Alex Infanger is an independent AI safety researcher based in the San Francisco Bay Area. He completed his PhD in 2022 at Stanford University's Institute for Computational and Mathematical Engineering (ICME), where he studied theory and algorithms for Markov chains. He transitioned into AI safety and alignment research, receiving Long-Term Future Fund grants for upskilling in deep learning and working on automated red-teaming and interpretability. He was a MATS (Machine Learning Alignment Theory and Surveys) Fellow, and his research has spanned machine unlearning robustness, sparse autoencoders and superposition, and reward misspecification. Notable works include "Distillation Robustifies Unlearning" (NeurIPS 2025 spotlight), "Misalignment from Treating Means as Ends" (arXiv 2025), and "Eliciting Language Model Behaviors using Reverse Language Models" (NeurIPS SoLaR Workshop 2023 spotlight). He also facilitated AGI safety fundamentals reading groups with the MIT AI Alignment Team in Fall 2022.

Co-founder of EquiStamp, a third-party evaluator that provides objective evaluations of frontier language-model systems and has served as a key contractor to METR on projects such as RE-Bench.

Managing Director at the Center on Long-Term Risk, where he leads work on reducing s-risks from powerful AI systems. Previously a researcher at CLR focusing on s-risk macrostrategy, he also led the community-building team and studied mathematics at the University of Cambridge and the University of Warwick.

Alexander Lintz (also known as Alex Lintz) is an AI governance researcher and entrepreneur based in Washington, DC. He is a co-founder and AI governance advisor at the Institute for AI Policy and Strategy (IAPS) and has helped launch several organizations in the AI safety and governance space, including The AI Governance Archive (TAIGA), the AI Safety Communications Centre (AISCC), and the Long-term AI Strategy Retreat (LAISR). In 2022, he co-organized LAISR in Washington, DC, a longtermist AI strategy retreat for approximately 35 researchers and practitioners, alongside Ashwin Acharya of Rethink Priorities. He has served as an affiliate and contractor for Rethink Priorities' AI Governance & Strategy team and has contributed feedback to foundational AI governance research including work by Allan Dafoe. He received grants from the Long-Term Future Fund for independent distillation and coordination work in the AI governance and strategy space, and earlier for organizing a career-focused workshop for European effective altruists interested in AI governance careers, which he ran with collaborators from EA Zürich. He is active on the EA Forum (handle: LintzA) where his posts on AI governance and democratic politics have received substantial engagement.

A nonprofit that archives humanity's ideas, ideologies, and world-views through structured debate mapping, with a focus on AI safety, alignment, and democratic governance of AI.

No summary available yet.

Kelsey Piper is an American journalist and staff writer at The Argument who previously wrote for Vox’s Future Perfect column, covering global challenges and catastrophic risks from an effective altruism perspective.

Malcolm Murray is Research Lead at SaferAI, where he heads work on quantitative risk assessment for large language models and advanced AI risks such as cybersecurity and biosecurity. He is an AI risk management expert with over two decades of experience in risk and strategy, previously serving as Chief of Research for Risk and Audit and Managing Vice President at Gartner and advising CEOs, prime ministers, and chief risk officers across the US, Europe, and Asia. He holds an MBA from INSEAD, a M.Sc. in Business and Economics from the Stockholm School of Economics, and a MIM from HEC, is a Good Judgment Project Superforecaster, and has been a CFA charterholder.

AFFINE (Agent Foundations FIeld NEtwork) runs intensive superintelligence alignment seminars and fellowships to upskill promising newcomers in agent foundations and AI alignment research.

Garrett Baker is an independent AI alignment researcher based in Berkeley, CA. He works on using singular learning theory (SLT), neuroscience, and reinforcement learning to build mathematically grounded theories for how values develop during training in ML systems. He has participated in the MATS program twice — as a MATS 3.0 scholar working on mechanistic interpretability of maze-solving agents under Alex Turner, and in the MATS 5.0/5.1 developmental interpretability stream — and has received funding via Manifund for both a MATS stipend and a full-time research salary. His research investigates epoch-wise critical periods in neural networks through an SLT lens, explores connections between ML inductive biases and neuroscience, and aims to create training stories that could produce inner-aligned AI. He is an active contributor to LessWrong and the AI Alignment Forum under the handle d0themath, with over 77 posts and 6,600 karma.

