Database

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Research scientist at Truthful AI; previously a member of technical staff on Anthropic’s alignment team; currently on leave from a PhD in computer science at UC Berkeley, supervised by Stuart Russell.

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Dr. Ysaline Bourgine de Meder is a medieval historian who now serves as Head of Strategy at Effective Altruism Sweden, where she applies her analytical and strategic skills to AI governance and policy work.

Jugal Patel is the COO and Co-Founder of Leap Laboratories, based in San Francisco, California. According to his Crunchbase profile he previously worked at Balance.io as an Operations Lead and studied Business Administration and Finance at San Francisco State University.

Vanessa Kosoy is an AI alignment researcher based in Israel, currently serving as Director of AI Research at ALTER (Association for Long Term Existence and Resilience) and Research Lead at CORAL (Computational Rational Agents Laboratory), while pursuing a PhD in Mathematics at the Technion – Israel Institute of Technology under Shay Moran. She holds a BSc in Pure Mathematics from Tel Aviv University and an MSc in Computer Science from the Hebrew University of Jerusalem, and spent over 15 years in software engineering roles including algorithm engineer, R&D manager, and startup founder before transitioning to AI safety research full-time roughly a decade ago. She was previously a research associate at the Machine Intelligence Research Institute (MIRI) and has been funded by the Long-Term Future Fund (LTFF). Her research centers on the learning-theoretic AI alignment agenda, and she is best known for developing Infra-Bayesianism (with co-author Alex Appel), a mathematical framework for handling non-realizability in reinforcement learning, as well as Infra-Bayesian Physicalism (now called Formal Computational Realism), which addresses naturalized induction. She has been a mentor in the MATS (ML Alignment Theory Scholars) program, running a track focused on the learning-theoretic agenda, and is a prolific contributor to the AI Alignment Forum and LessWrong.

Junior Research Scholar at ILINA and a Researcher at the University of Cape Town African Hub on AI Safety, Peace and Security, conducting Africa‑centric model safety evaluations; he previously served as a Junior Research Fellow at ILINA (technical governance track), completed the AI Safety Fundamentals technical track and the ALX Software Engineering program, and holds a law degree from Kabarak University plus a postgraduate diploma from the Kenya School of Law.

Research Engineer at GDM, interested in Scalable Oversight and Human-AI Complementarity

Research Associate and Head of Policy at ILINA, working on governance of AI in Global South countries and the governance of AI agents, with prior experience as an AI Futures Fellow and visiting researcher at the Centre for the Study of Existential Risk; she holds law degrees from Strathmore University and Columbia Law School.

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Geoffrey Hinton is a Director of The AI Safety Foundation and a pioneering British-Canadian researcher in artificial intelligence whose work on neural networks helped launch the deep learning revolution. He is Professor Emeritus at the University of Toronto and a 2024 Nobel Laureate in Physics for contributions to the theory and practice of artificial neural networks, and he now devotes much of his work to understanding and communicating the long-term safety risks of advanced AI systems.

Tyler Whitmer is the founder and CEO/President of Legal Advocates for Safe Science and Technology (LASST), a 501(c)(3) nonprofit that uses legal advocacy to make advances in science and technology safer for people and the planet. He previously spent over 15 years as an associate and then partner at Quinn Emanuel Urquhart & Sullivan, LLP as a commercial litigator, and later served as the first general counsel of a 501(c)(3) public charity before leaving that role at the end of 2023 to start LASST.

Tom David is President of the GPAI Policy Lab, a Campus Cyber–based policy lab that fosters international cooperation on general-purpose AI security and control through research, strategic advising, and training, and he is also co-founder of PRISM Eval, which specializes in stress-testing generative AI models on critical behaviors.

Senior AI Policy Researcher at the Safe AI Forum and Research Affiliate at the Oxford Martin AI Governance Initiative, focusing on identifying areas of possible agreement between leading AI powers and supporting the International Dialogues on AI Safety (IDAIS). Previously a Winter Fellow at the Centre for the Governance of AI in Oxford and a management consultant at Bain & Company in Singapore, he holds a Master’s in Global Affairs from Tsinghua University’s Schwarzman Scholars programme and a bachelor’s in Politics and Anthropology from the University of Cambridge.

Tilman Räuker is Co-Director of Pivotal Research, a fellowship program supporting researchers working on global catastrophic risk reduction with a focus on technical AI safety and AI governance. He holds a Master's degree from Leibniz University Hannover, where his thesis focused on temporally-extended reinforcement learning in dynamic algorithm configuration. His research centers on mechanistic interpretability and understanding the internal representations of deep neural networks, including work on transformer world models. He co-authored the widely-cited survey "Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks" (SaTML 2023), as well as papers on structured world representations and causal world models in maze-solving transformers, published at NeurIPS and ICLR. He previously served as a Technical AI Safety Research Manager at the ERA Fellowship and led requests for proposals on Cybersecurity AI and AI Agent Evaluation at the AI Safety Fund. He also participated in a FAR Labs residency researching goal-directedness in transformer models.

