
32:54 Data Science & Machine Learning Projects can fail.
38:50 Getting to the cool data science projects
47:21 Putting Machine Learning Models into Software
56:21 Software and Deduction, Machine Learning and Induction
1:06:06 Is Software A Deductive Complex System?

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Eric Daza  Nof1 Science & Causal Inference  Philosophy of Data Science
Interesting in Data Science? Learn Data Science and Statistics from experts as they cover key topics in the field. The Data & Science podcast focusses on teaching data scientists how to think critically in order to solve data analysis problems across various scientific domains.
Eric Daza  Nof1 Science & Causal Inference  Philosophy of Data Science
Much of our scientific inference revolves around the identification and replication of patterns in data. So what can be done when N=1? Eric Daza gives us a statistician's perspective on the ideas behind Nof1 studies, its best examples, and strongest critiques.
0:00  The purpose of Nof1 & generalizability
3:30  Successes and challenges in Nof1
9:30  A lightbulb moment
18:00 – Anomalies, Compliance, & Recurring Patterns
23:00 – Best Critiques of Nof1, Safety, Efficacy
41:20  Causal Inference
54:30 – Increasing the number of data scientists
1:03:30 – Biostatistics’ changing place in data science / statistical thinking