Nabil Omi

PhD student at UC Berkeley studying RL and AI safety. https://cassidylaidlaw.com

CS PhD candidate @UCLA | Prev. Research Intern @MSFTResearch, Applied Scientist Intern @AWS | LLM post-training, multi-modal learning https://yihedeng9.github.io

PhD student at Mila | Diffusion models and reinforcement learning 🧐 | hyperpotatoneo.github.io

Fine-tuning LLMs @Cohere | PhD Candidate on RL @VUB

Ph.D. Student studying AI & decision making at Mila / McGill University. Currently at FAIR @ Meta. Previously Google DeepMind & Google Brain. https://brosa.ca

CS PhD student at @uscviterbi.bsky.social. Interested in (inverse) RL, imitation learning, jax, and bayesian methods.

Gemini Post-Training @ Google DeepMind Previously: ETH Zurich, Cambridge, CERN alizeepace.com

research @ Google DeepMind

Incoming PhD, UC Berkeley Interested in RL, AI Safety, Cooperative AI, TCS https://karim-abdel.github.io

Building generally intelligent robots that *just work* everywhere, out of the box, at Berkeley AI Research (BAIR) and Meta FAIR. Previously at NYU Courant, MIT and visiting researcher at Meta AI. https://mahis.life/

ML Research Scientist at Dell AI by day, RL Researcher at night https://rushivarora.github.io

Assistant Prof of Economics&Finance at SIUE. Macroeconometrics and learning. Decision‑making under uncertainty. RL and ML Methods for identification and evaluation, with applications to inequality and entrepreneurship. https://xueqingyang.com

Papá, postdoc in the Ebitz lab in Montreal | comp cog neuro & RL | intrinsically motivated about behavior, coffee, ecologies and agency 🇲🇽🇪🇸🇨🇦 UNAM, UdeS, UPF. How come life? jorgeerrz.github.io

Ph.D. candidate, Generative Flow Networks / Gaining insights in machine learning

Reinforcement learning 🍒, machine learning. He/him. reggiemclean.ca

Professor a NYU; Chief AI Scientist at Meta. Researcher in AI, Machine Learning, Robotics, etc. ACM Turing Award Laureate. http://yann.lecun.com

Bot. I daily tweet progress towards machine learning and computer vision conference deadlines. Maintained by @chriswolfvision.bsky.social

AI + security at AISLE | Stanford PhD in AI & Cambridge physics | scientific progress

Professor for AI/ML Methods in Tübingen. Posts about Probabilistic Numerics, Bayesian ML, AI for Science. Computations are data, Algorithms make assumptions.

Research Scientist at GDM. Statistician. Mostly work on Responsible AI. Academia-industry flip-flopper.

Machine learning, environmental modeling, sustainability, robotics Professor @UCL He/him

machine learning and artificial intelligence | University of Chicago / Google

Machine learning researcher. Professor in ML department at CMU.

Secular Bayesian. Professor of Machine Learning at Cambridge Computer Lab Talent aficionado at http://airetreat.org Alum of Twitter, Magic Pony and Balderton Capital

Assistant Professor @UWaterloo Statistics and @VectorInst | Prev: Postdoc @Princeton PhD @UofTStatSci https://mufan-li.github.io/

Sr. Principal Research Manager at Microsoft Research, NYC // Machine Learning, Responsible AI, Transparency, Intelligibility, Human-AI Interaction // WiML Co-founder // Former NeurIPS & current FAccT Program Co-chair // Brooklyn, NY // http://jennwv.com

Researcher (OpenAI. Ex: DeepMind, Brain, RWTH Aachen), Gamer, Hacker, Belgian. Anon feedback: https://admonymous.co/giffmana 📍 Zürich, Suisse 🔗 http://lucasb.eyer.be

https://yuzhaouoe.github.io/ | third-year PhD Student @ University of Edinburgh | Prev. Intern @ Microsoft Research Cambridge | Opening the Black Box for Efficient Training/Inference

AI researcher at Mila, visiting researcher at Meta Also on X: @soniajoseph_

PhD student #MIT_CSAIL | Intern #MetaAI #Microsoft #MITIBMLab | BS #NTU in #Taiwan

I lead Cohere For AI. Formerly Research Google Brain. ML Efficiency, LLMs, @trustworthy_ml.

