Database

Timour Razek is Manager for Storytelling & Community Engagement at TechCongress, where he builds communications and partnerships that highlight the impact of technologists on national governance and the organization’s fellowship programs. Before joining TechCongress, he worked in global health communications, including roles with USAID’s Bureau for Global Health and the World Health Organization, focusing on equitable health reform and public engagement.

Independent AI alignment researcher in Sapporo, Japan, studying human-side alignment risks in non-self language models. Cohere Labs Catalyst Grant recipient; GLG independent consultant; published in AI Advances, Towards AI, and The Memoirist.

Malcolm Handley is part of the Softmax team and previously was employee #1 at Asana and an early engineer at Google. He founded Strong Atomics, a nuclear-fusion-focused venture capital firm, and serves as an advisor at ARPA-E.

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Morgan Rogers is a mathematician and Maître de conférences (Associate Professor) in computer science at LIPN (Laboratoire d'Informatique de Paris Nord), Université Sorbonne Paris Nord, based in Villetaneuse, France. He holds bachelor's and master's degrees in mathematics from the University of Cambridge and completed his PhD at the Università degli Studi dell'Insubria (Como, Italy) in 2021, with a thesis on toposes of monoid actions supervised by Professor Olivia Caramello. His academic research focuses on category theory and topos theory, with applications to models of lambda-calculus and descriptive complexity theory. In parallel, Rogers has engaged with AI alignment research through the AI Alignment Forum and LessWrong, receiving funding from the Long-Term Future Fund for a project to clarify and formalize the concept of goal-directedness, supervised by Adam Shimi. This work produced a sequence of posts applying formal mathematical approaches to the question of what it means for an agent to be goal-directed, connecting his category theory background to foundational questions in AI safety.

Charlie Petty is a life sciences and technology investor based in New York City who co-founded Adjuvant Capital, an investment firm focused on global public health, previously worked for the Global Health Investment Fund and Artemis Capital Partners (and its predecessor Axia Partners), and serves on the boards of multiple biotech and global health companies and nonprofits.

Jannik Brinkmann is a PhD student in Computer Science at the University of Mannheim, Germany, advised by Christian Bartelt and Paul Swoboda. He holds an M.Sc. in Data Science (with distinction) and a B.Sc. in Computer Science, both from the University of Mannheim. His research focuses on mechanistic interpretability of neural networks, including sparse autoencoders, causal mediation analysis, and the internal mechanisms underlying multi-step reasoning and cross-lingual representations in large language models. He has worked as a visiting researcher in the interpretable neural networks group at Northeastern University under David Bau, and in the ML2 group at NYU under He He. He received a Long-Term Future Fund grant to support part-time interpretability research in collaboration with David Bau and Logan Riggs, resulting in work on improving sparse autoencoder training methods and measuring progress in dictionary learning for language model interpretability.

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Founder @ On AIR with Aashka | Certified AI Governance Professional | Bug Bounty Hunter @ Anthropic | HBR Advisor | Interviewed by Finnish Media, NPR, France24

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Director at Apart Research

Member of technical staff at Anthropic focused on AI safety and reinforcement learning infrastructure, previously led AI safety programs at BlueDot Impact, and currently serves as an advisor to Formation Research.

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AI safety research

Dawn Song is a Professor of Computer Science at UC Berkeley and co-director of the Berkeley Center for Responsible, Decentralized Intelligence (RDI). Her research focuses on AI safety and security, agentic AI, deep learning, security and privacy, and decentralization technology, and she has received major honors including a MacArthur Fellowship, Guggenheim Fellowship, NSF CAREER Award, Sloan Research Fellowship, and ACM SIGSAC Outstanding Innovation Award.

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6-month stipend for a small group of collaborators to continue research on the Agent Structure Problem

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Luca De Leo is a Co-founding Director of BAISH (Buenos Aires AI Safety Hub) and an AI safety community builder based in Buenos Aires, Argentina. He studied Computer Science at the University of Buenos Aires (UBA), leaving the master's program to focus full-time on AI safety work after receiving grants from ACX+ and the Long-Term Future Fund to develop relevant research skills. He worked in operations at Nonlinear and facilitated AGI Safety Fundamentals courses, growing the AI Safety community at UBA to over 130 active members. He has achieved multiple competitive placements in Apart Research sprints and participated in AI safety hackathons organized at EAGx LatAm. His current work includes co-directing BAISH, serving as a facilitator for BlueDot Impact's AGI Strategy course, and part-time operations for the AI Species YouTube channel.

