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Career

· Doctor of Philosophy - PhD, Machine Learning, Carnegie Mellon University
· PhD Student, Machine Learning, Carnegie Mellon University
· Machine Learning Internship, Celonis
· Senior Software Engineer, CMU Auton Lab
· Research Intern, Google
· Research Scientist, Microsoft AI
· Doctor of Philosophy - PhD, Machine Learning, The Hong Kong University of Science and Technology

Publications (16)

Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification
Neural Information Processing Systems · 2020
114
cited
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification
arXiv.org · 2021
84
cited
What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions
arXiv.org · 2024
74
cited
Offline Contextual Bayesian Optimization
Neural Information Processing Systems · 2019
53
cited
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction
IEEE Conference on Decision and Control · 2020
30
cited
How Useful are Gradients for OOD Detection Really?
arXiv.org · 2022
28
cited
Offline Model-Based Reinforcement Learning for Tokamak Control
Conference on Learning for Dynamics & Control · 2023
25
cited
Offline Contextual Bayesian Optimization for Nuclear Fusion
arXiv.org · 2020
14
cited
Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks
arXiv.org · 2024
9
cited
Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning
arXiv.org · 2023
7
cited
Sampling-based Multi-dimensional Recalibration
International Conference on Machine Learning · 2024
3
cited
Parity Calibration
Conference on Uncertainty in Artificial Intelligence · 2023
2
cited
Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts
Trans. Mach. Learn. Res. · 2024
1
cited
N EURAL D YNAMICAL S YSTEMS
2020
Post-nonlinear Causal Model with Deep Neural Networks
2019
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Youngseog Chung | Researcher Profile | Sotabase | Sotabase