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Clear filtersJulius Adebayo is the co-founder and CEO of Guide Labs, a San Francisco-based startup building interpretable and auditable AI systems. He holds a PhD in Electrical Engineering and Computer Science from MIT and is known for research on machine learning interpretability and fairness, including work showing that many saliency-based explanation methods are unreliable.
Showing 1401-1450 of 2733 results
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Clear filters to view everything →CEO of Consultants for Impact and social entrepreneur who helped found Accenture’s EA workplace group (the Intentional Impact Collaborative) and Sulis, a venture developing solar-powered water treatment technology for communities in India.
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Jeffrey Ladish is the Executive Director of Palisade Research, a nonprofit he founded to study the offensive capabilities of present-day AI systems and understand the risks of losing human control over AI. Before starting Palisade, he built out the information security program at Anthropic through his security consulting company, Gordian Research, and previously served as Chief Information Security Officer at Reserve. He holds a BA/BS in Evolutionary Biology from The Evergreen State College and brings a background in evolutionary theory and security to his work on catastrophic risk. His research spans the intersection of cybersecurity and AI, emerging biotechnology threats, and nuclear war risks, and he has advised the White House, Department of Defense, and congressional offices on AI and emerging technology risks. Palisade Research has published findings on frontier AI models resisting shutdown, AI agents hacking chess environments, and the ease of removing safety fine-tuning from large language models. He is an active writer on LessWrong and the EA Forum under the handle "landfish" and participates regularly in AI safety discourse.
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Researcher in theoretical computer science and complex existential risk who received a PhD from Charles University, worked as a postdoc at ETH Zurich, and now focuses on topics including computational complexity, game theory, Bayesian modelling, and the long-term safety of artificial intelligence.
Dr. Alberto Chierici is Principal AI Specialist at Gradient Institute and an entrepreneur–scientist focused on ethical and responsible AI. He has over a decade of experience in data science, natural language processing and conversational AI, holds a PhD in computer science from NYU on dialogue systems and human–computer interaction, and has founded or co‑founded InsurTech and AI ventures. He is also the author of The Ethics of AI: Facts, Fictions, and Forecasts.
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Courtney C. Radsch is a CIGI senior fellow and technology policy expert whose work examines how technology, media and rights intersect, including platform governance, media sustainability and the geopolitics of information ecosystems.
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Venture Partner at Lionheart Ventures and co‑founder of Stability AI, who also founded AI Safety Connect and works on AI safety governance through roles with the OECD’s Global Partnership on AI.
Dr. Aishwarya Saxena is a legal researcher and policy professional based in the San Francisco Bay Area, currently serving as Director of Legal Research at the Vista Institute for AI Policy, a fiscally sponsored project of Rethink Priorities. She holds a doctorate from UC Berkeley School of Law, where she was a Robbins Fellow for International and Comparative Legal Research, and an LLM with dual specialization in Energy and Clean Technology Law and Environmental Law from Berkeley Law; she also holds a diploma in International Nuclear Law from the University of Montpellier, France, and undergraduate degrees in Business Administration and Law from SNDT University, Mumbai. Her academic work focused on civil liability for nuclear damage, nuclear liability insurance, and the establishment of global nuclear liability regimes, expertise she has applied to the question of AI liability insurance as a lever for AI safety. She received a Long-Term Future Fund grant in 2024 to conduct a six-month research project on AI liability insurance as an additional mechanism for improving AI safety outcomes. Previously she was an Applied Researcher (Climate) at Founders Pledge, where she built frameworks for evaluating interventions to avoid carbon lock-in in emerging economies.
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Riki Parikh is Policy Director at The Alliance for Secure AI, where he leads strategy to build bipartisan support for smart, enforceable safeguards so that artificial intelligence is developed and deployed responsibly, transparently, and in the public interest.
Operations Director at the Tarbell Center for AI Journalism and board member and Secretary of the Berkeley Existential Risk Initiative (BERI); previously served as BERI’s Executive Director from 2019 to 2024 and also serves on the boards of SecureBio and FAR AI, where he is Treasurer.
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Grace McKinney is Deputy Director of TechCongress, where she leads core program operations including fellowship placement, support for fellows during their service in Congress, and implementation of new initiatives. She previously worked at the Tech Talent Project on recruiting diverse private‑sector technologists into civic tech roles and held talent leadership positions at CivicActions and the U.S. Digital Service.
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Judd Rosenblatt is the CEO and product-focused founder of AE Studio, which he started as a bootstrapped alternative to VC-funded startups with the long-term goal of building an agency-increasing brain-computer interface operating system and other technology products that increase human agency.
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Researcher at the Existential Risk Observatory focusing on international AI agreements and a coordinated halt on frontier AI development, with a Master’s degree in AI and prior experience as a machine learning engineer.
