Murat Kocaoglu
Asst. Prof. at Purdue ECE. Causal ML Lab. Causal discovery, causal inference, deep generative models, info theory, online learning. Past: MIT-IBM AI Lab, UT Austin, Koc, METU.
@michaelchchoi.bsky.social
Applied probabilist. Probability, MCMC, optimization, information theory, TCS. https://mchchoi.github.io/
@elenadata.bsky.social
CS Prof @ University of Illinois Chicago. Research in causal inference, machine learning, graph mining, privacy. www.cs.uic.edu/~elena
@sebdarses.bsky.social
Math Assoc. Prof. at Aix-Marseille (France) Currently on Sabbatical at CRM-CNRS, Université de Montréal Teaching Project (non-profit): https://www.highkholle.fr/ https://sites.google.com/view/sebastien-darses/welcome
@raziehnabi.bsky.social
Rollins Assistant Professor of Biostatistics @EmoryRollins, PhD @JHU.edu Interested in causal inference, missing data, machine learning, algorithmic fairness, non/semiparametrics, graphical models, etc raziehnabi.com
@kerstingaiml.bsky.social
AI Prof at TU Darmstadt, Founding Co-Director Hessian.AI, DFKI, AAAI/EurAI/AAIA/ELLIS Fellow, AAAI24 Ass. PC CoChair, Fmr. PC CoChair UAI, ECML PKDD, Invest. @Aleph__Alpha, Fmr. AI Column German Newspaper Welt (am Sonntag)
@statmodeling.bsky.social
Automatically tweets new posts from http://statmodeling.stat.columbia.edu Please respond in the comment section of the blog. Old posts spool at https://twitter.com/StatRetro
@frejohk.bsky.social
Associate professor, Chalmers University of Technology. Machine learning for decision making & healthcare. http://healthyai.se, http://fredjo.com
@yuqirose.bsky.social
Machine Learning Prof @UCSanDiego, #Physics-Guided #AI, MIT TR-35 Innovator. Website: roseyu.com
@ziweijiang.bsky.social
Ph.D. Student at CausalML Lab, Purdue University | Causality | Machine Learning | Information Theory https://ziwei-jiang.github.io/
@alexpghayes.com
incoming postdoc @ stanford + assistant prof @ oregon state. networks, causal inference, contagion, measurement error, #rstats. he/him https://www.alexpghayes.com
@mmondelli.bsky.social
Professor at IST Austria | Machine learning, information theory | Prev: Stanford, EPFL | opinions my own
@moritzwillig.bsky.social
PhD student at AIML Lab, TU Darmstadt, Germany. Connecting AI & Causality. | Website: https://moritz-willig.de/
@osmanmian.bsky.social
www.sites.google.com/view/mian/ | Post Doc @ Institute for Artificial Intelligence in Medicine at Uniklinikum Essen | Previously Ph.D. Student. Causal Inference and Discovery @CISPA Helmholtz Center for Information Security
@ferhatay.bsky.social
Dad x 2, husband, son, brother, computational biologist, genome scientist, associate professor at la jolla institute for immunology (LJI) and UCSD https://www.lji.org/labs/ay/
@neurostats.org
AI in Bio & Health & Therapeutic Development Bio: https://linktr.ee/mnarayan Substack: https://blog.neurostats.org Peek into my brain: notes.manjarinarayan.org Previously @dynotx @StanfordMed PhD@RiceU_ECE | BS@ECEILLINOIS 🧪🧮⚕️🧬🧠🖥🤖📈✍️🩺👩📈📉
@auai.org
Association for Uncertainty in AI. Upcoming conference: #uai2025 July 21-25th in Rio de Janeiro, Brazil 🇧🇷 ! https://auai.org/uai2025
@skiandsolve.bsky.social
⛷️ ML Theorist carving equations and mountain trails | 🚴♂️ Biker, Climber, Adventurer | 🧠 Reinforcement Learning: Always seeking higher peaks, steeper walls and better policies. https://ualberta.ca/~szepesva
@a-marx.bsky.social
Prof. at TU Dortmund and RC Trust | Chair for Causality | Research on Causality, Machine Learning and Information Theory | Prev. ETH Zürich, ETH AI Center, CISPA and MPI for Informatics | Website: https://www.a-marx.com
@charlesassaad.bsky.social
Junior professor and Inserm chair | Head of the CIPHOD team | Causal Inference + Causal Discovery + Root Cause Analysis
@astrezh.bsky.social
Assistant Professor, UChicago Political Science. http://www.antonstrezhnev.com
@lucystats.bsky.social
Biostatistician • Assistant Prof @ Wake Forest University • former postdoc @ Hopkins Biostat • PhD @ Vandy Biostat • 🎙 Casual Inference • lucymcgowan.com
@tobigerstenberg.bsky.social
Tea drinking assistant professor of cognitive psychology at Stanford. https://cicl.stanford.edu
@dagophile.bsky.social
Interested in all things causal modeling. Ongoing projects on causal analyses of discrimination and on causation in dynamical systems.
