Assistant Professor @PrincetonCS
Research: Theoretical Computer Science, Optimization, Algorithmic Statistics.
Machine Learning Scientist @ ETH Zurich, Active Learning, Sequence Design, GenAI
Assistant professor of biostatistics at the University of Copenhagen
https://pmorzywolek.github.io
Unofficial bot by @vele.bsky.social w/ http://github.com/so-okada/bXiv https://arxiv.org/list/math.PR/new
List https://bsky.app/profile/vele.bsky.social/lists/3lim7ccweqo2j
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Advancing the frontiers of basic science through grantmaking, research and public engagement. Sign up for our newsletter: simonsfoundation.org/newsletter
TCS+ is the original online seminar in theoretical computer science, committed to the carbon-free dissemination of ideas across the globe since 2013. Talks from the cutting edge of research in TCS, for a wide audience: https://www.tcsplus.org
The world's leading venue for collaborative research in theoretical computer science. Follow us at http://YouTube.com/SimonsInstitute.
Interests on bsky: ML research, applied math, and general mathematical and engineering miscellany. Also: Uncertainty, symmetry in ML, reliable deployment; applications in LLMs, computational chemistry/physics, and healthcare.
https://shubhendu-trivedi.org
Professor of Computer Science at Cambridge.
Theoretical computer scientist working on quantum algorithms and complexity at Google Quantum AI. Previously at Microsoft Quantum, MIT, U. Waterloo, and IIT Bombay.
The Gene Ontology (GO, geneontology.org) knowledgebase is the world’s largest source of information on gene function. Our mission is to develop a comprehensive, computational model of biological systems, ranging from the molecular to organism level.
Ph.D. Student in Computer Architecture at Carnegie Mellon University (she/her)
Assistant Professor at NYU Courant Institute School | Postdoc at MIT | PhD at CMU | Theoretical Computer Science | Quantum Information
statistician • causal inference, machine learning, nonparametrics
professor @harvardmed.bsky.social
alexluedtke.com
Research Scientist GoogleDeepMind. Working on large-scale pretraining at Gemini.
https://phlippe.github.io/
PhD in causal machine learning @amlab.bsky.social
Research Engineer @MSFTResearch | Prev - UG CSE IIT Ropar'21 | Large scale ML
https://miguelhernan.org/
Using health data to learn what works.
Making #causalinference less casual.
Director, @causalab.bsky.social
Professor, @hsph.harvard.edu
Methods Editor, Annals of Internal Medicine @annalsofim.bsky.social
Lecturer in Maths & Stats at Bristol. Interested in probabilistic + numerical computation, statistical modelling + inference. (he / him).
Homepage: https://sites.google.com/view/sp-monte-carlo
Seminar: https://sites.google.com/view/monte-carlo-semina
Physics professor at San Jose State. Quantum Foundations. Big fan of space and time, and also many things therein.
CS PhD student @UArkansas EECS interested in causality, generative modeling, and applications in trustworthy AI
Website: akomand.github.io
LinkedIn: https://www.linkedin.com/in/akomand
Using computers to read the entrails of modernity (statistics, optimization, machine learning).
Currently: Stats PhD @Harvard
Previously: CS/Soc @Stanford, Stat/ML @Oxford
https://njw.fish
Assistant Professor at the University of Toronto
⚒️ 🏥 Deep learning and causal inference for computational medicine
Professor @RPI
Statistical inference, ML, Info theory
web: https://www.isg-rpi.com
Postdoc @ UCLA StarAI Lab, PhD in CS from Oxford. Probabilistic ML, Tractable Models, Causality
TMLR Homepage: https://jmlr.org/tmlr/
TMLR Infinite Conference: https://tmlr.infinite-conf.org/
PhD student @uwnlp.bsky.social
PhD student in Machine Learning and Causality @ University of Warwick | Enrichment student @ The Alan Turing Institute | These days thinking about Causal Abstractions, Optimal Transport, Information Theory, and Emergence.
