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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.
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Mathematician Researching AI Safety
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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.
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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.
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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.
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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.
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Founder and CEO of Heron AI Security Initiative, a non-profit based in Tel Aviv that bridges the gap between frontier AI models and the cybersecurity they need by connecting cybersecurity experts to high-leverage AI security work.
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Matthew Kenney is an AI researcher and the founder of Algorithmic Research Group, where he leads work on benchmarks, environments, and multi-agent systems aimed at understanding recursive self-improvement in AI. Before starting ARG he worked in machine learning roles in academia at Duke University and in industry at Apple and Alethea, focusing on applied machine learning and AI research.
Co-founder of the Buddhism & AI Initiative, previously COO of AI safety company Conjecture; he has been studying Buddhism since 2016 and has spent over four years living in monasteries between London, Canada, and India.
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Inda Harahap serves as Outreach & Communications Lead for the AI Whistleblower Initiative (AIWI) within Whistleblower-Netzwerk e.V.
Dan Valentine is a Member of Technical Staff at Anthropic, where he works on AI safety and alignment research with a focus on scalable oversight. He previously worked as a full-stack software engineer in Toronto, Canada, before transitioning into technical alignment research with support from the Long-Term Future Fund. He participated in MATS Summer 2023 (cohort 4.0) under the mentorship of Ethan Perez. During and after MATS, he contributed to research on debate as a scalable oversight method, co-authoring "Debating with More Persuasive LLMs Leads to More Truthful Answers" (ICML 2024 Best Paper/Oral), which demonstrated that LLM debate helps both non-expert models and humans answer difficult questions more accurately. He is also a co-author on "Failures to Find Transferable Image Jailbreaks Between Vision-Language Models" (ICLR 2025), and contributed to earlier work on mesa-optimization using toy models (AISC8, 2023). Prior to focusing on AI safety, he studied at Dublin City University (2009-2013) and was involved in organizing the Toronto AI Safety community.
PhD student in the Algorithmic Alignment Group at MIT advised by Dylan Hadfield-Menell, studying AI agent alignment to human preferences in open-ended settings, human–AI teaming, and decision making across NLP, vision, and robotics.
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Zershaaneh Qureshi is a podcast host and researcher associated with 80,000 Hours, where she co‑hosts episodes of The 80,000 Hours Podcast and authors problem profiles and articles on AI risk, including work on risks from power‑seeking AI systems and AI‑enhanced societal decision making. She holds a master’s degree in mathematics and philosophy from the University of Oxford, and previously worked as a researcher providing market intelligence to the global water industry before shifting her focus to AI safety and strategy.
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Research manager at ACS helping to set up and run ACS Research, with a Master’s degree in theoretical physics from Charles University and previous experience as executive director of an NGO running international physics competitions.
Secretary of the Existential Risk Observatory with a degree in Classics, working as an editor at a publishing house and a literary magazine, and active as a translator and occasional writer.
Robert Kirk is a Research Scientist at the UK AI Security Institute (AISI), where he is the acting lead of the alignment red-teaming sub-team, focusing on stress-testing model alignment to detect and understand model propensities relevant to loss-of-control risks. He completed his PhD at UCL's DARK Lab in January 2025, supervised by Tim Rocktäschel and Edward Grefenstette, with his dissertation on generalisation in LLM fine-tuning and reinforcement learning agents. Prior to his PhD he received an integrated Master's in Mathematics and Computer Science from Somerville College, Oxford, and worked as a software and infrastructure engineer at Smarkets. His research centers on generalisation in reinforcement learning, out-of-distribution robustness, AI safety and alignment, and evaluating the effects of RLHF on language model behaviour and diversity. He received a Long-Term Future Fund grant to perform human evaluations for evaluating different machine learning methods for aligning language models, and his paper "Understanding the Effects of RLHF on LLM Generalisation and Diversity" (2023) is widely cited in the alignment research community. He also contributes to the Alignment Newsletter covering interpretability and reinforcement learning, and serves as a mentor in the MATS program for the UKAISI red-teaming stream.
James is the Director of Community and Partnerships at Giving What We Can, where he helps support and grow the organisation’s network of givers. An experienced aid worker, he has led international development and humanitarian teams and from 2019 to 2024 lived and worked across Somalia, Kenya and Rwanda. He holds a bachelor’s degree in Philosophy, Politics and Economics from the University of Oxford.