classical is anything that predates the start of my PhD and the older I get then the more classical it was
31.10.2025 16:11 — 👍 3 🔁 0 💬 0 📌 0
New paper with @calebhmiles.bsky.social on density ratio learning!
21.10.2025 12:15 — 👍 7 🔁 1 💬 0 📌 0
when the information matrix is singular then should it be called the 'bagel variance' (ie a sandwich with a hole)?
20.10.2025 18:34 — 👍 2 🔁 0 💬 0 📌 0
Tbh I never understood midterm exams in the US. Why not just two weeks of exams at the end of the academic year? I always felt that I only really understood the content when revising on my own during the spring break 🤷🏼♂️
11.10.2025 18:08 — 👍 0 🔁 0 💬 0 📌 0
This week I started a new job as a postdoc at Columbia University! Excited to be back doing research and exploring NYC even with all the political madness that is going on
03.04.2025 22:13 — 👍 10 🔁 0 💬 0 📌 0
source: https://arxiv.org/rss/stat.ML
maintainer: @tmaehara.bsky.social
Principal Researcher in AI/ML/RL Theory @ Microsoft Research NE/NYC. Previously @ MIT, Cornell. http://dylanfoster.net
RL Theory Lecture Notes: https://arxiv.org/abs/2312.16730
Paul Zivich, Assistant (to the Regional) Professor
Computational epidemiologist, causal inference researcher, amateur mycologist, and open-source enthusiast.
https://github.com/pzivich
#epidemiology #statistics #python #episky #causalsky
Computery guy. HCI, ML, AI, privacy, security, fairness, political economy of tech. He/him. London/Oxford. Football alt at nofoolingreu.bsky.social
Post-doc at NYU Grossman School of Medicine (this account is solely in my personal capacity, all views are my own etc). Non-parametric statistics, causal inference, Bayesian methods. Herbsusmann.com
Ph.D Candidate, Stanford GSB
Econometrics, Causal Inference, Machine Learning
https://davidritzwoller.github.io/
Assistant Professor of Biostatistics at Columbia.
I study causal inference, graphical models, machine learning, algorithmic (un)fairness, social + environmental determinants of health, etc. Opinions my own.
http://www.dmalinsky.com
PhD student of applied statistics and causal inference
Senior Data Analyst at Columbia University Epidemiology // Incoming Biostatistics PhD student at UC Berkeley
Statistics & health policy person
PhD Student @ LMU Munich
Munich Center for Machine Learning (MCML)
Math Assoc. Prof. (On leave, Aix-Marseille, France)
Teaching Project (non-profit): https://highcolle.com/
Associate professor at Columbia University
Epidemiology, causal inference, addiction medicine
https://kararudolph.github.io/
Assistant professor of biostatistics at McGill
Causal inference, high-dimensional statistics, machine learning, precision medicine, and statistical software
https://pboileau.ca/
Institute Professor, MIT Economics. Co-Director of @mitshapingwork.bsky.social. Author of Why Nations Fail, The Narrow Corridor, and Power & Progress.
Assistant professor of biostatistics at Columbia University
Casual inference, statistics, etc
Pauca sed Matura
Professor in Economics at Lund University. Theoretical economist, market designer, policy advisor, author.
Webb: https://sites.google.com/site/tommyanderssonlunduniversity/home
Epidemiologist | Implementation Scientist | Chancellor's Fellow @ University of Edinburgh
Assistant professor at Columbia University
The official account for Imperial College London
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