72 episodes

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.

Data & Science with Glen Wright Colopy podofasclepius

    • Technology
    • 5.0 • 6 Ratings

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 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 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 – 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 bio-arxiv paper

    • 55 min
    • video
    Gualtiero Piccinini | What Are First-Person Data? | Philosophy of Data Science

    Gualtiero Piccinini | What Are First-Person Data? | Philosophy of Data Science

    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 Phenomenology
    13:26 First-person data is extracted from human behavior
    18:25 Skepticism & arguments against first-person data
    25:40 Psychophysics, introspectionists, behavioralists, cognitivists, and the origins of first-person data
    35:20 Using new instruments & methods in science
    46:00 Is this where the philosophers roam?


    #datascience #statistics #science

    • 51 min
    • video
    David Dunson | Advancing Statistical Science | Philosophy of Data Science

    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

    • 1 hr 17 min
    • video
    Martin Kuldorff | Spatiotemporal Models of Disease Outbreaks

    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 non-infectious diseases like cancer.


    0:00 - Spatio-temporal modeling of outbreaks
    6:02 - Important features of spatio-temporal 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

    • 1 hr 8 min
    • video
    Jason Costello | Data Science vs Software, Academia vs Industry

    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 (non-trivial) 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?


     

    • 1 hr 8 min
    • video
    Eric Daza | N-of-1 Science & Causal Inference | Philosophy of Data Science

    Eric Daza | N-of-1 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 | N-of-1 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 N-of-1 studies, its best examples, and strongest critiques.


     


    0:00 - The purpose of N-of-1 & generalizability


    3:30 - Successes and challenges in N-of-1


    9:30 - A lightbulb moment


    18:00 – Anomalies, Compliance, & Recurring Patterns


    23:00 – Best Critiques of N-of-1, 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

    • 1 hr 12 min

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