Lee Sharkey

For over two decades, the Machine Intelligence Research Institute (MIRI) has worked to understand and prepare for the critical challenges that humanity will face as it transitions to a world with artificial superintelligence.

Neuroscientist @Janelia, HHMI www.ahrenslab.org

✨ mechanistic interpretability research scientist @ Goodfire | deep learning, math, biology | creating a more beautiful future

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

Ph.D. student at @jhuclsp, human LM that hallucinates. Formerly @MetaAI, @uwnlp, and @AWS they/them🏳️‍🌈 #NLProc #NLP Crossposting on X.

Tell me about challenges, the unbelievable, the human mind and artificial intelligence, thoughts, social life, family life, science and philosophy.

Laplace Junior Chair, Machine Learning ENS Paris. (prev ETH Zurich, Edinburgh, Oxford..) Working on mathematical foundations/probabilistic interpretability of ML (what NNs learn🤷‍♂️, disentanglement🤔, king-man+woman=queen?👌…)

Postdoc at ETH. Formerly, PhD student at the University of Cambridge :)

PhD @ ETHZ - LLM Interpretability alestolfo.github.io

Independent Mechanistic Interpretability Researcher

PhD Student at the ILLC / UvA doing work at the intersection of (mechanistic) interpretability and cognitive science. Current Anthropic Fellow. hannamw.github.io

PhD student at Northeastern, previously at EpochAI. Doing AI interpretability. diatkinson.github.io

Ph.D. Student at UNC NLP | Prev: Apple, Amazon, Adobe (Intern) vaidehi99.github.io | Undergrad @IITBombay

PhD student in Responsible NLP at the University of Edinburgh, curious about interpretability and alignment

CS Ph.D. Candidate @ Northeastern | Interpretability + Data Science | BS/MS @ Brown koyenapal.github.io

Senior Research Scientist at Google DeepMind. 🌐 jasmijn.bastings.me

Research Engineer @ FAR.AI taufeeque9.github.io

Interpretability researcher at @eleutherai.bsky.social

Physics, Visualization and AI PhD @ Harvard | Embedding visualization and LLM interpretability | Love pretty visuals, math, physics and pets | Currently into manifolds Wanna meet and chat? Book a meeting here: https://zcal.co/shivam-raval

Interpretability

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

Tenure-track faculty at the Max Planck Institute for Software Systems Previously postdoc at UW and AI2, working on Natural Language Processing Recruiting PhD students! 🌐 https://lasharavichander.github.io/

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

Technology specialist at the EU AI Office / AI Safety / Prev: University of Amsterdam, EleutherAI, BigScience Thoughts & opinions are my own and do not necessarily represent my employer.

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

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

Organic machine turning tea into theorems ☕️ AI @ Microsoft Research ➡️ Goal: Teach models (and humans) to reason better Let’s connect re: AI for social good, graphs & network dynamics, discrete math, logic 🧩, 🥾,🎨 Organizing for democracy.🗽 www.rlaw.me

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

member of technical staff @stanfordnlp.bsky.social

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

PhDing @AIM_Harvard @MassGenBrigham｜PhD Fellow @Google | Previously @Bos_CHIP @BrandeisU More robustness and explainabilities 🧐 for Health AI. shanchen.dev

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

PhD student @CMU LTI - working on model #interpretability, student researcher @google; prev predoc @ai2; intern @MSFT nishantsubramani.github.io

PhD at EPFL with Robert West, Master at ETHZ Mainly interested in Language Model Interpretability and Model Diffing. MATS 7.0 Winter 2025 Scholar w/ Neel Nanda jkminder.ch

Aspiring 10x reverse engineer at Google DeepMind

PhD student at UC Berkeley. NLP for signed languages and LLM interpretability. kayoyin.github.io 🏂🎹🚵‍♀️🥋

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

Statistician. PhD @Harvard • Masters degree in pure math • Follow me for fun, nerdy content. Sign up to my newsletter: kareemcarr.substack.com

Associate Professor, Department of Psychology, Harvard University. Computation, cognition, development.

Professor, Department of Psychology and Center for Brain Science, Harvard University https://gershmanlab.com/

Director, MIT Computational Psycholinguistics Lab. President, Cognitive Science Society. Chair of the MIT Faculty. Open access & open science advocate. He. Lab webpage: http://cpl.mit.edu/ Personal webpage: https://www.mit.edu/~rplevy

Associate professor of computer science at Northeastern University. Natural language processing, digital humanities, OCR, computational bibliography, and computational social sciences. Artificial intelligence is an archival science.

