Data & Science with Glen Wright Colopy

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:...

Ruda Zhang | Math-Science Duality Watch it on... Youtube: https://youtu.be/GoDwen-RGZg Podbean: 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...

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...

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...

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 - Introduction 6:20 - Data privacy and privacy enhancing technologies 13:00 - History of privacy enhancing technologies 19:54 - Differential privacy:...

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...

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 - Intro 1:38 - Chris Holmes, Professor of Biostatistics at Oxford University 3:28 - UK Biobank & designing a valuable dataset 8:42 - Healthcare charities in the UK...

Philosophy of Data Science Series Keynote with Deborah Mayo Episode 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. -...

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)...

Charlotte Deane | Proteomics, AlphaFold 2, and Llamas #datascience #ai Charlotte Deane (Oxford University) talks about statistical approaches to proteomics, the evolution of Google Deedmind's AlphaFold 2 & its place in proteomics' deep learning landscape, humanizing antibodies, and the increasing role of software engineers in research groups. The topic of llamas, camels, and alpacas (and their unique place in proteomics research) make a surprise visit. Topics 0:00 Intro / An important...

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,...

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 consultant 4:10 Why did Janet...

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: Episode 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...

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 - Introduction 1:40 - Prioritizing topics in curricula 9:07 - Teaching with intent to test 11:22 - Statistics without computing 17:52 - What should be taught? How do we teach it? 19:07 -...

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...

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 &...

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...

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...

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...