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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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Helen Toner is an Australian researcher and AI policy expert focused on artificial intelligence, national security, and US–China relations. She serves as the interim executive director at Georgetown University’s Center for Security and Emerging Technology (CSET), which she helped found in 2019, and previously worked as a senior research analyst at Open Philanthropy. Toner sat on the board of OpenAI from 2021 to 2023 and has been recognized as one of TIME’s 100 most influential people in AI.
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Does harmful fine-tuning data cause broad misalignment only when the model already recognises the target behaviour as a norm violation?
CEO \u0026 co-founder at FAR AI, a trustworthy AI non-profit. PhD AI UC Berkeley 2022; LTFF fund manager 2020-2022
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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.
Research on safe, trustworthy, and verifiable AI systems.
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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Producing video content on AI alignment
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.
Master student in AI Ethics and Society at University of Cambridge; Thinking about mechanistic interpretability, neuroscience, ethics, and human-machine interaction.
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A bi-weekly newsletter by Concordia AI covering technical AI safety research, governance, and policy developments in China, aimed at bridging the knowledge gap between China's AI safety ecosystem and the global community.
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Stanford University's interdisciplinary research center tackling critical security challenges, including AI governance, nuclear risk, biosecurity, and emerging technology policy.
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Public Speaker, Author, Social Worker, Researcher, Systems Engineer
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.
4-month stipend to study refusals and jailbreaks in chat LLMs under Neel Nanda as part of the MATS 5.0 extension program
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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.
Mallory Strawn is Chief Operating Officer at TechCongress, overseeing the organization’s day‑to‑day operations. She is a seasoned professional with more than 12 years of experience in the tech industry focused on operations and knowledge management, holds a BBA from Georgia State University, and has supported numerous startups, SaaS companies, and nonprofit organizations.
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6-month salary for Fabian Schimpf to upskill into AI alignment research and conduct independent research on limits of predictability
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