Andrew Rothstein

According to our database, Andrew Rothstein authored at least 7 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

TokEye: Fast Signal Extraction for Fluctuating Time Series via Offline Self-Supervised Learning From Fusion Diagnostics to Bioacoustics.

Nathaniel Chen, Kouroche Bouchiat, Peter Steiner, Andrew Rothstein, David R. Smith, Max Austin, Michael Van Zeeland, Azarakhsh Jalalvand, Egemen Kolemen

CoRR, February, 2026

2025

Enabling Integrated AI Control on DIII-D: A Control System Design with State-of-the-art Experiments.

Andrew Rothstein, Hiro Josep Farre Kaga, Jalal Butt, Ricardo Shousha, Keith Erickson, Takuma Wakatsuki, Azarakhsh Jalalvand, Peter Steiner, SangKyeun Kim, Egemen Kolemen

CoRR, November, 2025

Regulation Compliant AI for Fusion: Real-Time Image Analysis-Based Control of Divertor Detachment in Tokamaks.

Nathaniel Chen, Cheolsik Byun, Azarakhsh Jalalvand, SangKyeun Kim, Andrew Rothstein, Filippo Scotti, Steve Allen, David Eldon, Keith Erickson, Egemen Kolemen

CoRR, July, 2025

Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks.

Rohit Sonker, Alexandre Capone, Andrew Rothstein, Hiro Josep Farre Kaga, Egemen Kolemen, Jeff Schneider

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Avoiding fusion plasma tearing instability with deep reinforcement learning.

Jaemin Seo, SangKyeun Kim, Azarakhsh Jalalvand, Rory Conlin, Andrew Rothstein, Joseph Abbate, Keith Erickson, Josiah Wai, Ricardo Shousha, Egemen Kolemen

Nat., 2024

2023

Multimodal Prediction of Tearing Instabilities in a Tokamak.

Jaemin Seo, Rory Conlin, Andrew Rothstein, SangKyeun Kim, Joseph Abbate, Azarakhsh Jalalvand, Egemen Kolemen

Proceedings of the International Joint Conference on Neural Networks, 2023

Alfvén eigenmode detection using Long-Short Term Memory Networks and CO2 Interferometer data on the DIII-D National Fusion Facility.

Alvin V. Garcia, Azarakhsh Jalalvand, Peter Steiner, Andrew Rothstein, Michael Van Zeeland, William W. Heidbrink, Egemen Kolemen

Proceedings of the International Joint Conference on Neural Networks, 2023