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Swadeep Singh is General Manager – Data Science at IndiaAI, where he leads the data science function within the Government of India’s national AI mission.

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Co-founder of Impact Ops and experienced operations professional; previously served as Head of Staff Support at Effective Ventures, Operations Specialist at the Centre for Effective Altruism, and Operations Manager at Brainlabs, and holds a first-class MA (Oxon) in Psychology and Philosophy from the University of Oxford.

Co‑founder and technology advisor at the Collective Intelligence Project, formerly a research engineer at DeepMind working on multi‑agent reinforcement learning and language models, and a researcher in technology governance with groups such as the Berkman Klein Center.

A private research university in Nashville, Tennessee, that received SFF Fairness Track funding for research related to AI fairness, algorithmic equity, and the societal implications of AI systems.

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Codruta (Coco) Lugoj is an independent AI safety researcher based in London, UK. She holds a master's degree in AI and has a background in machine learning research, with particular expertise in reinforcement learning and probabilistic and variational inference, developed during her time at Radboud University. She received Long-Term Future Fund grants in 2023 and 2024 to build alignment research engineering skills and to continue her work evaluating agent self-improvement capabilities. As part of the Axiom Futures Alignment Research Fellowship, she co-authored "Auto-Enhance: Towards a Meta-Benchmark to Evaluate AI Agents' Ability to Improve Other Agents," presented at NeurIPS 2024, which proposes a meta-benchmark for measuring the ability of LLM agents to modify and improve other agents across tasks including prompt injection resiliency, dangerous knowledge unlearning, and solving real GitHub issues. Her research sits at the intersection of AI safety evaluation and agentic AI capabilities.

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Dr Waku is a pseudonymous AI safety educator who creates YouTube videos, a Substack newsletter, and other content explaining AI alignment risks and AI security to general audiences.

Max Lamparth is a Research Fellow at the Hoover Institution's Technology Policy Accelerator at Stanford University, where he is also affiliated with the Stanford Intelligence Systems Laboratory and the Stanford Center for AI Safety. He holds a Ph.D. in Natural Sciences from the Technical University of Munich (2023) and B.Sc. and M.Sc. degrees in Physics from Ruprecht Karl University of Heidelberg. His research focuses on the security and safety of large language models through mechanistic interpretability, reward modeling, and robust evaluation, including developing model-internal interventions for failure modes such as backdoors and reward-proxy shortcuts. He created and teaches CS120: Introduction to AI Safety at Stanford, and his technical work has directly informed AI governance efforts through policy publications in outlets such as Foreign Affairs and the Carnegie Council for Ethics in International Affairs, as well as direct engagement with policymakers on AI legislation. His research has been published at NeurIPS, CoLM, FAccT, and AIES, and has received media coverage from MIT Technology Review, Axios, and New Scientist.

Viktoriya Malyasova is a machine learning engineer focused on AI alignment, based in the San Francisco Bay Area. She holds a Master's degree in Data Science from Skoltech (Skolkovo Institute of Science and Technology) and a Bachelor's degree in Mathematics from HSE (Higher School of Economics) in Russia. She has worked at FAR.AI (Frontier AI Alignment Research), a technical AI safety nonprofit, as well as at DeepSpin in Germany and MainsLab in Russia. She participated in the SERI MATS 2023 winter program as a scholar, where she focused on infrabayesianism — the mathematical framework developed by Vanessa Kosoy for reasoning under logical uncertainty and non-realizability. Prior to the SERI MATS program, she received retrospective funding for up-skilling in infrabayesianism, and she contributed to creating and testing educational exercises on inframeasure theory that were published on the AI Alignment Forum. She maintains a programming and machine learning blog at malyasova.github.io.

BlueDot Impact is a nonprofit talent accelerator that runs free cohort-based courses to train professionals in AI safety, AI governance, and biosecurity. It is the leading pipeline for building the workforce needed to safely navigate transformative AI.

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A 501(c)(3) research laboratory in Santa Monica, CA that uses neuroimaging, neuromodulation, VR/AR, and altered states to study consciousness, with an AI safety research program on preventing antisocial AI through artificial empathy.

Xin (Eric) Wang is an Assistant Professor of Computer Science at UC Santa Barbara and Director of the UCSB Center for Responsible Machine Learning, with research focusing on multimodal and agentic AI.

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EleutherAI is a nonprofit AI research institute focused on interpretability, alignment, and open-source foundation model research. It is best known for creating GPT-NeoX, the Pythia model suite, and The Pile dataset.

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Astrid Sorflaten leads operations at the Forecasting Research Institute, focusing on maximizing impact through efficient operations and project management. She previously led operations at early-stage organizations in both the nonprofit and corporate sectors, including developing systems and infrastructure for a rapidly growing team at the UK nonprofit Good Law Project.

Poseidon Research is an independent AI safety laboratory conducting deep technical research in interpretability, control, and secure monitoring to make advanced AI systems transparent, trustworthy, and governable.

Arden Berg is an Investor at Macroscopic Ventures, working on the organization’s investment portfolio with a background in economics and prior work investigating the trajectory of AI.

Patricio Vercesi is an Argentine individual based in Córdoba, Argentina, with involvement in the AI safety community. He co-authored a research project titled "Public Opinion and Its Importance to AI Policy" submitted to Apart Research as part of the EAGx LatAm Epoch AI Hackathon in July 2023. He is listed as a volunteer with PauseAI, an AI safety advocacy organization. He received a small grant from the Long-Term Future Fund to study machine learning at university and pursue AI safety independently over a 10-month period, reflecting an early-career path into AI safety work.

