Ph.D. student in Computer Science · UCLA

Fred Xu

I'm a Ph.D. student in Computer Science at UCLA, researching probabilistic generative modeling, graph learning and uncertainty quantification, with applications to finance and science. Here I write about philosophy, literature and AI, about film, and about my research.

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Philosophy · AI · Education · Flow The Virtue of FrictionSome friction only wastes our time, and some is how understanding is built and how hard work becomes absorbing. On learning, skill and flow when a machine can do the work. October 17, 2026 · 46 min read

Philosophy, Literature & AI The Philosophy of BoxingWhy would anyone choose to be hit in the head? Freud's death drive, Musashi's sword after the wars, and what boxing taught me about discipline, fear and flow.

Philosophy, Literature & AI Reflection on my First Boxing Bout - A Warrior's JourneyWhat I learned from losing my first boxing match: sacrifice, the question of why, a sunrise on a trail, and fighting as a spiritual practice.

Philosophy, Literature & AI Jim Simons, A TributeA tribute to Jim Simons, mathematician, codebreaker, investor and patron of science, and an inspiration of mine: on beauty, usefulness and what wealth can do for curiosity.

Film The Hermeneutics of Samurai Culture in Film and TelevisionBefore my first boxing match I read Hagakure. Reading it again through Kurosawa, Kobayashi, Mishima, Jarmusch and Samurai Champloo, I find in the samurai ideal a modern invention and a masculine death drive, and in the best films its undoing.

Film Kwaidan: Strange Tales of Nature and WarMasaki Kobayashi's Kwaidan is usually filed under horror. Seen on a big screen, its four ghost stories read better as mysteries: about a nature full of spirits, and about the dead of a lost war.

Selected papers

NeurIPS 2026
Improving Function Space Flow Matching with Kernel Optimal Transport
Fred Xu, Thomas Markovich, Barbora Barancikova, Yizhou Sun
arXiv
NeurIPS 2026
Graph Anomaly Detection as Finite-Horizon Control: Training-Free Scoring via Empirical Bayes
Fred Xu, Thomas Markovich, Florence Regol, Yizhou Sun
arXiv
NeurIPS 2026
A Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion
Fred Xu, Thomas Markovich, Florence Regol, Yizhou Sun
arXiv
KDD 2026
FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation
Haixin Wang, Ruoyan Li, Fred Xu, Fang Sun, Kaiqiao Han, Zijie Huang, Ching Chang, Xiao Luo, Wei Wang, Yizhou Sun · Co-first author
arXiv
NeurIPS 2025
Uncertainty Estimation on Graphs with Structure Informed Stochastic Partial Differential Equations
Fred Xu, Thomas Markovich
arXiv
Findings of ACL 2024
FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion
Fred Xu, Song Jiang, Zijie Huang, Xiao Luo, Shichang Zhang, Adrian Chen, Yizhou Sun
OpenReview arXiv