No summary available yet.

No summary available yet.

No summary available yet.

No summary available yet.

Project manager at 1Day Sooner. Focused on biosecurity and policy.

No summary available yet.

No summary available yet.

Yaya Shi is the Lab Manager at the Institute for Advanced Consciousness Studies and a mental health clinician in training. Her interests span clinical and social neuroscience, particularly the neural and psychological mechanisms underlying attachment, grief, emotional resilience, and transformative emotional states, and she is motivated to translate consciousness research into accessible clinical tools and public engagement.

Cameron King is Operations Lead at Animal Advocacy Africa, having moved from running an e-commerce business to charity entrepreneurship and remaining active in the effective altruism community for over a decade.

No summary available yet.

No summary available yet.

Mateusz Bagiński is a Polish AI safety researcher currently based in Tallinn, Estonia. He holds a BSc and MSc in cognitive science, and previously worked as a programmer at a startup developing software for enhancing collective sense-making. He transitioned into technical AI safety research after completing his dissertation, receiving a Long-Term Future Fund grant to skill up and gain experience working on AI safety full-time. In 2024, he was a PIBBSS Fellow mentored by Tsvi Benson-Tilsen (ex-MIRI), where he conducted a conceptual investigation of the core drivers of goal-achieving mental activity using the hermeneutic net method, presenting preliminary results at the PIBBSS Symposium '24 under the title "Fixing our concepts to understand minds and agency." His research focus is on theoretical and agent foundations work. He is active on LessWrong and the EA Forum, and has co-authored posts on AI safety policy including arguments for why safety-concerned researchers at capabilities labs should speak out publicly. He is the organizer of the AFFINE Superintelligence Alignment Seminar, a five-weekend intensive program in Hostačov, Czech Republic bringing together approximately 35 participants with leading mentors in the field.

Canada's national AI safety institute, established by the federal government in November 2024 to advance the science of AI safety and ensure governments can understand and act on risks from advanced AI systems.

No summary available yet.

Jeyashree Krishnan (JK) is a researcher at Apart Research, works on generative AI products in Siemens Corporate IT, and is a researcher at RWTH Aachen’s Center for Computational Life Sciences, with expertise spanning interpretability, AI safety and risk, time series modelling, and computational biology.

Clare Diane Harris is a Research Associate at Macroscopic Ventures, where she researches societal long-term risks; she is a medical doctor who now primarily conducts non-clinical research for organizations aiming for positive social impact.

Suren Pahlevan is a PhD student in the Faculty of Music at the University of Cambridge and a Student Fellow at the Leverhulme Centre for the Future of Intelligence. His ethnomusicological doctoral research, funded jointly by the Arts and Humanities Research Council and the Isaac Newton Trust, examines how British producers in genres such as pop, hip‑hop, R&B and EDM are incorporating AI tools into digital audio workstation production and what this implies for the design of ethical music‑AI systems.

No summary available yet.

No summary available yet.

Fabian Schimpf is an independent AI alignment researcher based in Stuttgart, Germany, supported by a grant from the Long-Term Future Fund. He received the grant to upskill into AI alignment research and conduct independent research on the limits of predictability, with mentorship from Andrea Iannelli at the University of Stuttgart. His research focus is on improving robustness in deep learning and using insights from that field to advance interpretability as a path toward ensuring AI robustly benefits humanity. He has a background in aerospace engineering from the University of Stuttgart, where he worked on autonomous soaring and asteroid exploration at the Flight Mechanics and Controls lab and completed an internship at NASA. He has contributed to approximately ten publications spanning aerospace and machine learning topics. He is active on LessWrong and the AI Alignment Forum under the handle 'fasc', where he has written on robustness in AI alignment and co-authored work on negative side effect minimization as part of an AI Safety Camp project.

No summary available yet.