Website: yfelekis.github.io
International Conference on Learning Representations https://iclr.cc/
Association for Uncertainty in AI.
Upcoming conference: #uai2026 August 17-21 in Amsterdam, Netherlands! 🇳🇱
https://auai.org/uai2026
Postdoc @quantumlah @NUSingapore
Previously: Postdoc @NUSComputing
https://sites.google.com/view/sayantans
Mathematician at UCLA. My primary social media account is https://mathstodon.xyz/@tao . I also have a blog at https://terrytao.wordpress.com/ and a home page at https://www.math.ucla.edu/~tao/
Assistant Professor of Physics at IIT Mandi and Science Writer. Writing a pop sci book about black hole information loss paradox.
Quantum information, useless information, generally informed.
Quantum algorithms researcher at phasecraft.io
https://ievacepaite.com/
Illuminating math and science. Supported by the Simons Foundation. 2022 Pulitzer Prize in Explanatory Reporting. www.quantamagazine.org
ML Research @ Apple.
Understanding deep learning (generalization, calibration, diffusion, etc).
preetum.nakkiran.org
Professor of Applied Physics at Stanford | Venture Partner a16z | Research in AI, Neuroscience, Physics
Machine learning researcher. Professor in ML department at CMU.
Machine Learning Professor
https://cims.nyu.edu/~andrewgw
San Diego Dec 2-7, 25 and Mexico City Nov 30-Dec 5, 25. Comments to this account are not monitored. Please send feedback to townhall@neurips.cc.
Machine learning lab at Columbia University. Probabilistic modeling and approximate inference, embeddings, Bayesian deep learning, and recommendation systems.
🔗 https://www.cs.columbia.edu/~blei/
🔗 https://github.com/blei-lab
Postdoc at CMU, previously PhD at RPI. Causality, representation learning, misc.
PhD Student Machine Learning & Causality with Bernhard Schölkopf at Max Planck Institute for Intelligent Systems // Prev.: Applied Science at Zalando, MSc Maths at Oxford, BSc Physics at Heidelberg
arminkekic.com
🏳️🌈👨👨👧👦 interested in causal inference, experimentation, optimization, RL, statML, econML, fairness
Cornell & Netflix
www.nathankallus.com
https://github.com/PySpur-Dev/PySpur
PhD Student at UCL // LLMs
PhD student working on understanding why neural nets generalize @MPI Tübingen | ex-Vector | path2phd.substack.com | 🇭🇺 🇪🇺
Ph.D. Candidate @uwstat; Research fellowship @Netflix — machine learning; semiparametrics; causal inference, reinforcement learning.
https://larsvanderlaan.github.io
CMU postdoc, previously MIT PhD. Causality, pragmatism, representation learning, and AI for biology / science more broadly. Proud rat dad.
Interested in all things causal modeling. Ongoing projects on causal analyses of discrimination and on causation in dynamical systems.
Machine Learning PhD student @UCL. Interested in Causality and AI Safety.
yuchen-zhu.github.io
PhD student working on causal inference and ML @ TU Delft
rickardkarlsson.com
PhD student at UC Berkeley
@Penn Prof, deep learning, brains, #causality, rigor, http://neuromatch.io, Transdisciplinary optimist, Dad, Loves outdoors, 🦖 , c4r.io
Bren Professor of Computational Biology @Caltech.edu. Blog at http://liorpachter.wordpress.com. Posts represent my views, not my employer's. #methodsmatter
Distinguished Scientist at Google. Computational Imaging, Machine Learning, and Vision. Posts are personal opinions. May change or disappear over time.
http://milanfar.org
AI research at Broad Institute and Boston University.
