Curriculum Vitae

Employment & education

Building intelligent dynamical systems: dynamics for AI, AI for dynamics, and the foundations of physical AI and learning.

Host: Yuanzhao Zhang. Two underlying principles for sequential prediction from recurrent models and transformers.

Supervisor: Dr. Danielle S. Bassett. Artificial intelligence; nonlinear dynamical systems; cognitive computational neuroscience.

Supervisor: Dr. Edward Ott. Dissertation: "Dynamics of large systems of nonlinearly evolving units."

Research software

I develop recurrent-model research software and specialized GPU implementations, connecting dynamical theory with efficient computation.

Selected mentoring

At the University of Pennsylvania, I co-mentored Jason Z. Kim on reservoir computing and nonlinear dynamics, and Lindsay M. Smith on the dynamical capacity of reservoir computers. Jason is now at Astera; Lindsay is a physics Ph.D. candidate at Princeton. The downloadable CV includes my broader teaching and mentoring experience.

Invited talks

• Discovering Stochastic Dynamics from Snapshots without Density or Score Estimation — Biological Physics & Physical Biology Seminar (October 2, 2026). Recording forthcoming; BPPB YouTube channel.
• Synchronize or Hop: Two Mechanisms for Predicting the Dynamical World in Modern ML Models — Santa Fe Institute (2026)
• How recurrent neural networks infer dynamical models from data for prediction, inference, and source separation — APS March Meeting, Las Vegas (2023)
• Invertible Generalized Synchronization: A General Principle for Dynamical Learning in Neural Networks — Los Alamos National Laboratory (2021)
• A putative mechanism for implicit learning in biological and artificial neural systems — Applied Dynamics Seminar, University of Maryland (2019)

Conference talks

• A First-Principles Method for Learning Dynamics from Non-temporal Data — Cell Modeling in Space and Time, CSHL (2026)
• Chaotic source separation solved by a tank of water through invertible generalized synchronization — APS March Meeting, Denver (2020; accepted talk, meeting canceled)
• Why do reservoir computing networks predict chaotic systems so well — APS March Meeting, New Orleans (2017)

Honors & awards

• Most downloaded and cited articles in Chaos (2017, 2018)
• Best in Show Award, Summer Workshop on the Dynamic Brain, Friday Harbor, WA (2017)
• 1st Place Poster Prize, IPST Spring Banquet, University of Maryland (2016)
• Merit Fellowship Award & Dean's Fellowship Award, University of Maryland (2013, 2011)

Service

Reviewer for 15 journals including Physical Review Letters, Nature Machine Intelligence, Nature Communications, IEEE TNNLS, Chaos, SIAM Review, and Neural Networks.