My recent work spans causal learning from time series at scale, causality-guided world modeling for reinforcement learning generalization, and agentic systems for end-to-end causal analysis. Before joining UCSD, I worked with Dr. Konrad Kording on meta-learning methods on domain-specific causal discovery for large complex systems (e.g., microprocessors). I am also interested in brain-computer interfaces and computational neuroscience, and previously worked on real-time neurofeedback systems advised by Dr. Gan Huang.
We reveal that transformers trained autoregressively naturally encode causal structures — gradient attributions directly recover underlying causal graphs without any explicit causal objectives.
An LLM-powered autonomous agent that automates the entire causal analysis pipeline — from algorithm selection to report generation — making advanced causal methods accessible to researchers across all domains.
A novel reinforcement learning framework that enhances generalization to unseen environments through language-guided compositional causal components. Accepted at ICLR 2025.
Learn to discover causality inside a large complex system without human prior — outperforming human-designed, domain-agnostic methods on the MOS 6502 microprocessor, the NetSim fMRI dataset and the Dream3 gene dataset.
A millisecond-level phase locked neural feedback system based on OpenBCI for real-time alpha wave regulation, integrating acquisition, phase estimation and stimulation on one chip.
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