Probabilistic Machine Learning
Researchers in probabilistic machine learning.
Created by
@timrudner.bsky.social
@nirliptapande.bsky.social
Researcher at NUS Singapore || Bayesian Statistics and Causal Inference I paint sometimes :) nirliptapande.github.io
@sunwoopkim.bsky.social
Theoretical physicist. Currently a PhD candidate at King's College London. Same handle on 🐦 and 🐘. Personal website: https://sunwoo-kim.github.io
@arnosolin.bsky.social
Assistant Professor in Machine Learning, Aalto University. ELLIS Scholar. http://arno.solin.fi
@starrtrooper.bsky.social
@sebastiendestercke.bsky.social
CS researcher in uncertainty reasoning (whenever it appears: risk analysis, AI, philosophy, ...), mostly mixing sets and probabilities. Posts mostly on this topic (french and english), and a bit about others. Personal account and opinions.
@minarezaei.bsky.social
Machine Learning Researcher, @LMU Munich, MCML Interested in probabilistic deep learning, generative models, and trustworthy ML. minare.github.io
@adriencorenflos.bsky.social
Research Fellow at the university of Warwick. I compute integrals for a living. https://adriencorenflos.github.io/
@eweinstein.bsky.social
Postdoc at Columbia with Dave Blei // Jura Bio // machine learning, statistics, biophysics, genomics On the 2024-2025 academic job market. https://eweinstein.github.io/
@terrytangyuan.xyz
Senior Principal Software Engineer at Red Hat AI | Open Source Leader at Argo, Kubeflow, @kubernetes.io Serving | Maintainer of KServe, XGBoost, TensorFlow | Keynote Speaker | Author | Technical Advisor More info: http://terrytangyuan.xyz We are hiring!
@blackhc.bsky.social
My opinions only here. 👨🔬 RS DeepMind Past: 👨🔬 R Midjourney 1y 🧑🎓 DPhil AIMS Uni of Oxford 4.5y 🧙♂️ RE DeepMind 1y 📺 SWE Google 3y 🎓 TUM 👤 @nwspk
@rarefin.bsky.social
PhD Candidate Mila/University of Montreal | Intern at Amazon | Ex. ServiceNow, Recursion, UpStride | Researching to understand how Deep Learning works https://rarefin.github.io
@nishantharun.bsky.social
PhD candidate @CarnegieMellon / ex @MartinosCenter interested in bridging the gap between machine learning academia and clinical practice; uncertainty quantification / explainable AI
@avt.im
Decision-making under uncertainty, machine learning theory, artificial intelligence · anti-ideological · Assistant Research Professor, Cornell https://avt.im/ · https://scholar.google.com/citations?user=EGKYdiwAAAAJ&sortby=pubdate
@jwenger.bsky.social
Postdoctoral Research Scientist in Statistics at Columbia University
@ayushbharti.bsky.social
Academy Research Fellow at the Dept. of Computer Science, Aalto University, Finland. Affiliated with the Finnish Center for Artificial Intelligence. Website: http://bharti-ayush.github.io
@elseml.bsky.social
💡 PhD candidate @ Heidelberg University. 🌱 AI for science - simulation-based inference, robust machine learning & cognitive modeling.
@gandry.bsky.social
PhD student @universitedeliege.bsky.social with @glouppe.bsky.social. @frsFNRS research fellow. Focuses on AI for scientific discoveries 🤖
@sigmabayesian.bsky.social
Visiting Researcher @NYU Courant, CILVR. PhD student @TU Denmark, MLLS(https://mlls.dk). Probabilistic ML/DL. Nth order Markovian. Support Manifolds and latents. Previously intern @SonyAI in deep generative modelling. web: http://uppalanshuk.github.io
@bleilab.bsky.social
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
@aaronschein.bsky.social
Assistant Professor of Statistics & Data Science at UChicago Topics: data-intensive social science, Bayesian statistics, causal inference, probabilistic ML Proud “golden retriever” 🦮
@jmtomczak.bsky.social
Group Leader, Generative AI | NeurIPS 2024 Program Chair | Principal Scientist & Director | Founder of Amsterdam AI Solutions
@dan-kowal.bsky.social
Associate Professor of Statistics and Data Science at Cornell University. https://www.danielrkowal.com/
@jamesallingham.bsky.social
Research Scientist @GoogleDeepMind | Organiser @DeepIndaba | Machine Learning PhD @CambridgeMLG | 🇿🇦
@jameshensman.bsky.social
Trying to make all the AIs more efficient. Former Gaussian process connoisseur. Researcher at Microsoft Research.