Conor McGlynn is a doctoral candidate in Public Policy (Science, Technology and Policy Studies) at Harvard Kennedy School and a fellow at the Harvard STS Program. He holds a B.A. in Economics and Philosophy from Trinity College Dublin and an M.Phil. in Philosophy from the University of Cambridge, where he focused on ethics and public policy. Before his PhD, he worked in EU affairs in Brussels as a Robert Schuman Trainee at the European Parliament and subsequently as a government relations consultant specializing in emerging technology and biotech regulation. He was a Schwarzman Scholar at Tsinghua University in Beijing (2019-2020), where he researched the governance of lethal autonomous weapon systems, and a Fulbright Irish Schuman Awardee at the Kissinger Institute on US-China Relations in Washington D.C. (2020-2021), focusing on global governance of emerging technologies. He was an IAPS 2025 AI Policy Fellow with a project on international technology competition, and co-authored the paper "Promising Topics for U.S.-China Dialogues on AI Risks and Governance" presented at the ACM FAccT conference in 2025. He received a grant from the Long-Term Future Fund to upskill for AI governance work before beginning his Science and Technology Policy PhD.

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Joe Collman is a Technical AI Governance Researcher at the Machine Intelligence Research Institute (MIRI), where he joined the Technical Governance Team in late 2024. He holds a Bachelor's degree in Mathematics from the University of Warwick (2000-2004). His research career in AI safety began with a focus on AI Safety via Debate and iterated amplification, including a stint as a Collaborating Researcher at OpenAI in early 2020 working specifically on AI Safety via Debate, and multiple grants from the Long-Term Future Fund supporting independent research on debate algorithms and human alignment in amplification. He has also served as Technical Lead and AI Safety cause area manager at the Stanford Existential Risks Initiative, as a Technical Generalist at the Berkeley Existential Risk Initiative, and as a Teaching Fellow for BlueDot Impact's AI Safety Fundamentals course. At MIRI, he co-authored "Existing Safety Frameworks Imply Unreasonable Confidence" (2025), arguing that current AI lab safety frameworks reflect systematic overconfidence. He is an active contributor to the Alignment Forum and LessWrong under the handle joe-collman.

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A not-for-profit policy institute that advises governments and political leaders worldwide on strategy, policy, and delivery, with a major focus on AI governance and technology adoption in the public sector.

Yashvardhan Sharma is an AI safety and policy researcher focused on compute governance, AI hardware, and model safety evaluation. He studied artificial intelligence, machine learning, and economics at Minerva University, where he led Minerva Effective Altruism and AI Safety. He is affiliated with FAR.AI, where he has co-authored research on adversarial AI safety including red-teaming studies of leading frontier models ("Illusory Safety") and jailbreak vulnerability analysis. He also co-authored "Distributed and Decentralised Training: Technical Governance Challenges in a Shifting AI Landscape," examining policy implications of shifts from centralized to distributed AI training. He previously served as Office Manager at SERI-MATS and received a stipend from the Long-Term Future Fund to research specialized AI hardware requirements for large AI training runs, mentored by Asher Brass at the Institute for AI Policy and Strategy (IAPS).

Peter McIntyre is a board member of Longview Inc. Ltd, the UK legal entity of Longview Philanthropy.

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A regularly-updated guide on how to donate most effectively to the AI safety field, structured by donation amount and time available.

Senior Advisor at Langsikt, working part-time on biotechnology and security. She is trained as a molecular biologist at the University of Glasgow, Imperial College London and the Institute of Cancer Research, where her PhD was awarded H.M. The King’s Gold Medal in 2015. She has previously been a senior adviser at the Norwegian Biotechnology Advisory Board and serves as a special adviser to the Norwegian Cancer Society.

Co-founder of Compassion Aligned Machine Learning (CaML), where she works on aligning AI systems with the welfare of non-human and other sentient beings using synthetic training data and compassion benchmarks such as ANIMA and related evaluations; previously designed security systems at Anthropic and has several years of cybersecurity and engineering experience.

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Florent Berthet is Director of Operations at CeSIA, where he oversees the organisation’s day-to-day operations in support of its AI safety research, education, and policy work.

Sabhanaz Rashid Diya is a CIGI senior fellow and founder of Tech Global Institute, a tech policy think tank focused on reducing equity and accountability gaps between technology companies and the global majority, advising governments and international organizations on AI, platform governance and digital rights.

Teun van der Weij is a Dutch AI safety researcher and Member of Technical Staff at Apollo Research in Zurich, where he focuses on evaluating AI capabilities and propensities related to scheming and AI control. He completed an MSc at Utrecht University (2022-2024) and a BSc summa cum laude at University College Groningen (2018-2021). He was a scholar in the MATS 5.0 program, where he researched sandbagging and how personas affect the cognition of language models under mentor Francis Rhys Ward. His most prominent work is the paper "AI Sandbagging: Language Models can Strategically Underperform on Evaluations," published at ICLR 2025, which demonstrated that frontier models like GPT-4 and Claude 3 Opus can be prompted or fine-tuned to selectively hide capabilities during evaluations. He also co-authored earlier work on shutdown avoidance in language models and activation steering. In addition to his research role, he co-founded the European Network for AI Safety (ENAIS) and serves on its board.

Riccardo Varenna is a founder with over nine years of experience scaling startups and has recently co-founded TamperSec, where he works on protecting hardware against sophisticated attacks.

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Kelvin Yu is a non-resident fellow at the Foundation for American Innovation whose work focuses on advancing U.S. technological and industrial policy. He previously led AI policy for the House Science Committee, completed fellowships with In-Q-Tel and Horizon Institute for Public Service, and before moving to Washington built and invested in early-stage startups in San Francisco as a founding engineer and venture investor.