Data & Science with Glen Wright Colopy

Glen Wright Colopy
  • 5.0(7개의 평가)
  • 수학
  • 매주 업데이트
Data & Science with Glen Wright Colopy

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.

  1. 2022. 08. 02.

    Keith O’Rourke | The Logic of Statistics

    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

    1시간 13분

  2. 2022. 07. 26.

    Jack Fitzsimons | Evil Models: Hiding Malware in Neural Networks

    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/

    52분

  3. 2022. 07. 18.

    Scott Cunningham | Causal Inference (The Mixtape)

    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

    1시간 21분

  4. 2022. 07. 11.

    Eric Daza | Important Ideas in Causal Inference

    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

    1시간 24분

  5. 2022. 05. 10.

    Wenting Cheng & Weidong Zhang | Advances in Biotech/Biopharma

    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.

    35분

  6. 2022. 05. 10.

    Ruda Zhang | Gaussian Process Subspace Regression

    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

    1시간 9분

  7. 2022. 04. 14.

    Ruda Zhang | Math-Science Duality

    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

    1시간 23분

  8. 2022. 04. 06.

    Simon Mak | Integrating Science into Stats Models

    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

    1시간 19분
모두 보기(89개)

평가 및 리뷰

5
최고 5점
7개의 평가

소개

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.

정보

  • 제작진
    Glen Wright Colopy
  • 방송 연도
    2020년 - 2022년
  • 에피소드
    89
  • 등급
    전체 연령 사용가
  • 저작권
    © Copyright 2022 All rights reserved.
  • 웹사이트 보기
    Data & Science with Glen Wright Colopy

무삭제판 에피소드를 청취하려면 로그인하십시오.

이 프로그램의 최신 정보 받기

프로그램을 팔로우하고, 에피소드를 저장하고, 최신 소식을 받아보려면 로그인하거나 가입하십시오.
국가 또는 지역 선택

아프리카, 중동 및 인도

아시아 태평양

유럽

라틴 아메리카 및 카리브해

미국 및 캐나다