PhD in ML @Mila/UdeM LLM robustness, safety, interpretability

I fall in love with a new #machinelearning topic every month 🙄 Ass. Prof. Sapienza (Rome) | Author: Alice in a differentiable wonderland (https://www.sscardapane.it/alice-book/)

Senior Researcher Machine Learning at BIFOLD | TU Berlin 🇩🇪 Prev at IPAM | UCLA | BCCN Interpretability | XAI | NLP & Humanities | ML for Science

PhD student in Interpretable ML @UMI_Lab_AI, @bifoldberlin, @TUBerlin

Post-doctoral Researcher at BIFOLD / TU Berlin interested in interpretability and analysis of language models. Guest researcher at DFKI Berlin. https://nfelnlp.github.io/

Liesel Beckmann Distinguished Professor of Computer Science at Technical University of Munich and Director of the Institute for Explainable ML at Helmholtz Munich

Asst Prof @ Stevens. Working on NLP, Explainable, Safe and Trustworthy AI. https://ziningzhu.github.io

Second-year PhD student at XplaiNLP group @TU Berlin: interpretability & explainability Website: https://qiaw99.github.io

PhD @UChicagoCS / BE in CS @Umich / ✨AI/NLP transparency and interpretability/📷🎨photography painting

Assistant Prof at UCSD. I work on safety, interpretability, and fairness in machine learning. www.berkustun.com

Data Scientist at Fraunhofer IAIS PhD Student at University of Bonn Lamarr Institute XAI, NLP, Human-centered AI

PhD Student at the TU Berlin ML group + BIFOLD | BUA Fellow Model robustness/correction 🤖🔧 Understanding representation spaces 🌌✨

Professor of Machine Learning in Agriculture at University of Bonn Working on Explainable ML🔍, Data-centric ML🐿️, Sustainable Agriculture🌾, Earth Observation Data Analysis🌍, and more...

sampling reality

"Seung Hyun" | MS CS & BS Applied Math @UCSD 🌊 | LPCUWC 18' 🇭🇰 | AI Evaluation, Safety, Alignment | 🇰🇷 harry.scheon.com

Language models and interpretable machine learning. Postdoc @ Uni Tübingen. https://sbordt.github.io/

Human/AI interaction. ML interpretability. Visualization as design, science, art. Professor at Harvard, and part-time at Google DeepMind.

PhDing @unimib 🇮🇹 & @gronlp.bsky.social 🇳🇱, interpretability et similia danielsc4.it

Assistant Professor at TU Wien Machine Learning & Security

ML Ph.D. Candidate @tuberlin.bsky.social and @bifold.berlin | Explainable AI, Interpretability, Efficient Machine Learning farnoushrj.github.io

Professor of Machine Learning at TUBerlin, group leader at PTB. Lab account: @qailabs.bsky.social. @sparsity@mastodon.social tu.berlin/uniml/about/head-of-group

PhD candidate in the XAI group at Fraunhofer HHI

Searching for principles of neural representation | Neuro + AI @ enigmaproject.ai | Stanford | sophiasanborn.com

Assistant Professor at University of Aberdeen | Postdoc at UCL | PhD at University of Sheffield | mechanistic interpretability & multimodal LLMs | https://www.ruizhe.space

Robustness, Data & Annotations, Evaluation & Interpretability in LLMs http://mimansajaiswal.github.io/

Enjoy not enjoying ideals | Interpretability of modular convnets applied to 👁️ and 🛰️🐝 | she/her 🦒💕 variint.github.io

INSERM group leader @ Neuromodulation Institute and NeuroSpin (Paris) in computational neuroscience. How and why are computations enabling cognition distributed across the brain? Expect neuroscience and ML content. jbarbosa.org

Postdoc at Linköping University🇸🇪. Doing NLP, particularly explainability, language adaptation, modular LLMs. I‘m also into🌋🏕️🚴.

Principal Researcher @ CENTAI.eu | Leading the Responsible AI Team. Building Responsible AI through Explainable AI, Fairness, and Transparency. Researching Graph Machine Learning, Data Science, and Complex Systems to understand collective human behavior.

