nullret

Professionally talking about stuff. Mostly AI, revenue, and growth. Also enjoying life.

Writer, reader, HuffPo contributor, resister, fierce equal rights advocate, defender of innocents, climate activist, marketing maven, social media guru, cat herder, dog lover, espresso drinker, legend in my own mind, and overall sassy broad.

Bronx boy. Cubs fan. Dad, husband, writer, podcaster and cable news host. The Sirens’ Call: How Attention Became the World’s Most Endangered Resource out now. https://sirenscallbook.com/

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

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.

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

PhD student at University of Alberta. Interested in reinforcement learning, imitation learning, machine learning theory, and robotics https://chanb.github.io/

Associate Professor at CS UWaterloo Machine Learning Lab: opallab.ca

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

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]

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., RLHF). https://zstevenwu.com/

Sr Research Scientist at Google DeepMind, Toronto. Member, Mila. Adjunct, McGill CS. PhD Machine Learning & MASt Applied Math (Cambridge), BSc Math (Warwick). gkdz.org

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?👌…)