Sean Richardson

PhD student in Statistics at UC Berkeley. I work on causal inference, AI evaluation, and mechanistic interpretability. Previously at UChicago (MS, advised by Victor Veitch; also a year of law school, made a pivot) and UCLA (BA Philosophy).

seanrichardson@berkeley.edu / scholar / linkedin / @bayesian_wasian

Papers

David Reber, Sean Richardson, Todd Nief, Cristina Garbacea, Victor Veitch
ICML 2025
Jacy Reese Anthis, Ryan Liu, Sean Richardson, et al.
ICML 2025 (Position Paper)
Todd Nief, David Reber, Sean Richardson, Ari Holtzman
ICLR 2026
Mark Muchane, Sean Richardson, Kiho Park, Victor Veitch
Under review
On the Significance of Softmax Geometry: Interpretability and Token Decoding
Yibo Jiang, Lin Gui, Sean Richardson, et al.
Under review

Writing

Something will go here eventually.

Readings & Recommendations

Magnus Vinding
A key part of my worldview and a challenge for us to make things better for all sentient beings.
Peter Singer
As another Singer (Isaac Bashevis Singer) wrote, "In their behavior toward creatures, all men are Nazis. Human beings see oppression vividly when they're the victims. Otherwise they victimize blindly and without a thought." We have an obligation to open our eyes to the suffering we inflict on animalkind. If you're not in the mood to read, check out the film "Dominion," which also exposes the cruelty of modern-day animal agriculture.
Robert Wright
Ostensibly a book about evolutionary psychology, but really a biography of Darwin that uses his life, marriage, and moral struggles to illustrate the very forces he discovered. A humbling read.
Ken Liu
We are entering a world with digital minds, a second species. What should we expect from this world? Pantheon is, in my opinion, the most realistic depiction of how conflict between humans and artificial intelligence may play out. Animatrix "The Second Renaissance" is also a must-watch. Let's not cultivate an adversarial relationship with the entities we are creating, for both of our sakes.
Saunders MacLane
Many people perceive math as some arcane truths delivered by the divine. MacLane illustrates how math arises from normal human activity, along with a natural affinity towards abstraction. May offer a newfound appreciation for math, without requiring you to solve any equations or do any proofs.

Other

GSI for Stat 214: Applied Statistics and Machine Learning (Bin Yu). Board member at Sentience Institute. Facilitator for the AI×Animals Fellowship. Co-organizer of the AI Interpretability Reading Group at Berkeley (with Will Fithian and Wes Holliday).