PhD student at the University of Pennsylvania. Prev, intern at MSR, and Meta FAIR. Interested in reliable and replicable reinforcement learning, robotics and knowledge discovery: https://marcelhussing.github.io/
All posts are my own.
Computer science, math, machine learning, (differential) privacy
Researcher at Google DeepMind
Kiwi🇳🇿 in California🇺🇸
http://stein.ke/
Professor at Penn, Amazon Scholar at AWS. Interested in machine learning, uncertainty quantification, game theory, privacy, fairness, and most of the intersections therein
cs phd @upenn advised by Michael Kearns, Aaron Roth, and Duncan Watts| previously @stanford | she/her
https://psamathe50.github.io/sikatasengupta/
Associate professor at U of Toronto. Computer science and math research: (differentially) private data analysis, geometry, discrepancy, optimization.
Associate Professor of Computer Science at Northeastern University in Boston. Dad. Imposter.
Senior Lecturer #USydCompSci at the University of Sydney. Postdocs IBM Research and Stanford; PhD at Columbia. Converts ☕ into puns: sometimes theorems. He/him.
Assistant prof at JHU CS. Interested in theory of ML, privacy, cryptography. All cat pictures my own and do not represent the cats of my employer
Assistant Prof of CS at the University of Waterloo, Faculty and Canada CIFAR AI Chair at the Vector Institute. Joining NYU Courant in September 2026. Co-EiC of TMLR. My group is The Salon. Privacy, robustness, machine learning.
http://www.gautamkamath.com
wharton stats phd — ml theory, ml for science
prev: comp neuro, data, physics
working with Edgar Dobriban and Konrad Körding
also some sports (esp. philly! go birds)
PhD student at University of Alberta. Interested in reinforcement learning, imitation learning, machine learning theory, and robotics
https://chanb.github.io/
CS PhD student at UPenn studying strategic human-AI interaction. On the job market! Nataliecollina.com
Machine Learning @ University of Edinburgh | AI4Science | optimization | numerics | networks | co-founder @ MiniML.ai | ftudisco.gitlab.io
Associate Professor at CS UWaterloo
Machine Learning
Lab: opallab.ca
PhD student at @cmurobotics.bsky.social working on efficient algorithms for interactive learning (e.g. imitation / RL / RLHF). no model is an island. prefers email. https://gokul.dev/. on the job market!
Professor at UT Nuremberg, Germany
I’m 🇫🇷 and I work on RL and lifelong learning. Mostly posting on ML related topics.
Organic machine turning tea into theorems ☕️
AI @ Microsoft Research ➡️ Goal: Teach models (and humans) to reason better
Let’s connect re: AI for social good, graphs & network dynamics, discrete math, logic 🧩, 🥾,🎨
Organizing for democracy.🗽
www.rlaw.me
doing a phd in RL/online learning on questions related to exploration and adaptivity
> https://antoine-moulin.github.io/
Prof at TU Nuremberg, PI at Helmholtz AI, Fellow at Zuse School for reliable AI, Branco Weiss Fellow, ELLIS Scholar.
Prev: TUM, Cambridge CBL, St John's College, ETH Zürich, Google Brain, Microsoft Research, Disney Research.
https://fortuin.github.io/
Associate professor in machine learning at the University of Amsterdam. Topics: (online) learning theory and the mathematics of explainable AI.
www.timvanerven.nl
Theory of Interpretable AI seminar: https://tverven.github.io/tiai-seminar
Information theory, probability, statistics. Churchill Professor of Mathematics of Information @UofCambridge: dpmms.cam.ac.uk/person/ik355/ 🧮 #MathSky 🧪 #Science
[used to be @yiannis_entropy at the other place]
Assistant Professor @Dept. Of Computer Science, University of Copenhagen, Ex Postdoc @MPI-IS, ETHZ, PhD @University of Oxford, B.Tech @CSE,IITK.
ML & Privacy Prof at the University of Melbourne, Australia. Deputy Dean Research. Prev Microsoft Research, Berkeley EECS PhD. @bipr on the X bird site. He/him.
Postdoc at UW CSE. Differential privacy, memorization in ML, and learning theory.
Computer science professor at Carnegie Mellon. Researcher in machine learning. Algorithmic foundations of responsible AI (e.g., privacy, uncertainty quantification), interactive learning (e.g., imitation/reinforcement learning).
https://zstevenwu.com/
Research Director, Founding Faculty, Canada CIFAR AI Chair @VectorInst.
Full Prof @UofT - Statistics and Computer Sci. (x-appt) danroy.org
I study assumption-free prediction and decision making under uncertainty, with inference emerging from optimality.
Sr Research Scientist at Google DeepMind, Toronto. Member, Mila. Adjunct, McGill CS. PhD Machine Learning & MASt Applied Math (Cambridge), BSc Math (Warwick). gkdz.org
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
full-time ML theory nerd, part-time AI-non enthusiast
Laplace Junior Chair, Machine Learning
ENS Paris. (prev ETH Zurich, Edinburgh, Oxford..)
Working on mathematical foundations/probabilistic interpretability of ML (what NNs learn🤷♂️, disentanglement🤔, king-man+woman=queen?👌…)
Postdoc researcher at IDEAL Institute in Chicago, hosted by UIC and TTIC.
My research interests are in machine learning theory, data-driven sequential decision-making, and theoretical computer science.
https://www.idanattias.com/
Professor @UCLA, Research Scientist @ByteDance | Recent work: SPIN, SPPO, DPLM 1/2, GPM, MARS | Opinions are my own
Computational Statistics and Machine Learning (CSML) Lab | PI: Massimiliano Pontil | Webpage: csml.iit.it | Active research lines: Learning theory, ML for dynamical systems, ML for science, and optimization.
Researcher @PontilGroup.bsky.social| Ph.D. Student @ellis.eu, @Polytechnique, and @UniGenova.
Interested in (deep) learning theory and others.
https://erfunmirzaei.github.io/
Post-doctoral Fellow @ Vector Institute, Toronto
Mathematician working on probability and ML at U of Guelph in Canada. I also make math videos at https://youtube.com/@MihaiNicaMath
Researcher at Google. Improving LLM factuality, RAG and multimodal alignment and evaluation. San Diego. he/him ☀️🌱🧗🏻🏐 Prev UCSD, MSR, UW, UIUC.
Stats Postdoc at Columbia, @bleilab.bsky.social
Statistical ML, Generalization, Uncertainty, Empirical Bayes
https://yulisl.github.io/
official Bluesky account (check username👆)
Bugs, feature requests, feedback: support@bsky.app