@nickchk.com
Econ prof at Seattle University. Book The Effect http://theeffectbook.net out now! Substack https://nickchk.substack.com/ Twitter @nickchk
@maosbot.bsky.social
Parent, spouse, Australian, cyclist, Professor of Machine Learning in Oxford. Bayesian ML, Long Covid, photos of dog, AI must be good for humans, https://www.robots.ox.ac.uk/~mosb
@handle.invalid
ML and Statistics researcher. Interested in privacy, causal inference, optimal transport. Website: donlapark.pages.dev
@idanattias.bsky.social
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/
@suriyag.bsky.social
@dileepkalathil.bsky.social
Associate Professor, Texas A&M University (TAMU). Areas of interest: Reinforcement Learning, Machine Learning
@johandh2o.bsky.social
PhD Student at UiO, Researcher at NIPH, Fulbright Scholar, Statistician, Economist, Industrial Engineer. Web: johandh2o.github.io
@kiragoldner.bsky.social
Assistant Professor at BU CDS EconCS | Theory of CS | MD+AI+DS4SG | MD4SG co-founder Previously Columbia, UW, Oberlin. Views are mine alone. www.kiragoldner.com
@carlbergstrom.com
UW biology prof. I study how information flows in biology, science, and society. Book: *Calling Bullshit*, http://tinyurl.com/fdcuvd7b LLM course: https://thebullshitmachines.com Corvids: https://tinyurl.com/mr2n5ymk I don't like fascists. he/him
@alxndrmlk.bsky.social
"The Causal Guy" http://causalpython.io Author || Advisor || Educator Host at http://CausalBanditsPodcast.com Causal ML Tutor @ Uni of Oxford CausalSky: https://bsky.app/profile/did:plc:imz3rf35poonl7yxt7bogui4/feed/aaamrclcu3tfa
@edwardhkennedy.bsky.social
assoc prof of statistics & data science at Carnegie Mellon https://www.ehkennedy.com/ interested in causality, machine learning, nonparametrics, public policy, etc
@p-hunermund.com
Professor of Technology and Economic Policy | Co-founder of causalscience.org | Associate Editor at Journal of Causal Inference | Executive Team at Academy of Management TIM Division
@yoshuabengio.bsky.social
Full professor at UdeM, Founder and Scientific Advisor at Mila - Quebec AI Institute, A.M. Turing Award Recipient. Working towards the safe development of AI for the benefit of all. Website and blog: https://yoshuabengio.org/
@atlaswang.bsky.social
https://vita-group.github.io/ 👨🏫 UT Austin ML Professor (on leave) https://www.xtxmarkets.com/ 🏦 XTX Markets Research Director (NYC AI Lab) Superpower is trying everything 🪅 Newest focus: training next-generation super intelligence - Preview above 👶
@amritsinghbedi.bsky.social
CS Faculty at UCF (SafeRR AI Lab), previous @UMD @ARL@IITK Interested in Generative AI Alignment, RL, Optimization methods, Robotics, LLMs
@cmcaram.bsky.social
Professor at UT Austin. Research in ML & Optimization. Always rethinking how I teach. Amateur accordion player. Committed bike commuter. Online classes in English & Greek. https://caramanis.github.io/
@urish.bsky.social
Machine learning researcher, working on causal inference and healthcare applications
@guyvdb.bsky.social
🎓 CS Prof at UCLA 🧠 Researching reasoning and learning in artificial intelligence: tactable deep generative models, probabilistic circuits, probabilistic programming, neurosymbolic AI https://web.cs.ucla.edu/~guyvdb/
@mahdi.ch
Theoretical Computer Science professor @ U. of Michigan-Ann Arbor. Opinions are mine and may evolve over time. repost ≠ endorsement. Policy: I don't interact with anonymous profiles. Join AAUP. he/him/his.
@stein.ke
Computer science, math, machine learning, (differential) privacy Researcher at Google DeepMind Kiwi🇳🇿 in California🇺🇸 http://stein.ke/
@danielmalinsky.bsky.social
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