Chester Holtz

University of California San Diego
San Diego, CA 92093
Email: chholtz AT ucsd.edu

CV, (short)
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I'm a Postdoc in HDSI at UCSD working with Gal Mishne. I am also the instructor of record for DSC40B: Theoretical Foundations of Data Science. I'm interested in graphs and machine learning and optimization. I did my PhD with Chung-Kuan Cheng. Previously I interned as a quantitative researcher with the systematic options alpha group at Citadel Securities and in the Risk and Quantitative Technologies department at JP Morgan, I also interned with the ML group at Qualcomm. I also like horror movies, swimming, and snowboarding and a bit of running.

Here are some papers from my PhD and Postdoc. Some of the authorship ordering is alphabetical

Sawyer Jack Robertson, Chester Holtz, Zhengchao Wan, Gal Mishne, Alexander Cloninger, Robust Graph-Based Semi-Supervised Learning via p-Conductances, arXiv:2502.08873.

Dhruv Kohli, Jesse He, Chester Holtz, Gal Mishne, Alexander Cloninger, Robust estimation of boundary using doubly stochastic scaling of Gaussian kernel, arXiv:2411.18942.

Chester Holtz, Pengwen Chen, Zhengchao Wan, Chung-Kuan Cheng, Gal Mishne, Continuous Partitioning for Graph-Based Semi-Supervised Learning, Openreview, NeurIPS 2024.

Pengwen Chen, Chung-Kuan Cheng, Chester Holtz, Sequential subspace methods on Stiefel manifold optimization problems, arXiv:2404.13301.

Chester Holtz, Yucheng Wang, Chung-Kuan Cheng, Bill Lin, On Robustness and Generalization of ML-Based Congestion Predictors to Valid and Imperceptible Perturbations, arXiv:2403.00103.

Changhao Shi, Chester Holtz, Gal Mishne, Online adversarial purification based on self-supervision, arXiv:2101.09387, ICLR 2021.

Dongwon Park, Daeyeal Lee, Ilgweon Kang, Chester Holtz, Sicun Gao, Bill Lin, Chung-Kuan Cheng, Grid-based framework for routability analysis and diagnosis with conditional design rules, ieee, TCAD.

Romain Tavenard, Johann Faouzi, Gilles Vandewiele, Felix Divo, Guillaume Androz, Chester Holtz, Marie Payne, Roman Yurchak, Marc Rußwurm, Kushal Kolar, Eli Woods, Tslearn, a machine learning toolkit for time series data, JMLR, JMLR.

Chung-Kuan Cheng, Chia-Tung Ho, Chester Holtz, Net separation-oriented printed circuit board placement via margin maximization, arXiv:2210.14259 (best paper at ASP-DAC'22).

Chester Holtz, Changhao Shi, Gal Mishne, Provable Robustness by Geometric Regularization of ReLU Networks, preprint.