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Software engineer on the benchmarking team at Epoch AI, working on infrastructure for running AI benchmarks and improving new benchmarks, with prior experience in data science and cloud infrastructure at BlueCat and embedded GPU technology at Broadcom, and a degree in Mathematics and Computer Science from Oxford.

Preeti Ravindra is a Senior AI Security Researcher at Confidential AI Neocloud and a technical leader who has spent over a decade applying AI to security problems to make AI systems more reliable and trustworthy. Her career spans startups to Fortune 100 companies, where she has advanced research into scalable, revenue‑aligned systems and worked across security operations, detection engineering and vulnerability management while bridging research and engineering execution. She is a recognised industry voice, speaking at conferences such as DEFCON and BSides, serving on program committees for WiCyS and CAMLIS, and helping bridge the AI and security communities while supporting early‑career professionals.

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Matt Sheehan is a senior fellow in the Asia Program at the Carnegie Endowment for International Peace, where he researches global technology issues with a focus on China’s artificial intelligence ecosystem, technology policy, and political economy.

Education and public health entrepreneur

Sunishchal Dev is an AI evaluation research scientist at RAND, where his work focuses on evaluating and governing artificial intelligence systems. Before joining RAND, he spent about a decade in industry as a data scientist and management consultant implementing AI solutions, and he holds a B.A. in technology and innovation management from the University of Washington Bothell.

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Co-Founder of the AI Safety Initiative at Georgia Tech and fourth-year computer science student who also helped co-found the Effective Altruism club at Georgia Tech and is interested in AI safety field-building and technical work to mitigate long-term risks from transformative AI.

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Katerina Manoli is a researcher with Sentience Institute who has a background in philosophy, psychology, and neuroscience and is a PhD student at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany. She has conducted research on the perception of artificial agents and the ethics of human AI interaction at institutions including the Donders Institute for Brain, Cognition and Behaviour, Harvard Universitys Department of Social Studies, and the University of Glasgows School of Psychology and Neuroscience, and has served on boards of effective altruism organizations and as a STEM mentor for Effective Thesis.

Michael Hanna is a PhD student at the Institute for Logic, Language and Computation at the University of Amsterdam whose research focuses on interpreting and evaluating NLP models. As an Anthropic Fellow he led the development of the circuit-tracer library for feature circuits, which he now maintains as an open-source project integrated with Neuronpedia and supported by Decode Research.

Kunvar Thaman is a machine learning research engineer at Standard Intelligence in San Francisco. He studied at the Birla Institute of Technology and Science (BITS), Pilani. His work focuses on training large-scale neural networks and mechanistic interpretability research — reverse-engineering the internal computations of neural networks to understand how they work. He co-authored "Benchmark Inflation: Revealing LLM Performance Gaps Using Retro-Holdouts," presented at ICML 2024, which introduces a methodology called retro-holdouts to measure how much public benchmark scores are inflated by training data contamination. He also participated in AI Safety Camp (AISC9) where his team applied mechanistic interpretability methods to study out-of-context learning in neural networks. He maintains a research site at mechinterp.com and writes at kunvarthaman.com.

Federico L.G. Faroldi is a professor of Ethics, Law and Artificial Intelligence at the University of Pavia, where he directs the Center for Reasoning, Normativity and AI (CERNAI) and the Normative Risk Lab and holds a 1.2m € FIS grant on "generic reasoning". He is also affiliate faculty at UC Berkeley's Center for Human-Compatible AI (CHAI). His current research focuses on AI safety and risk (especially regulation and governance), AI alignment using normative reasons, deontic and modal logic, truthmaker semantics, and the law and ethics of artificial intelligence.

Executive coach, writer, and serial social entrepreneur; founder and Executive Director of Original Position and creator of What Future World?, and previously Executive Director of The AI Governance Archive (TAIGA) as well as co-founder of LifeWork and the Small Business School Challenge.

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Philippe Rivet is a Canadian electrical engineer based in Toronto, Ontario, who has pursued applied technical AI alignment research. He received an NSERC Undergraduate Student Research Award during his undergraduate studies and later completed studies related to machine learning and AI safety, including coursework from Dan Hendryck's ML Safety program. His GitHub profile reflects interests in evolutionary computing, reinforcement learning, interpretability, and AI alignment. He has described his path as a "costly attempt at AI safety research" and has since considered earning-to-give as an alternative contribution to effective altruism. He received a $10,000 grant in support of applied technical AI alignment research. He is active in the effective altruism community, having published a post on the EA Forum titled "The Highly Sensitive EA," and maintains interests in contemplative practice and philosophy.