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Zhen Lin
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I was a Ph.D. student at the University of Illinois at Urbana-Champaign from 2020 to 2024, supervised by Professor Jimeng Sun. My research interest is in developing robust deep learning models for healthcare problems, with a focus on uncertainty quantification. I was fortunate to be recognized as a 2023 Meta PhD Fellowship Finalist.
Previously, I received my bachelor’s degrees in Computer Science, Mathematics, and Statistics from UChicago. I also spent two years at AQR Capital Management LLC as an alpha signal researcher.
Contextualized Sequence Likelihood: Enhanced Confidence Scores for Natural Language Generation |
Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models |
Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control |
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks |
Conformal Prediction with Temporal Quantile Adjustments |
Conformal Prediction Intervals with Temporal Dependence |
SCRIB: Set-classifier with Class-specific Risk Bounds for Blackbox Models |
Locally Valid and Discriminative Prediction Intervals for Deep Learning Models |
Clebsch-Gordan Networks: A Fully Fourier Space Spherical Convolutional Neural Network |
† denotes alphabetical author ordering |