Database

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.

7-month stipend for organising AI Alignment Irvine (AIAI)

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.

No summary available yet.

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.

No summary available yet.

No summary available yet.

No summary available yet.

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.

No summary available yet.

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.

No summary available yet.

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.

No summary available yet.

No summary available yet.

No summary available yet.

No summary available yet.

No summary available yet.

No summary available yet.

No summary available yet.

No summary available yet.

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

No summary available yet.

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.

No summary available yet.

No summary available yet.

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.

No summary available yet.

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.

No summary available yet.

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.

No summary available yet.

No summary available yet.

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.

No summary available yet.

Stanford University's interdisciplinary research center tackling critical security challenges, including AI governance, nuclear risk, biosecurity, and emerging technology policy.

No summary available yet.

No summary available yet.

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