A 3-month fellowship in Cape Town, connecting a global cohort of talent to top mentors at MIT, Oxford, CMU, and Google DeepMind

Revealing Latent Knowledge Through Personality-Shift Tokens

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Sienka Dounia is a technical AI safety researcher originally from Chad, currently based in London after relocating to work on Eliciting Latent Knowledge (ELK) research with Jake Mendel at Apollo Research. She is a 2023 fellow of the ILINA Program (Initiative for Longtermism in Africa) and a 2024 fellow of the AI Futures Fellowship in Mexico, where she worked on developmental interpretability and singular learning theory. She is an AI Safety Content Associate and Knowledge & Systems Lead at Successif, a platform supporting professionals transitioning into AI risk reduction roles. She is also a researcher at the African Hub on AI Safety, Peace and Security at the University of Cape Town, and a co-author of the 2025 paper "Assessing the Case for Africa-Centric AI Safety Evaluations," which develops a risk taxonomy for frontier AI deployments in African contexts. Her research interests include model evaluation, model interpretability, AI deception, and the technical governance of advanced AI systems.

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Policy researcher at the Existential Risk Observatory with a background in regulatory policy in government and a tertiary qualification in economics, preparing submissions and policy proposals on AI existential risk.

Program Director for Talk to the City at the AI Objectives Institute, focused on building technology for knowledge curation and large-scale qualitative data. Before joining AOI she managed a DARPA research portfolio on how machines can help humans understand complex geopolitical systems and holds a PhD in computational modeling from the University of Florida and a B.S. in Symbolic Systems from Stanford.

Co-founder and founding advisor of Sage who led software development for the early Quantified Intuitions calibration and pastcasting tools and serves as a director of Sage Future Inc on corporate filings.

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6-month stipend to work on an ML safety project, with the aim of joining a ML safety team full-time after

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Senior Campaigner at the Existential Risk Observatory, founder of Conjointly and the Campaign for AI Safety, with a background in marketing and management consulting and a focus on AI safety advocacy.

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Andy Graham is a Chartered Mechanical Engineer and Fellow of the Institution of Mechanical Engineers who co-founded Amodo Design, a Sheffield-based multi-disciplinary engineering consultancy, after earning a first-class Mechanical Engineering degree from the University of Sheffield.

Kathleen Fisher is Chief Executive Officer of the Advanced Research and Invention Agency (ARIA). She previously led DARPA’s Information Innovation Office, overseeing more than 50 programmes, and founded a centre at RAND applying AI and formal methods to cybersecurity. She holds a PhD in computer science from Stanford and is a Fellow of AAAS and ACM.

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Marcus Williams is an AI safety researcher currently working on deception and scheming monitoring at OpenAI. He completed his Master's degree in engineering physics and machine learning at Lund University in 2023. After graduating, he worked at AI Safety Hub Oxford on a theoretical reinforcement learning project, co-authoring "On the Expressivity of Objective-Specification Formalisms in Reinforcement Learning," which was accepted at ICLR 2024. He received a Long-Term Future Fund grant for a six-month independent project on Multi-Objective Reinforcement Learning from AI Feedback (MORLAIF), which produced an arXiv paper demonstrating that decomposing preference modeling into multiple principles outperforms standard RLAIF baselines. In the MATS Summer 2024 cohort under mentor Micah Carroll, he researched annotator vulnerabilities and LLM influence on human preferences, resulting in the paper "Targeted Manipulation and Deception Emerge in LLMs Trained on User Feedback" (accepted at NeurIPS workshops). He is also a co-author on "Stress Testing Deliberative Alignment for Anti-Scheming Training" alongside researchers from OpenAI and Apollo Research.

Researcher at Anthropic whose prior roles include researcher at Google and PhD student at the Technical University of Munich; his expertise spans AI safety and alignment as well as graph neural networks and machine learning on molecules.