Meet the Principles of Intelligence
Organizing Team
Coming from an interdisciplinary background, Lucas spent time studying Philosophy, Anthropology and Computer Science before fully devoting themselves to the alignment problem. As the PIBBSS program lead their time is mostly divided between providing technical support for the various research projects at PIBBSS, and gleaning insights from the history and philosophy of science to support pluralistic and non-paradigmatic research practices. Prior to joining the PIBBSS team, they worked at Conjecture as an Applied Epistemologist and Research Engineer.
Lucas Teixeira
Executive Director, RESEARCH
Dušan is a professor of Finance and Economics at Emlyon Business School and a consultant in the fields of Communication, Operations, and Education. He is a systems thinker with a passion for making the world a better place. In the past, he has volunteered with AIESEC, and he is the President of the Rotary Club Belgrade-Dedinje Belgrade. He has founded EA Serbia and SpEAk and coaches EA Organizations on how to communicate their knowledge efficiently.
Dušan D. Nešić
Executive Director, OPERATIONS
Research Team
After completing a PhD in physics, Lauren taught at NYU before pursuing independent research in AI alignment. She is interested in the evolution of scientific cultures and shaping the emerging field of AI safety through cross-disciplinary collaborations. At PIBBSS, Lauren will draw from her interdisciplinary background in physics, ML, and STS to contribute to technical work, field building, and establishing better AI safety research practice.
Lauren Greenspan
Technical Director
Dmitry Vaintrob is an AI safety and interpretability researcher. He has a mathematics background, having studied interactions between algebraic geometry, representation theory and physics. He is now using physics ideas to understand the geometry of representation abstractions in AI, and desperately trying to avoid the siren song of applying theoretical formalism to concrete problems.
Dmitry Vaintrob
Technical Director
[Affiliate Jun 2025-Present] I’m currently working to close the gap between theories of neural representations and practical interpretability research. My academic background is in mathematical game theory applied to political economy, which provides a unique foundation for AI interpretability/alignment. Both fields require reasoning about complex systems that are difficult to experiment on directly—particularly in AI alignment, as super-intelligence doesn’t exist yet. I believe better interpretability will ultimately yield more robust methods for auditing and reshaping AI behavior.
Andrew Mack
Researcher
[Affiliate Jun 2025-Present] I’m an AI safety researcher working to improve humanity’s scientific knowledge of advanced AI systems. My research focuses on creating mathematical and empirical models to study how AI systems develop internal representations of the world. Currently, I’m investigating how data models that exhibit critical phenomena and scale-free structure can be applied to improve AI interpretability tools. My background is in experimental high-energy astrophysics, where, among other things, I developed deep learning methods and statistical models to better understand the variation of gamma-ray emission powered by supermassive black holes.
Ari Brill
Researcher
[Affiliate Jun 2025-Present] I’m interested in both the philosophical and practical aspects of AI safety. Currently, I’m exploring whether physics-inspired theories of deep learning can be used to generate concrete algorithms for mechanistic interpretability. Previously, I worked in theoretical physics, which I maintain an interest in.
Jennifer Lin
Researcher
I am Nischal, a PhD student in theoretical neuroscience at the Hebrew University of Jerusalem interested in mathematical theories of brain functions. I’m curious if and how we can use tools and ideas from neuroscience to understand AI.
Nischal Mainali
Researcher
Board members
Alexander likes to think about philosophical questions with a mathematical lens:
‘What are abstractions and are they convergent’? What are good frameworks to think about minds and goal-directed behaviour? What are the atomic units of computation? How do symbols acquire meaning?
Alexander directs academic outreach at Timaeus, the singular learning theory alignment org, organizing a number of workshops on singular learning theory, computational mechanics and agent foundations in the context of AI alignment. He is also a sometimes PhD candidate at University College London working on theory of computation.
Alexander Gietelink Oldenziel
BOARD MEMBER & MENTOR
Gabriel is an Assistant Professor at Touro University Law Center. Prior to joining the Touro faculty, he was a research manager at the Climate Leadership Council and a Law Clerk at the White House Council on Environmental Quality. He was a PIBBSS fellow during the summer of 2023 when he wrote a paper on the role that tort law can play in mitigating catastrophic AI risk. Gabriel’s work has received coverage in Vox’s Future Perfect, appeared in Lawfare, and been featured on the AI X-Risk Research Podcast. You can follow his work on SSRN and X (formerly Twitter).
Gabriel Weil
BOARD AND ALUMNI
Nora works as Technical Specialist for the Safeguarded AI programme at the UK’s Advanced Research & Innovation Agency. She co-founded and directed PIBBSS until Spring 2024, and continues her support as President of the Board. She has pursued various research and field-building efforts in AI safety for the last >6 years. Her research background spans political theory, complex systems and philosophy of science. She is a PhD student in Philosophy and AI and a Foresight Fellow. Her prior experience includes work with the Future of Humanities Institute (University of Oxford), the Alignment of Complex Systems research group, the Epistemic Forecasting project, and the Simon’s Institute for Longterm Governance.
Nora Ammann
BOARD & CO-FOUNDER
Xuan is a PhD candidate with the MIT Probabilistic Computing Project and the Computational Cognitive Science research group. Their research focuses on efficient inference over Bayesian models of human decision-making and normativity more broadly, with an eye towards inferring human goals, values, and norms. To that end, they are broadly interested in inter-subjective accounts of human normativity (what do people agree upon as right?) and metaphysics (how do people develop shared/contested conceptual representations of the world?), and how they can be formalized to a sufficient degree that they can serve as targets for AI alignment.