@ianholmes.org
Berkeley professor (Bioeng, Compbio). Visiting Scientist at Calico. JBrowse genome browser / Apollo annotation editor, ML for gene regulation / molecular evolution / synbio. Occasional music, games, jokes
@sulinliu.bsky.social
Postdoc at MIT. Generative models, inference, AI for science. Prev: Princeton, Meta, NUS. liusulin.github.io
@flaviucipcigan.bsky.social
Building AIs for scientific discovery at IBM Research & AI Alliance. Discovered antibiotics and materials for carbon capture. Tango dancer. See more at flaviucipcigan.com. Opinions my own.
@swetakar.bsky.social
Machine learning PhD student @ Blei Lab in Columbia University Working in mechanistic interpretability, nlp, causal inference, and probabilistic modeling! Previously at Meta for ~3 years on the Bayesian Modeling & Generative AI teams. 🔗 www.sweta.dev
@glouppe.bsky.social
AI for Science, deep generative models, inverse problems. Professor of AI and deep learning @universitedeliege.bsky.social. Previously @CERN, @nyuniversity.
@wordscompute.bsky.social
nlp/ml phding @ usc, interpretability & reasoning & pretraining & emergence 한american, she, iglee.me, likes ??= bookmarks
@kareemyousrii.bsky.social
Postdoc @ University of California, Irvine | PhD from CS@UCLA Neuro-Symbolic AI, Tractable Probabilistic Reasoning, Generative Models kareemahmed.com
@siahkoohi.bsky.social
Assistant Professor, CS at UCF | Uncertainty Quantification https://alisiahkoohi.github.io/
@imurray.bsky.social
Professor of Machine Learning and Inference, Edinburgh Informatics, Formally Amazon Scholar. Opinions are my own. Also https://homepages.inf.ed.ac.uk/imurray2/ and https://mastodon.social/@imurray and https://x.com/driainmurray
@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/
@pierrealquier.bsky.social
Professor of Statistics @ ESSEC Business School Asia-Pacific campus Singapore 🇸🇬 https://pierrealquier.github.io/ Previously: RIKEN AIP 🇯🇵 ENSAE Paris 🇫🇷 🇪🇺 UCD Dublin 🇮🇪 🇪🇺 Random posts about stats/maths/ML/AI, poor jokes & birds photo 🌈
@mschauer.bsky.social
Statistician, Associate Professor (Lektor) at University of Gothenburg and Chalmers; inference and conditional distributions for anything https://mschauer.github.io http://orcid.org/0000-0003-3310-7915 [ˈmoː/r/ɪts ˈʃaʊ̯ɐ]
@philipphennig.bsky.social
Professor for AI/ML Methods in Tübingen. Posts about Probabilistic Numerics, Bayesian ML, AI for Science. Computations are data, Algorithms make assumptions.
@sperez-vieites.bsky.social
Postdoctoral researcher at Aalto University and FCAI, Helsinki 🇫🇮 Previously in Edinburgh, Lille and Madrid. Working on Bayesian inference for state-space models, and Bayesian experimental design. https://sarapv.github.io/
@gdalle.bsky.social
Researcher in machine learning and optimization. Open source enthusiast. Parody songwriter (aka PianoHamster). OCD survivor.
@thomas-pinder.bsky.social
Bayesian statistics, Gaussian processes, and all things ML. Senior Applied Scientist at Amazon and developer of GPJax.
@jrudoler.bsky.social
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)
@trappmartin.bsky.social
ML Researcher @ Aalto University 🇫🇮. Previous: TU Graz 🇦🇹, originally from 🇩🇪. Doing: Reliable ML | uncertainty stuff | Bayesian stats | probabilistic circuits https://trappmartin.github.io/
@dianarycai.bsky.social
Machine learning & statistics researcher @ Flatiron Institute. Posts on probabilistic ML, Bayesian statistics, decision making, and AI/ML for science. www.dianacai.com
@velezbeltran.bsky.social
Machine Learning PhD Student @ Blei Lab & Columbia University. Working on probabilistic ML | uncertainty quantification | LLM interpretability. Excited about everything ML, AI and engineering!
@andreasgrv.bsky.social
Postdoc in ML/NLP at the University of Edinburgh. Interested in Bottlenecks in Neural Networks; Unargmaxable Outputs. https://grv.unargmaxable.ai/
@loreloc.bsky.social
#probabilistic-ml #circuits #tensor-networks PhD student @ University of Edinburgh https://loreloc.github.io/
@leanderk.bsky.social
ML PhD student @ Uni Edinburgh Interesting in (tractable) probabilistic machine learning, computing niche integrals and whatever you want to tell me about your research!