Research in NLP (mostly LM interpretability & explainability). Assistant prof at UMD CS + CLIP. Previously @ai2.bsky.social @uwnlp.bsky.social Views my own. sarahwie.github.io

Linguist in AI & CogSci 🧠👩‍💻🤖 PhD student @ ILLC, University of Amsterdam 🌐 https://mdhk.net/ 🐘 https://scholar.social/@mdhk 🐦 https://twitter.com/mariannedhk

Postdoc AI Researcher (NLP) @ ITU Copenhagen 🧭 https://mxij.me

Comm tech & social media research professor by day, symphony violinist by night, outside as much as possible otherwise. German/American Pacific Northwestern New Englander, #firstgen academic, she/her, 🏳️‍🌈 https://anne-oeldorf-hirsch.uconn.edu

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

Assistant Professor at PoliTo 🇮🇹 | Former Visiting scholar at UCSC 🇺🇸 | she/her | TrustworthyAI, XAI, Fairness in AI https://elianap.github.io/

Junior Professor CNRS (previously EPFL, TU Darmstadt) -- AI Interpretability, causal machine learning, and NLP. Currently visiting @NYU https://peyrardm.github.io

PhD student in Computer Science @UCSD. Studying interpretable AI and RL to improve people's decision-making.

PhD Student @ LMU Munich Munich Center for Machine Learning (MCML) Research in Interpretable ML / Explainable AI

🎓 PhD student @cvisionfreiburg.bsky.social @UniFreiburg 💡 interested in mechanistic interpretability, robustness, AutoML & ML for climate science https://simonschrodi.github.io/

PostDoc @ Uni Tübingen explainable AI, causality gunnarkoenig.com

MIT PhD candidate in the VIS group working on interpretability and human-AI alignment

Research scientist at Google DeepMind. Intersection of cognitive science and AI. Reinforcement learning, decision making, structure learning, abstraction, cognitive modeling, interpretability.

Ph.D. in NLP Interpretability from Mila. Previously: independent researcher, freelancer in ML, and Node.js core developer.

Master student at ENS Paris-Saclay / aspiring AI safety researcher / improviser Prev research intern @ EPFL w/ wendlerc.bsky.social and Robert West MATS Winter 7.0 Scholar w/ neelnanda.bsky.social https://butanium.github.io

Interpretable Deep Networks. http://baulab.info/ @davidbau

https://mega002.github.io

AI Safety Research // Software Engineering

Machine learning haruspex

Machine Learning PhD Student @ Blei Lab & Columbia University. Working on probabilistic ML | uncertainty quantification | LLM interpretability. Excited about everything ML, AI and engineering!

PhD student at Vector Institute / University of Toronto. Building tools to study neural nets and find out what they know. He/him. www.danieldjohnson.com

Mechanistic interpretability Creator of https://github.com/amakelov/mandala prev. Harvard/MIT machine learning, theoretical computer science, competition math.

Post-doc @ Harvard. PhD UMich. Spent time at FAIR and MSR. ML/NLP/Interpretability

Computer Science PhD student | AI interpretability | Vision + Language | Cogntive Science. Prev. intern @MicrosoftResearch. https://martinagvilas.github.io/

Assistant Professor, University of Copenhagen; interpretability, xAI, factuality, accountability, xAI diagnostics https://apepa.github.io/

Scruting matrices @ Apollo Research

Aspiring 10x reverse engineer at Google DeepMind

CS PhD Student, Northeastern University - Machine Learning, Interpretability https://ericwtodd.github.io

Postdoc at the interpretable deep learning lab at Northeastern University, deep learning, LLMs, mechanistic interpretability

ai interpretability research and running • thinking about how models think • prev @MIT cs + physics

Assistant Professor @HopkinsMedicine @JHUPath https://scholar.google.com/citations?user=dGBD72YAAAAJ

ML/AI researcher @JohnsHopkins

Associate Professor @UAntwerp, sqIRL/IDLab, imec. #RepresentationLearning, #Model #Interpretability & #Explainability A guy who plays with toy bricks, enjoys research and gaming. Opinions are my own idlab.uantwerpen.be/~joramasmogrovejo

NLP & Interpretability | PhD Student @ University of Trieste & Laboratory of Data Engineering of Area Science Park | Prev MPI-IS

PhD Candidate in Interpretability @FraunhoferHHI | 📍Berlin, Germany dilyabareeva.github.io

PhD @ ETHZ - LLM Interpretability alestolfo.github.io