Subverter of the dominant paradigm
IEEE Conference on Secure and Trustworthy Machine Learning
March 2026 (Munich) • #SaTML2026
https://satml.org/
Waitress turned Congresswoman for the Bronx and Queens. Grassroots elected, small-dollar supported. A better world is possible.
ocasiocortez.com
PhD student at the University of Pennsylvania. Currently, intern at MSR. Interested in reliable and replicable reinforcement learning and using it for knowledge discovery: https://marcelhussing.github.io/
All posts are my own.
cs phd @upenn advised by Michael Kearns, Aaron Roth, and Duncan Watts| previously @stanford | she/her
https://psamathe50.github.io/sikatasengupta/
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
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/
Penn CS PhD student and IBM PhD Fellow studying strategic algorithmic interaction. Calibration, commitment, collusion, collaboration. She/her. 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 interactive algorithms for agentic alignment (e.g. imitation/RLHF). no model is an island. https://gokul.dev/.
Group Leader in Tübingen, 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/
PI at Helmholtz AI, Faculty at TU Munich, Fellow at Zuse School for reliable AI, Branco Weiss Fellow, ELLIS Scholar.
Prev: 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 interpretable 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]
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