Reinforcement Learning / Bandits / Experiment Design
Mexicano 🇲🇽
Assistant Prof at ISTA - Causal Learning and AI Lab
Mathematician, writer, Cornell professor. All cards on the table, face up, all the time. www.stevenstrogatz.com
Asst. Prof. in Machine Learning at UofT. #LongCOVID patient.
https://www.cs.toronto.edu/~cmaddis/
Associate Professor of Machine Learning, University of Oxford;
OATML Group Leader;
Director of Research at the UK government's AI Safety Institute (formerly UK Taskforce on Frontier AI)
Professor at the University of Sydney. Quantum computing enthusiast.
Views expressed here are my own.
Complexity, in all its forms.
Associate Professor of Computer Science at Columbia University.
http://www.henryyuen.net
Caltech theoretical physicist
Computer science, math, machine learning, (differential) privacy
Researcher at Google DeepMind
Kiwi🇳🇿 in California🇺🇸
http://stein.ke/
Statistics and ML Theory
https://ankitp.net/
professor of EECS at MIT, currently visiting IAS. working in theoretical computer science namely algorithm design, complexity theory, circuit complexity, etc.
i'll let you know when P != NP is proved (and when it's not)
Assistant Professor at the University of Michigan.
I design fast graph algorithms in dynamic/distributed/local settings.
https://sites.google.com/site/thsaranurak/
🤖
new arXiv preprints mentioning "differential privacy" or "differentially private" in the title/abstract
- unrelated quantum/FL papers
+ updates from https://differentialprivacy.org
[Under construction.]
Assistant Professor @Dept. Of Computer Science, University of Copenhagen, Ex Postdoc @MPI-IS, ETHZ, PhD @University of Oxford, B.Tech @CSE,IITK.
Professor in Scalable Trustworthy AI @ University of Tübingen | Advisor at Parameter Lab & ResearchTrend.AI
https://seongjoonoh.com | https://scalabletrustworthyai.github.io/ | https://researchtrend.ai/
machine learning and artificial intelligence | University of Chicago / Google
causal ml; ai+society; social media, comp social science. having fun.. my opinions. he/him. http://hci.social/@emrek
Professor at Penn, Amazon Scholar at AWS. Interested in machine learning, uncertainty quantification, game theory, privacy, fairness, and most of the intersections therein
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
Assistant professor (of mathematics) at the University of Toronto. Algebraic geometry, number theory, forever distracted and confused, etc. He/him.
Stanford Linguistics and Computer Science. Director, Stanford AI Lab. Founder of @stanfordnlp.bsky.social . #NLP https://nlp.stanford.edu/~manning/
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
Professor and Head of Machine Learning Department at Carnegie Mellon. Board member OpenAI. Chief Technical Advisor Gray Swan AI. Chief Expert Bosch Research.
Full prof at Saarland University & part of the Amsterdam Machine Learning Lab at the University of Amsterdam | ELLIS scholar | #causality #causalML anything #causal |
🇮🇹🇸🇮 in 🇩🇪🇳🇱
#UAI2026 general chair
https://saramagliacane.github.io/
Machine learning researcher, working on causal inference and healthcare applications
Senior Lecturer #USydCompSci at the University of Sydney. Postdocs IBM Research and Stanford; PhD at Columbia. Converts ☕ into puns: sometimes theorems. He/him.
AI @ OpenAI, Tesla, Stanford
Assistant Professor at UC Berkeley and UCSF.
Machine Learning and AI for Healthcare. https://alaalab.berkeley.edu/
Research at Google DeepMind. Ex-Physicist. Controllable World Simulators (GNNs, Structured World Models, Neural Assets). TLM Veo Capabilities (Ingredients & more).
📍 San Francisco, CA
Assistant Prof. of CS at Johns Hopkins
Visiting Scientist at Abridge AI
Causality & Machine Learning in Healthcare
Prev: PhD at MIT, Postdoc at CMU
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.
Foundations of AI. I like simple and minimal examples and creative ideas. I also like thinking about the next token 🧮🧸
Google | PhD, CMU |
https://arxiv.org/abs/2504.15266 | https://arxiv.org/abs/2403.06963
vaishnavh.github.io
