Ian Char | Carnegie Mellon University

About Me

I am an ABD Ph.D. student in the Machine Learning Department at CMU advised by Jeff Schneider. My research interests include reinforcement learning, Bayesian optimization, and uncertainty quantification. I am particularly interested in how machine learning can be applied to the sciences, and my current research focuses revolve around how we can learn controls for tokamaks.

Before joining CMU, I earned a MS in Applied Mathematics from University of Colorado Boulder and was advised by Manuel Lladser. I also went to University of Colorado Boulder for my undergraduate and earned a BS in both applied math and computer science.

Publications

PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks
Ian Char, Jeff Schneider
Advances in Neural Information Processing Systems. NeurIPS 2023.
[PDF] [Code]
Offline Model-Based Reinforcement Learning for Tokamak Control
Ian Char, Joseph Abbate, Laszlo Bardoczi, Mark D. Boyer, Youngseog Chung, Rory Conlin, Keith Erickson, Viraj Metha, Nathan Richner, Egemen Kolemen, Jeff Schneider
Learning for Discovery and Control 2023
[PDF]
Near-optimal Policy Identification in Active Reinforcement Learning
Xiang Li, Viraj Mehta, Johannes Kirschner, Ian Char, Willie Neiswanger, Jeff Schneider, Andreas Krause, Ilija Bogunovic
International Conference on Learning Representations 2023 Notable-Top-5% (Oral)
[PDF]
Exploration via Planning for Information about the Optimal Trajectory
Viraj Mehta, Ian Char, Joseph Abbate, Rory Conlin, Mark D Boyer, Stefano Ermon, Jeff Schneider, Willie Neiswanger
Advances in Neural Information Processing Systems. NeurIPS 2022.
[PDF] [Code]
Deep Attentive Variational Inference
Ifigeneia Apostolopoulou, Ian Char, Elan Rosenfeld, Artur Dubrawski
International Conference on Learning Representations. ICLR 2022.
[PDF] [Code]
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification
Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider
Advances in Neural Information Processing Systems. NeurIPS 2021.
[PDF] [Code]
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction
Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D Boyer, Egemen Kolemen, Jeff Schneider
IEEE Conference on Decision and Control. CDC 2021.
[PDF]
Offline Contextual Bayesian Optimization
Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark D Boyer, Egemen Kolemen, Jeff Schneider
Advances in Neural Information Processing Systems. NeurIPS 2019.
[PDF] [Code]
Stochastic Analysis of Minimal Automata Growth for Generalized Strings
Ian Char, Manuel Lladser
Methodology and Computing in Applied Probability. 2019.
[Paper] [Code]

Software

Uncertainty Toolbox
A python toolbox for predictive uncertainty quantification, calibration, metrics, and visualizations. Also: a glossary of useful terms and a collection of relevant papers and references.
[Code] [Website]

Workshop Papers and Technical Reports(* indicates equal contribution)

Towards LLMs as Operational Copilots for Fusion Reactors
Viraj Mehta, Joseph Abbate, Allen Wang, Andrew Rothstein, Ian Char, Jeff Schneider, Egemen Kolemen, Christina Rea, Darren Garneir
NeurIPS 2023 AI4Science Workshop. Preprint.
PDF coming soon!
Correlated Trajectory Uncertainty for Adaptive Sequential Decision Making
Ian Char^*, Youngseog Chung^*, Rohan Shah, Jeff Schneider
NeurIPS 2023 Worshop on Adaptive Experimental Design and Active Learning in the Real World Preprint.
PDF coming soon!
How Useful are Gradients for OOD Detection Really?
Conor Igoe, Youngseog Chung, Ian Char, Jeff Schneider
Preprint.
[PDF]
How Useful are Gradients for OOD Detection Really?
Conor Igoe, Youngseog Chung, Ian Char, Jeff Schneider
Preprint.
[PDF]
BATS: Best Action Trajectory Stitching
Ian Char^*, Viraj Mehta^*, Adam Villaflor, John Dolan, Jeff Schneider
NeurIPS 2021 Offline Reinforcement Learning Workshop
[PDF]
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification
Youngseog Chung, Ian Char, Han Guo, Jeff Schneider, Willie Neiswanger
Uncertainty and Robustness in Deep Learning Workshop (ICML 2021)
[PDF][Code] [Website]
Offline contextual bayesian optimization for nuclear fusion
Youngseog Chung, Ian Char, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark Dan Boyer, Egemen Kolemen, Jeff Schneider
Machine Learning and the Physical Sciences Workshops at NeurIPS 2019.
[PDF]

Awards

Teaching Assistant of the Year
Machine Learning Dperatment, Carnegie Mellon Univeristy (2021-2022)
NSF GRFP (2018)
NSF Graduate Research Fellowship Program, 2018
Outstanding Graduate for Academic Achievement
University of Colorado at Boulder, May 2018
Awarded to students graduating with a 4.0.

Teaching

(10-606/607) Mathematical/Computational Foundations for Machine Learning
Teaching Assistant
Carnegie Mellon University, Fall 2021
(10-716) Advanced Machine Learning: Theory and Methods
Teaching Assistant
Carnegie Mellon University, Spring 2020
(APPM 4350) Fourier Series and Boundary Value Problems
Teaching Assistant
University of Colorado Boulder, Fall 2016

Service

Machine Learning Department Social Committee
Fall 2019 Onwards
Reviewer for NeurIPS 2023
NeurIPS 2023
Reviewer for Real World Experiment Design and Activate Learning Workshop
ICML 2022