Asst Prof at Johns Hopkins Cognitive Science • Director of the Group for Language and Intelligence (GLINT) ✨• Interested in all things language, cognition, and AI jennhu.github.io

Associate Professor at Harvard & Kempner Institute. Applying computational frameworks & machine learning to decode multi-scale neural processes. Marathoner. Rescue dog mom. https://www.rajanlab.com/

🥇 LLMs together (co-created model merging, BabyLM, textArena.ai) 🥈 Spreading science over hype in #ML & #NLP Proud shareLM💬 Donor @IBMResearch & @MIT_CSAIL

| Cellular/Molecular-turned-Computational Neuroscientist | | What do neurons even do?? | Neural Computation with Dendrites | | Biophysical Optimization | AI <-> Neuro | | Postdoctoral Research Fellow at the Harvard Kempner Institute | | www.ilenna.com

Senior Machine Learning Researcher, Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard. Board Member, Neuromatch. she/her. Views are my own.

HBI brings together neuroscience researchers from different parts of Harvard and its affiliated hospitals. Throughout all that we do, we aspire to build and nurture a scientific community that is diverse, inclusive, and welcoming. https://brain.harvard.edu

Postdoc at CBS, Harvard University (New around here)

(jolly good) Fellow at the Kempner Institute @kempnerinstitute.bsky.social‬, incoming assistant professor at UBC Linguistics (and by courtesy CS, Sept 2025). PhD @stanfordnlp.bsky.social‬ with the lovely @jurafsky.bsky.social‬ isabelpapad.com

Neuroscience Professor at Harvard University. Personal account and posts here. Research group website: https://vnmurthylab.org.

Robotics and Reinforcement Learning tinkerer. brandonrohrer.com Wrangler of algorithms for Confluence @ Atlassian. Eater of bread. Sipper of whisky. Reports to a Shih Tzu.

research scientist @deepmind. language & multi-agent rl & interpretability. phd @BrownUniversity '22 under ellie pavlick (she/her) https://roma-patel.github.io

I make sure that OpenAI et al. aren't the only people who are able to study large scale AI systems.

Author of Interpretable Machine Learning and other books Newsletter: https://mindfulmodeler.substack.com/ Website: https://christophmolnar.com/

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

NLP assistant prof at KU Leuven, PI @lagom-nlp.bsky.social. I like syntax more than most people. Also multilingual NLP, interpretability, mountains and beer. (She/her)

Assistant Professor in NLP (Fairness, Interpretability and lately interested in Political Science) at the University of Copenhagen ✨ Before: PostDoc in NLP at Uni of CPH, PhD student in ML at TU Berlin

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

Full of childlike wonder. Building friendly robots. UT Austin PhD student, MIT ‘20.

Associate professor at IT University of Copenhagen: NLP, language models, interpretability, AI & society. Co-editor-in-chief of ACL Rolling Review. #NLProc #NLP

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

AI for storytelling, games, explainability, safety, ethics. Professor at Georgia Tech. Director of ML Center at GT. Time travel expert. Geek. Dad. he/him

Professor of Statistical Machine Learning at the University of Adelaide. https://sejdino.github.io/

Explainability, Computer Vision, Neuro-AI.🪴 Kempner Fellow @Harvard. Prev. PhD @Brown, @Google, @GoPro. Crêpe lover. 📍 Boston | 🔗 thomasfel.me

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

Reverse engineering neural networks at Anthropic. Previously Distill, OpenAI, Google Brain.Personal account.

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

Postdoc at Northeastern and incoming Asst. Prof. at Boston U. Working on NLP, interpretability, causality. Previously: JHU, Meta, AWS

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

https://mega002.github.io

Gemini Post-Training ⚫️ Research Scientist at Google DeepMind ⚫️ PhD from ETH Zurich

AI Safety Research // Software Engineering

Postdoc @ Northeastern, @ndif-team.bsky.social w/ @davidbau.bsky.social. Interpretability ∩ HCI ∩ #NLProc. Built @inseq.org. Prev: PhD @gronlp.bsky.social, ML @awscloud.bsky.social gsarti.com

Waiting on a robot body. All opinions are universal and held by both employers and family. ML/NLP professor. nsaphra.net

Machine learning haruspex

NLP PhD student at Imperial College London and Apple AI/ML Scholar.

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

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/

ml/nlp phding @ usc, currently visiting harvard, scientisting @ startup; interpretability & training & reasoning iglee.me