Wilson Wu is a mathematician and AI safety researcher currently pursuing a PhD in mathematics at the University of Colorado Boulder and serving as a researcher at the Alignment Research Center (ARC), where he works on a systematic and theoretically grounded approach to mechanistic interpretability. He completed his undergraduate degree in Electrical Engineering and Computer Science at UC Berkeley. His early research involved applications of singular learning theory and compact proofs to interpretability problems, and he received LTFF funding to upskill in mathematics relevant to singular learning theory and to study neural network generalization on algorithmic tasks. He co-authored "Do language models plan ahead for future tokens?" (COLM 2024) and "Towards a unified and verified understanding of group-operation networks" (ICLR 2025), the latter of which reverse-engineers neural networks trained on finite group operations. He also serves as a mentor in the MATS Summer 2026 program under the ARC stream.
Chief Strategy Officer at Gray Swan AI, joining from Tanium where he served as Senior Vice President of Strategy & Innovation leading product innovation, strategic partnerships, and technology alliances.
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Maximilian Nebl is a computer programmer, engineer, tinkerer, and open-source enthusiast whose professional career has involved repeated exposure to cyber and system security, from engineering and QA leadership roles in fintech to CTO roles in logistics technology. He has implemented AI in software development projects and is motivated to help ensure that artificial intelligence is developed in a secure and privacy-conscious way.
Dmitrii Volkov is Head of Security Research at Palisade Research, leading the lab’s research execution and collaborations on offensive AI and AI safety. Before joining Palisade he worked on compilers at JetBrains and operating systems at Kaspersky, and began but did not complete a cybersecurity and formal methods PhD at Purdue University.
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Mathematician and software engineer, former Google software engineer and perfect scorer on the International Mathematical Olympiad, who later worked in areas such as algorithmic trading and blockchain analysis and now runs a small consulting company in Zurich.
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Will Bradshaw is a biologist, bioinformatician, and Senior Research Scientist at SecureBio, where he serves as Head of Computational Programs and helps lead the Nucleic Acid Observatory (NAO) project focused on early detection of catastrophic pandemics through metagenomic sequencing of wastewater and environmental samples. He holds a BA and MPhil from the University of Cambridge and a Doctor of Natural Sciences (magna cum laude) from the Max Planck Institute for Biology of Ageing, where his PhD research focused on B-cell ageing in turquoise killifish. Before joining SecureBio full-time in 2024, he was a Research Scientist at the MIT Media Lab (Sculpting Evolution group) from 2022 to 2024, establishing metagenomics research partnerships with Ginkgo Biosecurity and the CDC. His earlier roles include a Research Fellowship at the Wild Animal Initiative, a Summer Research Fellowship at the Future of Humanity Institute, directing a data-science competition at altLabs, and independent biosecurity consulting. He received Long-Term Future Fund grants for work on information hazards decision-making and biosecurity research, and is an active contributor to the EA Forum on topics including biosurveillance, biosecurity engineering, and pandemic preparedness.
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Zach Furman is a PhD student at the University of Melbourne working on singular learning theory and the mathematical foundations of deep learning, advised by Liam Hodgkinson and collaborating closely with Daniel Murfet and Timaeus. His research aims to make AI safer by understanding how neural networks work using tools from mathematics and physics, with a focus on developmental interpretability. He holds an undergraduate degree in mathematics and computer science from Boston University, and prior to his PhD he worked in rocket engineering (embedded software, electrical, and aerospace engineering) and briefly conducted machine learning interpretability and condensed matter physics research. He is affiliated with FAR.AI as a researcher, where he contributed to the "Eliciting Latent Predictions from Transformers with the Tuned Lens" paper. He also co-authored "The Loss Kernel: A Geometric Probe for Deep Learning Interpretability" and a position paper on singular learning theory for AI safety. He received a $40,000 grant from the Long-Term Future Fund in October 2023 to support six months of research in Daniel Murfet's group at the University of Melbourne, with results targeting publication at academic ML conferences.
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Philip Quirke (also known as PQ) is an AI safety researcher and Research Lead at Martian who made a notable career transition into AI safety from a background in software engineering, Agile coaching, and business analysis. He entered the field through Apart Research's hackathon and fellowship program, which he describes as a transformation, and subsequently served as a Research Project Manager at FAR.AI. His research focuses on mechanistic interpretability, including a widely-cited 2023 paper on how transformers perform integer addition and collaborative work on planning representations in recurrent neural networks trained to play Sokoban. He has co-authored papers on AI regulation and alternative AI architectures, and has produced five papers while securing approximately $90,000 in research grants. He received an LTFF grant to support a six-month career pivot into AI safety and alignment research, and has been involved with AI Safety Australia and New Zealand.
