Research.
Real-time control of tokamaks — foundation models, computer vision, and signal processing for plasma state estimation and instability prediction. Advised by Egemen Kolemen at Princeton MAE / PPPL.
Current focus
Building foundation models for fusion: large neural networks trained on diverse tokamak data that can be specialized to downstream control and diagnostic tasks. I'm also working on real-time emission-front control and self-supervised identification of coherent modes — both run at DIII-D.
Selected experimental work
- Led a real-time ML-enabled emission front control experiment at DIII-D (APS DPP 2024).
- Built TokEye, a fast signal-extraction pipeline for fluctuating tokamak time series (arXiv Feb 2026).
Broader interests
- Foundation models for scientific time-series data
- Vision-based state estimation and active learning on streaming data
- Uncertainty quantification when ML sits inside safety-critical control loops
- Regulatory and compliance considerations for AI in fusion
External profiles
Peer-reviewed
Regulation Compliant AI for Fusion
Preprints & workshop papers
Towards Large-Scale Heterogeneous Data Organization for Scientific Foundation Models
TokEye: Fast Signal Extraction for Fluctuating Time Series
Beyond the Loss Curve: Scaling Laws, Active Learning
Visual Perception of 3D Space and Shape in Time
Talks
Self-Supervised Identification of Coherent Modes in Tokamaks
Towards a Foundation Model for Fusion
Compact Experimental Negative TriAngUlarity Reactor (CENTAUR)
Real-Time Machine-Learning Enabled Emission Front Control at DIII-D
Investigating Guiding Center Versus Full Orbit Effects with ORBIT-GPU