Mateus Bonadiman works as a QA professional at AE Studio, supporting quality assurance for the company’s software projects.

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John Burden is an AI safety and evaluation researcher currently serving as an AI Research Engineer at Prolific, having previously been a Senior Research Fellow and Programme Co-Director of the Kinds of Intelligence programme at the Leverhulme Centre for the Future of Intelligence (LCFI) at the University of Cambridge. He holds a PhD in Computer Science from the University of York and a Master's degree in Computer Science from Oriel College, Oxford. He is a Research Affiliate at the Centre for the Study of Existential Risk (CSER), Cambridge, and has contributed to the Centre for Science and Policy network. His research focuses on developing robust evaluation frameworks for AI systems, characterising their capabilities and generality, and linking behavioural evidence to safety-relevant assessment — work that directly addresses the risks posed by large foundation models. He has published extensively on AI evaluation methodology, predictable AI, human-in-the-loop frameworks, and international governance mechanisms for assessing societal-scale AI risks, with recent work appearing in Nature and the journal Artificial Intelligence.

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Carole Bibas-Barkan is a nonprofit leader focused on talent building and impact. She serves as CEO of Effective Altruism Israel and previously worked as COO of the Modern Agriculture Foundation, and she holds a cum laude degree in industrial engineering from the Technion.

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Constantin Weisser is an AI safety researcher and machine learning engineer with an interdisciplinary PhD in Physics, Statistics, and Data Science from MIT, where his thesis applied machine learning to particle physics at CERN. He participated in the MATS 6.0 program (Summer 2024), supervised by CHAI's Micah Carroll, during which he demonstrated that targeted manipulation and deception emerge in LLMs trained on user rather than annotator feedback — work that was accepted as an oral contribution at the SATA workshop and a spotlight at the SoLaR workshop at NeurIPS 2024. He received a MATS extension grant to establish a benchmark for LLMs' tendency to influence human preferences. Following MATS, he became the first technical staff member at Haize Labs, working on dynamic safety evaluations and LLM automated red teaming for frontier labs including Anthropic, OpenAI, and AI21. Prior to his AI safety work, he spent several years as a machine learning consultant at McKinsey/QuantumBlack and contributed to NASA Frontier Development Lab projects in climate forecasting and flood prediction.

Daniel Faggella is the founder and head of research at Emerj Artificial Intelligence Research, where he leads analysis on AI use cases and return on investment for large enterprises and public institutions. He is an internationally recognised speaker and advisor on the business applications and societal implications of AI, and hosts the AI in Business podcast, interviewing AI leaders from Fortune 500 companies and high‑growth startups.

Yatharth Agarwal is a member of the Softmax team with a background as cofounder of Stanford AI Group and as a screentime addiction researcher. He created the first high-school curriculum and a for-credit Stanford class on AI safety, co-taught AI ethics for faculty, led an AI regulation seminar at Stanford Law, and previously led AI at Axle Health while writing extensively on self-alignment in humans.

Founder of Ergo Impact and board member at AVERI, Max Henderson is a philanthropist and investor who has helped incubate, operate, and fund close to $1B of social good efforts across areas such as global health, science, nuclear security, and AI, after earlier product and go-to-market roles at organizations including CovidActNow, Firebase, Google, Oracle, and Compass.

Mathematician focused on bringing category-theoretic ideas into science, technology, and society; after earning his PhD from UC Berkeley in 2007 he has pursued novel mathematical research with collaborators in fields such as materials science, robotics, aeronautics, and computer science and co-founded the Topos Institute and Weve.

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Kristy Loke is an independent researcher based in Canada focusing on China's AI development and governance, Chinese political economy, and international AI governance. She was a research fellow at MATS (ML Alignment Theory Scholars) and has previously held fellowships and affiliations at the Centre for the Governance of AI (GovAI), Concordia AI, and The Future Society. She also serves as a mentor at 80,000 Hours and previously worked as an AI Governance Teaching Fellow at BlueDot Impact. She holds a BA in Philosophy, Politics, and Economics from the University of Warwick, an MA in International Relations from The George Washington University, and an MA in International and Public Affairs from The University of Hong Kong. She co-authored the 2025 Oxford Martin School policy paper "Promising Topics for US-China Dialogues on AI Safety and Governance" and contributes analysis on China's AI strategy to The Wire China.