@nicolabranchini.bsky.social
🇮🇹 Stats PhD @ University of Edinburgh 🏴 @ellis.eu PhD - visiting @avehtari.bsky.social 🇫🇮 🤔💭 about uncertainty quantification. Interested in sampling/transport methodologies, applications in climate/science. https://www.branchini.fun/about
@lenazellinger.bsky.social
ELLIS PhD student at the University of Edinburgh https://lenazellinger.github.io/
@javaloyml.bsky.social
Postdoc at the University of Edinburgh working on Machine Learning. Previously in Saarbrücken, Tübingen, and Murcia. 🌐 adrianjav.github.io
@idris1.bsky.social
Johns Hopkins CS PhD student. #MedicalRobotics #ComputerVision #DeepLearning #Guitar
@sethaxen.com
Empowering scientists with machine learning @mlcolab.org. Sometimes #Bayesian. Usually #FOSS. Preferably in #JuliaLang. Expat: 🇺🇸 ➡️ 🇩🇪 💼 On the job market (remote/Stuttgart area) sethaxen.com
@olegranmo.bsky.social
AI Professor and Founding Director @ https://cair.uia.no | Chair of Technical Steering Committee @ https://www.literal-labs.ai | Book: https://tsetlinmachine.org
@marvinschmitt.bsky.social
🇪🇺 AI/ML, Member @ellis.eu 🤖 Generative NNs, ProbML, Uncertainty Quantification, Amortized Inference, Simulation Intelligence 🎓 PhD+MSc CS, MSc Psych 🏡 marvinschmitt.github.io ✨ On the job market, DMs open 📩
@mgollub.bsky.social
🧬💻 | Director of Machine Learning @jura.bsky.social | Probabilistic models, ML, Molecular biology, Immune receptors.
@emilevankrieken.com
Post-doc @ University of Edinburgh. Neurosymbolic Machine Learning, Generative Models, NLP https://www.emilevankrieken.com/
@nolovedeeplearning.bsky.social
human being | assoc prof in #ML #AI #Edinburgh | PI of #APRIL | #reliable #probabilistic #models #tractable #generative #neuro #symbolic | heretical empiricist | he/him 👉 https://april-tools.github.io
@karen-ullrich.bsky.social
Research scientist at FAIR NY ❤️ Machine Learning + Information Theory. Previously, PhD at UoAmsterdam, intern at DeepMind + MSRC.
@willieneis.bsky.social
Assistant Professor in CS + AI at USC. Previously at Stanford, CMU. Machine Learning, Decision Making, AI-for-Science, Generative AI, ML Systems, LLMs. https://willieneis.github.io
@yaringal.bsky.social
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)
@vincefort.bsky.social
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/
@tychovdo.bsky.social
Postgraduate researcher (PhD) at Imperial College London and visiting researcher at the University of Oxford. Working on probabilistic machine learning.
@maosbot.bsky.social
Parent, spouse, Australian, Professor of Machine Learning in Oxford. Bayesian ML, Long Covid, trans rights, photos of dog, AI must be good for humans, https://www.robots.ox.ac.uk/~mosb
@mgorinova.bsky.social
ex-@TwitterCortex @Birdwatch 💙 | Now LLMs for innovation & IP 🚀 | PhD in probabilistic machine learning, loyal servant to a cat, collector of random variables, lover of well-placed puns https://mgorinova.github.io/
@mvdw.bsky.social
Associate Professor in Machine Learning at the University of Oxford. Interested in automatic inductive bias selection using Bayesian tools.
@jwvdm.bsky.social
Associate Professor (UHD) at the University of Amsterdam. Probabilistic methods, deep learning, and their applications in science in engineering.
@eringrant.bsky.social
Senior Research Fellow @ ucl.ac.uk/gatsby & sainsburywellcome.org {learning, representations, structure} in 🧠💭🤖 my work 🤓: eringrant.github.io not active: sigmoid.social/@eringrant @eringrant@sigmoid.social, twitter.com/ermgrant @ermgrant
@desirivanova.bsky.social
Research fellow @OxfordStats @OxCSML, spent time at FAIR and MSR Former quant 📈 (@GoldmanSachs), former former gymnast 🤸♀️ My opinions are my own 🇧🇬-🇬🇧 sh/ssh
@sejdino.bsky.social
Professor of Statistical Machine Learning at the University of Adelaide. https://sejdino.github.io/
@canaesseth.bsky.social
Assistant Professor of Machine Learning Generative AI, Uncertainty Quantification, AI4Science Amsterdam Machine Learning Lab, University of Amsterdam https://naesseth.github.io
@cmaddis.bsky.social
Asst. Prof. in Machine Learning at UofT. #LongCOVID patient. https://www.cs.toronto.edu/~cmaddis/
@alexlew.bsky.social
Theory & practice of probabilistic programming. Current: MIT Probabilistic Computing Project; Fall '25: Incoming Asst. Prof. at Yale CS
@timrudner.bsky.social
Assistant Professor & Faculty Fellow, NYU. AI Fellow, Georgetown University. Probabilistic methods for robust and transparent ML & AI Governance. Prev: Oxford, Yale, UC Berkeley. https://timrudner.com