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Mehmet Sencan is the CEO of Earendil, with work spanning medical devices, biosensors, nanofabrication and optics.
Haskell programmer
Iván Godoy is an AI safety researcher who received a grant to dedicate six months full-time to upskilling and AI alignment research, with a tentative focus on agent foundations. A LessWrong account under the handle "ivan-godoy" was created in September 2025, suggesting recent entry into the AI safety community. The small grant amount ($6,000 for six months of salary support) indicates Godoy is likely based in Latin America where living costs are lower. Beyond these details, limited public information is available about Godoy's background, prior roles, or research output at this time.
angel biotech investor, former quantitative trader
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Nick Hay is an AI alignment researcher and co-founder of Encultured AI, an alignment-focused startup developing platforms for AI safety experiments. He holds a PhD from UC Berkeley (2015), where he studied metalevel control under Professor Stuart Russell, applying reinforcement learning and Bayesian analysis to how agents can learn to control their own computations. Before co-founding Encultured AI, he spent five years at Vicarious AI working on AGI approaches grounded in robotics and served as a technical researcher at the Machine Intelligence Research Institute (2017-2021). In 2021 he received a $150,000 grant from the Long-Term Future Fund to design and implement simulations of human cultural acquisition as both an analog of and testbed for AI alignment, working as a visiting scholar at CHAI advised by Andrew Critch and Stuart Russell. His research interests span reinforcement learning, value alignment, and using cultural acquisition dynamics as a lens for understanding how AI systems can learn human-compatible behavior. He first engaged with AI safety upon reading Eliezer Yudkowsky's Creating Friendly AI in 2001, interning at MIRI in 2006 and attending the Singularity Summit in 2007.
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Compute Funding
1-year stipend and compute for conducting a research project focused on AI safety via debate in the context of LLMs.
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6-month stipend to work on AI alignment research (automated redteaming, interpretability)
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Research Director at the Collective Intelligence Project and a neuroscientist focused on emerging technologies and the science of collectivity, following a 15‑year academic career and leadership roles at Irrational Labs and the Social Science Observatory.
Leveraging AI to enable coordination without demanding centralization
MSCS Graduate Student at UMass
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Independent researcher trying to make the world better place through my efforts
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Emre Yavuz is Director of Programs at the Cambridge Boston Alignment Initiative, where he leads CBAI’s research fellowship portfolio. He also serves as a research volunteer with MIT’s AI Risk Initiative, applying insights from political science and macroeconomics to frontier AI governance and risk.
Michael E. Leiter is chair of the RAND Corporation’s Board of Trustees and a partner at Skadden, Arps, Slate, Meagher & Flom LLP, where he leads the firm’s national security-focused practice. He previously served as director of the United States National Counterterrorism Center (NCTC) and has held senior roles across government and the private sector dealing with national security, intelligence, and cybersecurity matters.
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Building towards a "Limited Agent Foundations" thesis on mild optimization and corrigibility
Keith Wynroe is an independent mechanistic interpretability researcher focused on attention layers in transformer models. He graduated from Trinity College, Cambridge with double first class honours and previously worked as a Research Analyst at the Forethought Foundation for Global Priorities Research, William MacAskill's global priorities research organization. He participated in the SERI MATS (ML Alignment & Theory Scholars) program, working in Lee Sharkey's stream during the Winter 2023-24 cohort, and received LTFF grants to continue independent research afterward. His research outputs include "An OV-Coherent Toy Model of Attention Head Superposition" (co-authored with Lauren Greenspan, 2023) and "Decomposing the QK Circuit with Bilinear Sparse Dictionary Learning" (co-authored with Lee Sharkey, 2024), which applies bilinear sparse dictionary learning methods to understand how query and key features interact in attention circuits.
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Nicky Pochinkov is an independent AI safety researcher and ML engineer based in Ireland, currently affiliated with Coefficient Giving. He studied theoretical physics at Trinity College Dublin and represented Ireland in both the International Mathematical Olympiad (2018) and the International Chemistry Olympiad. His research focuses on mechanistic interpretability of large language models, including machine unlearning via selective pruning, neuron modularity and specialization in transformers, and decoding residual stream activations to understand planning in LLMs. He is co-author of the paper "Dissecting Language Models: Machine Unlearning via Selective Pruning" (with Nandi Schoots, arXiv 2024). He was a scholar in the SERI MATS program working with Evan Hubinger, has served as a teaching assistant and compute infrastructure lead for the ARENA alignment research training program (versions 5.0 and 6.0), and has served as a guest lecturer for AI Safety Tokyo. He has received multiple grants from the Long-Term Future Fund totaling approximately $200,000 for independent AI safety research.
Fund a new research agenda, based on computational mechanics, bridging mechanism and behavior to develop a rigorous science of AI systems and capabilities.
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We are fostering the next generation of AI Policy professionals through the Talos Fellowship. Your help will directly increase the number of places we can offer
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An association for interdisciplinary interest in AI
Empowering everyone to detect and combat AI-generated content threats with advanced multi-modal verification tool
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An audit-grade evaluation of persistent influence, reset failure, and isolation assumptions in long-context AI systems
A trusted profession that has advocated against existential risks like nuclear war can do so again for AI — but clinicians must first be made aware of the risks
Support David Reber -9.5 months of strategic outsourcing to read up on AI Safety and find mentors
