Alec McClean
Postdoc @ NYU Grossman; stats / ML + causal inference
https://alecmcclean.github.io/
@pedrosantanna.bsky.social
Associate Professor at Emory University. Causal Inference | Difference-in-Differences | Econometrics. Dad x4
@kathoffman.bsky.social
biostatistician + biostatistics phd student at the university of washington. learning + writing about better ways to conduct research. #rstats, #dataviz, causal inference. she/her. blog: https://www.khstats.com/
@aschuler.bsky.social
Assistant Professor of Biostatistics UC Berkeley semiparametric statistics, machine learning, causal inference, stats/ML pedagogy, social justice Modern Causal Inference Book: alejandroschuler.github.io/mci/
@kallus.bsky.social
🏳️🌈👨👨👧👦 interested in causal inference, experimentation, optimization, RL, statML, econML, fairness Cornell & Netflix www.nathankallus.com
@angelamczhou.bsky.social
assistant prof at USC Data Sciences and Operations; phd Cornell ORIE. data-driven decision-making, operations research/management, causal inference, algorithmic fairness/equity angelamzhou.github.io
@idiaz.bsky.social
Statistician. Associate prof. at NYU Grossman Department of Population Health. Causal inference, machine learning, and semiparametric estimation. https://idiazst.github.io/website/
@causalhuber.bsky.social
Professor of Applied Econometrics and Policy Evaluation at the University of Fribourg/Freiburg (Switzerland) - causal analysis, statistics, econometrics, machine learning...and telemarking
@ianws.bsky.social
Postdoc @ UC Berkeley | statistics, probability, machine learning, privacy 🇨🇦 ianws.com
@alexluedtke.bsky.social
associate professor of statistics @uw • causal inference, machine learning, nonparametrics alexluedtke.com
@bonv.bsky.social
Assistant Professor in the Department of Statistics at Rutgers He/him 🏳️🌈
@larsvanderlaan3.bsky.social
Ph.D. Student @uwstat; Research fellowship @Netflix; visiting researcher @UCJointCPH; M.A. @UCBStatistics - machine learning; calibration; semiparametrics; causal inference. https://larsvanderlaan.github.io
@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
@donskerclass.bsky.social
Econometrics, Statistics, Computational Economics, etc 🏳️⚧️ http://donskerclass.github.io
@aliciacurth.bsky.social
Machine Learner by day, 🦮 Statistician at ❤️ In search of statistical intuition for modern ML & simple explanations for complex things👀 Interested in the mysteries of modern ML, causality & all of stats. Opinions my own. https://aliciacurth.github.io
@jiweizhao.bsky.social
Associate Professor of Statistics and of Biostatistics & Medical Informatics at UW-Madison • Semiparametrics, Domain Adaptation, Machine Learning, Causal Inference, Patient Reported Outcome, Aging, Mental Health
@zshahn.bsky.social
Causal inference, assistant professor at CUNY SPH. Hoping this will be an unhealthy addiction I can feel good about. All likes are endorsements, but maybe by my 3 year old who stole my phone.
@djfoster.bsky.social
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
@lucystats.bsky.social
Biostatistician • Assistant Prof @ Wake Forest University • former postdoc @ Hopkins Biostat • PhD @ Vandy Biostat • 🎙 Casual Inference • lucymcgowan.com
@nshejazi.bsky.social
assistant professor of biostatistics at harvard—causal inference, machine learning, and semi-parametric estimation for science on infectious and chronic diseases and cancer avid runner, concertgoer, timezone hopper https://nimahejazi.org
@mcarone.bsky.social
Professor of Biostatistics, University of Washington School of Public Health. Affiliate Investigator, Fred Hutch Vaccine and Infectious Disease Division. Causal inference, ML, survival analysis, statistical epi, viruses and vaccine science. 🇨🇦🇮🇹🇦🇲
@herbps10.bsky.social
Post-doc at NYU Grossman School of Medicine (this account is solely in my personal capacity, all views are my own etc). Non-parametric statistics, causal inference, Bayesian methods. Herbsusmann.com
@awlevis.bsky.social
Postdoc @ CMU Statistics & Data Science www.awlevis.com interests: causal inference, distribution shift, machine learning, non/semiparametrics, w/ applications in EHR data & beyond
@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
@wenbowu.bsky.social
Assistant Professor of Biostatistics, Nephrology, and Data Science at NYU | Incoming Assistant Professor of Data Science at Johns Hopkins Bloomberg School of Public Health | https://wenbowu.me
@bsky.app
official Bluesky account (check username👆) Bugs, feature requests, feedback: [email protected]