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Lennart Heim is an independent researcher focused on semiconductors and AI policy, based in Washington, DC. He previously served as an Associate Information Scientist at the RAND Corporation, where he led the Compute Team at RAND's Center on AI, Security, and Technology and held a faculty position as Professor of Policy Analysis at the Pardee RAND Graduate School. He is an Adjunct Fellow at the Centre for the Governance of AI (GovAI), where he previously held a Senior Research Fellow role leading the Compute Governance initiative. He is also a co-founder of Epoch AI, a research organization tracking trends in machine learning compute, data, and hardware. His research centers on the role of computing power in advanced AI systems and how compute can be leveraged as an instrument for AI governance, including work on training compute thresholds, export controls, data center policy, and AI diffusion frameworks. He has a background in computer engineering from ETH Zurich and RWTH Aachen, is a member of the OECD.AI Expert Group on AI Compute and Climate, and is an active contributor to EA Forum discussions on compute governance and AI safety.

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Siméon Campos is the founder of SaferAI and now serves as an advisor and chair of the board, contributing to work on AI standards, risk management research, fundraising, and external partnerships. He is a technical expert on AI risk management at ISO/IEC and CEN-CENELEC and led the development of SaferAI’s ratings of AI developers’ risk-management maturity. Before founding SaferAI, he co-founded EffiSciences, an organisation providing responsible-AI training that has been used to train hundreds of people across France and Europe, and he is a frequent commentator on AI governance and risk management in forums such as TIME, Euractiv, OECD and Le Monde.

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Tiffany M. Moore is senior vice president of political and industry affairs at the Consumer Technology Association (CTA), where she leads federal advocacy on technology policy and oversees initiatives to expand diversity, inclusion, and innovation within the consumer technology sector.

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Joe Kwon is an AI safety researcher and policy analyst based in Washington, DC. He holds a BS in Computer Science and Psychology from Yale University and has conducted research at MIT's Computational Cognitive Science Lab, where he studied moral and social cognition with Josh Tenenbaum and Sydney Levine. His technical background includes early RLHF work at OpenAI, empirical ML research at UC Berkeley with Jacob Steinhardt and Dan Hendrycks focused on evals and out-of-distribution detection, and a stint as a Research Engineer at LG AI Research working on multilingual large language models. He subsequently transitioned to AI governance work, completing a GovAI DC Fellowship focused on risks from internal AI deployment and automated R&D, and serving as a Technical Policy Analyst at the Center for AI Policy (CAIP). Most recently he has been an Astra Fellow working with Tom Davidson and Fabien Roger on threat modeling and ML experiments related to secretly loyal AI. He received a Long-Term Future Fund grant as a stipend to work on an ML safety project with the goal of joining an ML safety team full-time.

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Kai Fronsdal is an AI safety researcher based in the San Francisco Bay Area, currently affiliated with Meridian Research Labs. He completed a masters degree at Stanford University studying mathematics and computer science, where he was also involved with Stanford AI Alignment (SAIA). He participated in MATS 6.0 (Summer 2024) under the mentorship of David Lindner, focusing on measuring instrumental self-reasoning in frontier models as a precursor to deceptive alignment. His primary research output from this period is the paper "MISR: Measuring Instrumental Self-Reasoning in Frontier Models" (NeurIPS 2024), which proposes evaluation tasks for assessing how well LLM agents can engage in instrumental self-reasoning across scenarios including self-modification and knowledge-seeking. He also contributed to Anthropic's alignment auditing tools as an Anthropic Fellow, co-authoring "Petri: An open-source auditing tool to accelerate AI safety research" and the Bloom behavioral evaluation framework, as well as AuditBench, a benchmark for evaluating alignment auditing techniques on models with hidden behaviors. He received a grant from the Long-Term Future Fund to conduct deceptive alignment evaluation research and explore control and mitigation strategies.

Deger Turan is the CEO of Metaculus, specializing in forecasting technological uncertainty and its impact on society and the environment. Before joining Metaculus as CEO in 2024, he served as President of the AI Objectives Institute, where he led development of Talk to the City, a platform that strengthens communication between under-resourced communities and the government officials serving them. Prior to AOI, he founded Cerebra Technologies, which forecasted shifts of public opinion and demand trends for hundreds of millions of citizens and was used by governments, hedge funds, and international retailers.

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Research Associate on the Frontier Security team at the Institute for AI Policy and Strategy. Previously served as Co-director of the Cambridge AI Safety Hub, where he ran the MARS (Mentorship for Alignment Researchers) programme, interned at the World Bank, and completed dual BAs in Philosophy, Politics and Economics and in Economics at Claremont McKenna College.

Condoleezza Rice is the Tad and Dianne Taube Director of the Hoover Institution and Denning Professor in Global Business and the Economy at Stanford Graduate School of Business. She is also a founding partner of Rice, Hadley, Gates & Manuel LLC, where she advises companies on international strategy, drawing on her service as the 66th U.S. Secretary of State and previously as National Security Advisor to President George W. Bush.