Ryan Khurana is a senior fellow at the Foundation for American Innovation and an AI practitioner based in Toronto. He has helped launch and lead AI products at companies including WOMBO and Maple Leaf Sports & Entertainment and now leads agentic applications at TwelveLabs, alongside prior research and policy roles with organizations such as the Macdonald-Laurier Institute and the Consumer Choice Center.

No summary available yet.

Naoya Okamoto is an early-career researcher exploring AI safety and alignment, based in the United States. They graduated from Fordham University in 2023 and have a background in proof-based mathematics. Inspired by Brian Christian's book The Alignment Problem, Okamoto pursued upskilling in machine learning through the University of Illinois Urbana-Champaign's Mathematics of Machine Learning course in summer 2023, funded by a Long-Term Future Fund grant. After exploring theoretical alignment research, they shifted focus toward empirical alignment research, working through the MLAB curriculum in 2024. They have also interned at the U.S.-Japan Council and volunteered with the Human Restoration Project, a progressive education nonprofit. Outside of AI safety, they are interested in AI policy advocacy and biosecurity.

No summary available yet.

Aleena Khan is Senior Outreach & Program Manager at TechCongress, where she leads fellowship recruitment and selections. Previously she served as Deputy Director for Content at TEDxFoggyBottom and as a Research Assistant at the Institute for International Economic Policy, supporting research on data and ethics. Aleena holds a B.A. in Political Science with a focus in Public Policy from The George Washington University and is pursuing a Master of Public Administration at American University.

Dr Keegan McBride is Director of Science & Technology at the Tony Blair Institute for Global Change, leading work on AI, digital government and technology policy, including how states can harness emerging technologies to improve competitiveness and public services. Previously he was a departmental research lecturer in AI, Government and Policy at the Oxford Internet Institute, where his research examined digital government, AI in the public sector and the future of the state in the digital age.

AI safety organiser and writer based in Sydney who co-founded AI Safety Australia and New Zealand, has been involved in the AI safety space for more than half a decade, leads local movement-building in Australia and New Zealand, and has experience including a summer fellowship with the Stanford Existential Risk Initiative, facilitation for BlueDot Impact and the Center for AI Safety, and organising the Sydney AI Safety Fellowship.

Chu Chen is an AI safety researcher who has been involved with the ML Alignment & Theory Scholars (MATS) program. In Q4 2022, they received a Long-Term Future Fund grant of $96,000 covering one year of salary to support upskilling in technical AI alignment research, indicating a career transition into the field. Their LinkedIn profile lists MATS as an affiliation, suggesting participation in the MATS scholar program as part of this transition.

No summary available yet.

No summary available yet.

A leading Canadian research university founded in 1957, home to AI safety-relevant research programs including technical AI safety grants from Coefficient Giving and CIFAR's Canadian AI Safety Institute program.

No summary available yet.

ControlAI is a nonprofit advocacy organization working to keep humanity in control of advanced AI by pushing governments to prohibit the development of artificial superintelligence.

AI Safety Hungary is a Budapest-based nonprofit that runs educational programs and career support to help Hungarian students and professionals enter the AI safety field.

Daniel Schwarcz is the Fredrikson & Byron Professor of Law and a Distinguished University Teaching Professor at the University of Minnesota Law School. His scholarship focuses on insurance law, financial regulation, consumer protection, and the growing role of artificial intelligence in legal practice and legal education, and he has led multiple empirical studies on how generative AI affects legal work and legal training.

No summary available yet.

Shavindra Jayasekera is a PhD student in the Modern Statistics and Statistical Machine Learning CDT programme at Imperial College London, which he joined in October 2023. He completed his undergraduate and master's degrees in mathematics at the University of Cambridge, specialising in statistics, and was awarded high distinction for his Part III Essay on Generative Adversarial Networks in Biomedical Imaging. He then received funding from the Long-Term Future Fund to complete a one-year master's in computational statistics and machine learning at University College London. His doctoral research at Imperial focuses on improving the explainability, interpretability, and reliability of foundational models, with additional interests in machine learning safety and applications in chemistry. He was a co-author on the paper "Variational Uncertainty Decomposition for In-Context Learning" presented at NeurIPS 2025, and was selected as one of six finalists for the IMA Lighthill-Thwaites Prize in 2025.