Database

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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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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.

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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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Yale University is a private Ivy League research university in New Haven, Connecticut, home to several AI safety and governance research programs, including the Schmidt Program on AI and National Power, the Center for Algorithms, Data, and Market Design (CADMY), and the Digital Ethics Center.

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Amy Labenz serves on LASST’s board of directors and works as a director at the Centre for Effective Altruism, where she has previously served as general counsel. Before joining CEA she worked as a civil rights attorney in Detroit and as chief compliance officer and chief operating officer at the Machine Intelligence Research Institute, and she is a graduate of New York University School of Law.

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Brad Rogers Carson is an American lawyer and public servant who represented Oklahoma’s 2nd congressional district in the U.S. House of Representatives from 2001 to 2005 and later served as General Counsel and then Under Secretary of the Army, as well as Acting Under Secretary of Defense for Personnel and Readiness. He subsequently became the 21st president of the University of Tulsa and has since moved into leadership in AI policy advocacy and governance.

Bryce Meyer is a software engineer and the primary maintainer of TransformerLens, the leading open-source library for mechanistic interpretability research on GPT-style language models. TransformerLens was originally created by Neel Nanda and allows researchers to load 50+ open-source language models and inspect their internal activations, making it the de facto standard tool for mechanistic interpretability work at organizations including Anthropic, Meta Research, Redwood Research, and Apollo Research. Meyer has maintained the library with a track record of consistent contributions, rapid iteration to support newly released models, and active community support via a weekly live-coding stream in the Open Source Mechanistic Interpretability Slack. He received a $50,000 grant from the Long-Term Future Fund in 2023 to build and enhance open-source mechanistic interpretability tooling, followed by a $90,000 year-long LTFF stipend to serve as TransformerLens's primary maintainer. He is also the president of Pomelo Productions, an independent software development studio based in Milwaukee, Wisconsin, and a self-taught developer with many years of professional engineering experience.

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Independent AI safety researcher investigating structural failure modes in agentic systems, including logic-layer escapes and governance enforcement. Developer of EasyStreet / AEGIS-ALD-W1, a deterministic, audit-grade evaluation framework for AI agents.

Co-founder and CEO of Noya, a direct air capture company aiming to reverse climate change by pulling CO2 from the atmosphere; he studied Chemical‑Biological Engineering at MIT and previously worked as a project manager at Tesla and Harley‑Davidson.

AI alignment researcher who has collaborated with Orthogonal on the QACI formal-goal alignment agenda, co-authoring the “formalizing the QACI alignment formal-goal” article.

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JJ Hepburn is the founder of Ashgro and previously worked as a facilitator at AI Safety Camp; he has training in machine learning with TensorFlow on Google Cloud Platform and studied at Macquarie University and the Australian National University.

A remote, non-profit research group focused on mechanistic interpretability of deep learning models, developing causal abstraction frameworks, open-source course materials, and mentorship programs for the AI safety community.

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An Ivy League research university in Philadelphia with multiple programs relevant to AI safety, including formal verification of autonomous systems, AI governance research, and AGI international security analysis.