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David Manheim is the founder and head of research and policy at ALTER (Association for Long Term Existence and Resilience), an academic research and advocacy organization based in Israel, and a visiting lecturer at the Technion - Israel Institute of Technology. He holds a PhD in risk analysis and decision theory from the Frederick S. Pardee RAND Graduate School and an undergraduate degree in mathematics from Lander College. Before pivoting to policy research, he worked in equity derivatives risk at an investment bank and built reinsurance terrorism risk models. His research focuses on mitigating large-scale risks to humanity, with particular emphasis on AI governance and safety, biosecurity and pandemic prevention, systemic risks, and geopolitical and technological forecasting. He is widely known in the AI safety community for co-authoring the influential paper "Categorizing Variants of Goodhart's Law" with Scott Garrabrant, and for his work on multiparty dynamics and failure modes for machine learning systems. He is a Superforecaster with Good Judgement Inc. and was one of the original participants in the Good Judgement Project IARPA forecasting competition. He has received grants from the Long-Term Future Fund for research on AI risk structure and multi-model approaches to x-risk mitigation, and advises organizations including Bluedot Impact, the Foresight Institute grant program, and the Unjournal.

Michelle Yi is a technology leader specializing in artificial intelligence and machine learning, with about 15 years of experience in technology consulting and applied AI. She has served as Senior Director for Applied Artificial Intelligence at RelationalAI and later as Head of Applied AI at Able, and co-founded Generationship. Michelle serves on multiple advisory boards, sits on the board of Women in Data, volunteers with programs such as Girls Who Code, and is affiliated with Basis Research Institute in the broader AI ecosystem.

AI governance and safety researcher and policy analyst who leads the AI Risk Index at MIT FutureTech and The University of Queensland, using systematic evidence synthesis and policy engagement to inform decision‑makers about high‑priority AI risks and mitigations.

Doctoral student in Computer Science at MIT CSAIL advised by Dylan Hadfield-Menell in the Algorithmic Alignment Group, broadly interested in AI alignment and reinforcement learning, including neurosymbolic and multi-agent RL.

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Daniel Kestenholz is Chief Operating Officer at Macroscopic Ventures, overseeing the organization’s operational infrastructure after previously serving in similar roles at other nonprofits and working in civil engineering and as an executive coach.

Lola Carbonell is co-founder of the General-Purpose AI Policy Lab, where she serves as Secretary General and Head of International Affairs.

Richie Bonett is a founding member and researcher at the Berryville Institute of Machine Learning and a security engineer at Verisign, where he applies machine learning and static analysis to improve software engineering. He holds a B.S. in Computer Science from William & Mary and co-authors BIML’s work on architectural risk analysis and machine learning security.

Member of Technical Staff on Sage’s Epistemics team, contributing to Sage’s mission of building tools to make sense of the future alongside AI Digest’s interactive explainers.

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Nikiforos Pittaras is a Greek machine learning researcher and engineer currently working as an ML Research Engineer at the Digital Safety Research Institute (UL Research Institutes), where he focuses on safety evaluations of large language models. He holds a PhD in machine learning from the Informatics and Telecommunications department of the University of Athens, an MSc in Signal and Information Processing from the University of Athens, and a BSc in Computer Science from the University of Ioannina. His doctoral thesis was titled "Beyond Deep Learning: Enriching Data Representations for Machine Learning Tasks." His AI safety work includes a cooperative reinforcement learning project on detecting and penalizing betrayal patterns in self-interested agents, undertaken at AI Safety Camp 2022 and supported by the EA Long-Term Future Fund. He also served as a Teaching Assistant on Machine Learning, Deep Learning, AI Safety, and Alignment at the Center for AI Safety's ML Safety Scholars program. His broader research background spans NLP (argument mining, event detection, summarization), computer vision, audio, and multimodal data tasks.

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David “davidad” Dalrymple is a Programme Director and technical advisor at the UK’s Advanced Research and Invention Agency (ARIA), where he launched and helps lead the £59m Safeguarded AI programme on mathematically guaranteed safe AI. He previously worked on technical AI safety at Oxford’s Future of Humanity Institute, co‑invented the cryptocurrency Filecoin, and has a long‑standing research background spanning computer science, neuroscience, and formal methods.

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Co-Director of the AI Safety Initiative at Georgia Tech and computer science undergraduate minoring in Law, Science & Technology, with research interests in interpretability and technically informed AI policy that protects individuals without hindering innovation.

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Executive Director of the AI Safety Awareness Project. Previously spent five years at Bridgewater Associates’ Systemized Intelligence Lab and four years as a founding engineer at Vowel.com (acquired by Zapier). He has completed stints at the Recurse Center studying formal verification, modern AI, and AI safety, and holds an A.B. in Mathematics with a secondary concentration in Comparative Religion from Harvard University. He frequently speaks on criminal AI, law enforcement, and AI safety for public-sector and crisis-management audiences.