Database

Gabriel Recchia is a cognitive scientist and director of Modulo Research, where he works on the evaluation and alignment of large language models and the design of scalable oversight protocols. At Modulo he has led work releasing datasets of expert-annotated valid and invalid long-form solutions for use in scalable oversight experiments and related studies on how humans and models evaluate complex answers. Previously, he led user-testing research and evaluation for patient-friendly genetic reports and the Predict: Breast Cancer prognostic tool at the University of Cambridge’s Winton Centre for Risk and Evidence Communication, and has co-authored widely cited research on risk perception and communication.

David Reber is a PhD student in Computer Science at the University of Chicago, advised by Victor Veitch and Ari Holtzman. His research centers on precise causal inference over large language models, with a focus on post-hoc internal interpretability and validating human-understandable concepts within these systems. He is motivated by AI safety applications such as monitoring long-term planning and detecting deception, and is also interested in fairness and adversarial robustness. Earlier in his PhD he worked on empirical and theoretical extensions of Cohen and Hutter's pessimistic conservative reinforcement learning agent under the guidance of Michael Cohen. He received multiple grants from the Long-Term Future Fund, beginning in 2021, supporting his early RL safety research and his transition into the AI safety field. He is an active contributor to the AI Alignment Forum and LessWrong under the handle derber, and has published at venues including ICML.

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Roman Soletskyi is a Ukrainian researcher based in Paris, France, currently working as a researcher at Mistral AI. He completed an MS in Physics at Ecole Normale Superieure – PSL (2022–2024) after undergraduate studies in physics at Moscow Institute of Physics and Technology (2020–2022), and won a gold medal at the International Physics Olympiad in 2017. His research spans AI safety and formal verification, machine learning theory, and applied deep learning. Notable work includes "Training Safe Neural Networks with Global SDP Bounds" (2024, co-authored with David Dalrymple of ARIA), which develops methods for training neural networks with formal safety guarantees using semidefinite programming, with applications to safe reinforcement learning policies; this work was supported by the Long-Term Future Fund and the Machine Learning Alignment Theory Scholars program. At Mistral AI he has contributed to projects including the Pixtral 12B multimodal model and research on variational inference theory.

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Muslim theologian and scholar of Islamic law who has served as Imam of the Islamic Center of Virginia for many years, previously Imam of the Colorado Muslim Society in Denver, after spending 12 years studying in Saudi Arabia at Umm Al‑Qura University in Mecca and the Graduate Institute for the Preparation of Imams in Mecca.

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Master's student - Hasso-Plattner-Institute

Cody Rushing is a Member of Technical Staff at Redwood Research, where he works on AI control, model organisms, scheming and deception, and AI security. He completed his Bachelor's in Computer Science from UT Austin in Fall 2024 and attended the MATS Summer 2024 program under mentor Buck Shlegeris, later receiving a stipend extension to continue his AI Control research. He now also serves as a MATS mentor for the Redwood Research stream. His prior research includes mechanistic interpretability work under Neel Nanda and value alignment research with Brad Knox. He is a co-author of several papers including "Ctrl-Z: Controlling AI Agents via Resampling," "Factor(T,U): Factored Cognition Strengthens Monitoring of Untrusted AI," and "Basic Legibility Protocols Improve Trusted Monitoring." He is based in the San Francisco Bay Area.

Kiryl Shantyka is Executive Manager at Effective Altruism Sweden and works on operations and community support, including speaking on operations as a high‑impact career for the effective altruism movement.

Co-Director of the AI Safety Initiative at Georgia Tech, overseeing the group’s funding and strategy and conducting technical AI safety research on representation engineering, applied interpretability, and agentic misalignment.

Robbie McCorkell is the CTO at Leap Laboratories (Leap Labs), as indicated on his GitHub profile, and works on the company’s machine learning and interpretability tooling.

Garrison Lovely is a Brooklyn-based freelance journalist covering the intersection of money, power and artificial intelligence. He publishes Obsolete, an independent Substack newsletter on the economics and geopolitics of the AI industry, and is the author of the forthcoming book Obsolete: The AI Industry’s Trillion Dollar Race to Replace You—and How to Stop It (Nation Books/OR Books, 2026). His reporting on AI and corporate power has appeared in outlets including The New York Times, Nature, Bloomberg, The Verge, TIME, The Guardian US, SF Standard, The Nation, The American Prospect, Jacobin, BBC Future, Vox and Le Monde Diplomatique, and has been referenced by publications such as The New Yorker, The New York Times, The Atlantic and ProPublica. Lovely previously served as a Reporter in Residence at the Omidyar Network and earlier worked at GiveDirectly and as a product manager at Enigma Technologies.

Director and research lead at Truthful AI, an AI safety research non-profit based in Berkeley; affiliate of CHAI at UC Berkeley; previously based at the University of Oxford’s Future of Humanity Institute; holds a PhD from MIT and serves on the board of Ought.

Susie Alegre is an international human rights lawyer and CIGI senior fellow whose work focuses on technology, human rights and the emerging right to freedom of thought in the digital age; she is the author of "Freedom to Think" and "Human Rights, Robot Wrongs: Being Human in the Age of AI."

School Psychologist. AI Enthusiast

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Monica Valcourt is a Bay Area engineer currently working on an FPGA hardware project. She previously founded and ran a small MEV trading firm, has hobby interests in GPUs, semiconductor manufacturing, and other low-level systems, and has completed internships at Uber ATG and Amazon. She graduated from MIT in 2022 with a degree in Computer Science.

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Asanga Abeyagoonasekera is a Sri Lankan author, international security and geopolitics analyst, and strategic advisor who serves as Senior Fellow and Executive Director of the South Asia Foresight Network (SAFN) at The Millennium Project in Washington, DC. He is also a technical advisor to the IMF, formerly led Sri Lanka’s national security and foreign-policy think tanks, and has authored books including Teardrop Diplomacy, Conundrum of an Island, Sri Lanka at Crossroads, and Towards a Better World Order.

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Designer and researcher leading the Autostructures project with Groundless and co-developing the Soloware Platform, focusing on fluid interfaces and craftsmanship for AI-era sensemaking.

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Proud holder of first place on the inverse Manifold profit leaderboard.

Engineer at Google

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Co-Founder @ Seldon

Postdoc at Northeastern University

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Daniel Herrmann is an Assistant Professor in the Department of Philosophy at the University of North Carolina at Chapel Hill, where he is also Core Faculty in the Philosophy, Politics, and Economics program. He holds a PhD in Logic and Philosophy of Science from the University of California, Irvine, and conducted postdoctoral research at the University of Groningen. His research specializes in decision theory, formal epistemology, and the philosophy of artificial intelligence, with a focus on mathematical and computational models of optimal reasoning and learning as applied to artificial agents and self-reasoning systems. He was a fellow at PIBBSS (Program for Integrated Research in Alignment), and received funding from the Long-Term Future Fund to support the final year of his PhD research on embedded agency, a core topic in AI alignment concerning how agents reason about themselves as part of the world they act in. His work on the Alignment Forum includes co-authored research on subjective naturalism in decision theory and puzzles related to wireheading and utility functions for embedded agents.

Susan Etlinger is a senior fellow at CIGI and Director of AI and Innovation at Microsoft, where she leads work on the business and societal impacts of artificial intelligence, data and technology ethics.

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William Wang is a Professor in the Department of Computer Science at the University of California, Santa Barbara, the Mellichamp Endowed Chair in Mind and Machine Intelligence, Director of UC Santa Barbara’s Natural Language Processing Group, and the founding Director of the Center for Responsible Machine Learning.

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