Jiacheng Zhu

I am a Ph.D. student at Carnegie Mellon University Safe AI Lab. I am fortunate to work under the supervision of Prof. Long Nguyen at UMich Statistics and Prof. Bo Li at UIUC CS.

I obtained a master's in Machine Learning from Carnegie Mellon University. Before that, I received my Bachelor's degree in Engineering Mechanics and a minor in Data Science from Fudan University.

Email  /  CV  /  Google Scholar  /  Github

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I am interested in machine learning problems on heterogeneous data sources. Specifically, I work on topics including optimal transport, Bayesian nonparametric models, and domain adaptation/generalization with applications to robotics, autonomous driving and healthcare.

A preliminary clustering analysis of my works:
Selected publications & preprints
blind-date Functional optimal transport: map estimation and domain adaptation for functional data
Jiacheng Zhu*, Aritra Guha*, Dat Do*, Mengdi Xu, XuanLong Nguyen, Ding Zhao.
Submitted to JMLR, shorter version as AAAI OT-SDM 2022 workshop spotlight
arXiv / code / project page

blind-date Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment
Jielin Qiu, Jiacheng Zhu, Mengdi Xu, Frank Dernoncourt, Trung Bui, Zhaowen Wang, Bo Li, Ding Zhao, Hailin Jin.
Under review

blind-date Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation
Peide Huang, Mengdi Xu, Jiacheng Zhu, Laixi Shi, Fei Fang, Ding Zhao.
Conference on Neural Information Processing Systems (NeurIPS) 2022

blind-date GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Cardiovascular Prediction
Jiacheng Zhu*, Jielin Qiu*, Zhuolin Yang, Douglas Weber, Michael Rosenberg, Emerson Liu, Bo Li, Ding Zhao.
PMLR Machine Learning for Healthcare (MLHC) 2022, shorter version on ICLR 2022 SRML workshop
arXiv / spotlight talk / ICLR 2022 SRML Workshop

blind-date Accelerated Policy Evaluation: Learning Adversarial Environments with Adaptive Importance Sampling
Mengdi Xu, Peide Huang, Fengpei Li, Jiacheng Zhu, Xuewei Qi, Kentaro Oguchi, Zhiyuan Huang, Henry Lam, Ding Zhao.
IEEE International Conference on Intelligent Robots and Systems (IROS) 2022, shorter version on ICLR 2021 workshop
arXiv / ICLR 2021 Workshop
blind-date PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression
Jiacheng Zhu, Greg Darnell, Agni Kumar, Ding Zhao, Bo Li, XuanLong Nguyen, Shirley You Ren.
PMLR Conference on Health, Inference, and Learning (CHIL), 2022
paper / arXiv / spotlight talk / Apple Machine Learning Research

blind-date Context-Aware Safe Reinforcement Learning for Non-Stationary Environments
Baiming Chen, Zuxin Liu, Jiacheng Zhu, Mengdi Xu, Wenhao Ding, Ding Zhao.
IEEE International Conference on Robotics and Automation (ICRA), 2021
paper / arXiv
blind-date Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao.
Conference on Neural Information Processing Systems (NeurIPS), 2020
paper / arXiv / code
blind-date Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios
Chengyuan Zhang, Jiacheng Zhu, Wenshuo Wang, Junqiang Xi.
IEEE Transactions on Intelligent Transportation Systems (ITS), 2021
paper / arXiv / project / shorter vision on ITSC 2019
blind-date Recurrent Attentive Neural Process for Sequential Data
Shenghao Qin* Jiacheng Zhu*, Jimmy Qin, Wenshuo Wang, Ding Zhao.
LIRE Workshop NeurIPS, 2019
arXiv / code / talk
blind-date A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitive
Jiacheng Zhu, Wenshuo Wang, Ding Zhao.
The 24th IEEE International Conference on Intelligent Transportation Systems ITSC, 2018
paper / arXiv / online data platform
  • Reviewer: ICLR (2022, 2023), NeurIPS (2021, 2022), ICML (2021, 2022), AAAI 2023, MLHC 2022, CHIL 2022, Transactions on ITS, ITSC