Egemen Kolemen — accepted AI-conference papers

2026

Multimodal Super-Resolution: Discovering Hidden Physics and Its Application to Fusion Plasmas (Abstract Reprint)

Azarakhsh Jalalvand, SangKyeun Kim, Jaemin Seo, Qiming Hu, Max Curie, Peter Steiner +3

AAAI 2026technical

Understanding complex physical systems often requires integrating data from multiple diagnostics, each with limited resolution or coverage. We present a machine learning framework that reconstructs synthetic high-temporal-resolution data for a target diagnostic using information from other diagnosti

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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

ICML 2025poster

Machine learning algorithms often struggle to control complex real-world systems. In the case of nuclear fusion, these challenges are exacerbated, as the dynamics are notoriously complex, data is poor, hardware is subject to failures, and experiments often affect dynamics beyond the experiment's dur…

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2019

Offline Contextual Bayesian Optimization

Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark Boyer +2

NeurIPS 2019poster

In black-box optimization, an agent repeatedly chooses a configuration to test, so as to find an optimal configuration. In many practical problems of interest, one would like to optimize several systems, or

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