Short Bio

I am an Assistant Professor in the Department of Computer Science at the University of Houston. Prior to this, I was a researcher at the Data Science Department of NEC Labs from 2018 to 2022 and the AWS AI Labs from 2022 to 2024. I received my Ph.D. degree from College of IST, The Pennsylvania State University in 2018, advised by Prof. Xiang Zhang. My research is centered around machine learning, data mining, and artificial intelligence, with a focus on time series analysis through cross-modal learning, multimodal integration, and LLM reasoning. My research has been extended to applications in healthcare (including personalized healthcare, press coverage: Science Japan, KeguanJP), biomedicine, cyber-physical systems, and AIOps (e.g., deployed in AWS cloud systems), and published in refereed conferences (e.g., ICLR, ICML, NeurIPS, ACL, AAAI, CVPR, KDD, WWW) and journals (e.g., IEEE TKDE, ACM TKDD), with more than 20 patents filed or granted.

Research Interests

• Machine learning: time series analysis with LVMs/LLMs/LMMs; agentic AI; generative models; anomaly/OOD analysis; graph learning
• Data science: AI for science (e.g., neuroscience, geoscience), AI for social good (e.g., healthcare, cyber-physical systems, AIOps)

News

[Prospective Students] I am looking for self-motivated Ph.D. students to join my group in Spring 2026. If you are interested, please drop me an email at jni7 [at] uh [dot] edu with your CV/resume, transcripts and any materials that you think are helpful.

Selected Publications

Full List | Google Scholar | DBLP

^♮ indicates equal contribution, ^★ indicates students / interns I have advised.

• Harnessing Vision Models for Time Series Analysis: A Survey
  Jingchao Ni, Ziming Zhao^★, ChengAo Shen^★, Hanghang Tong, Dongjin Song, Wei Cheng, Dongsheng Luo, Haifeng Chen
  Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI), 2025
  
  Github


• Multi-modal Time Series Analysis: A Tutorial and Survey
  Yushan Jiang, Kanghui Ning, Zijie Pan, Xuyang Shen, Jingchao Ni, Wenchao Yu, Anderson Schneider, Haifeng Chen, Yuriy Nevmyvaka, Dongjin Song
  Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2025
  
  Github


• Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal Discovery
  ChengAo Shen^★, Zhengzhang Chen, Dongsheng Luo, Dongkuan Xu, Haifeng Chen, Jingchao Ni
  Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL Findings), 2025
  
  Github


• Interpreting Graph Neural Networks with In-Distributed Proxies
  Zhuomin Chen, Jiaxing Zhang, Jingchao Ni, Xiaoting Li, Yuchen Bian, Md Mezbahul Islam, Ananda Mohan Mondal, Hua Wei, Dongsheng Luo
  Proceedings of the International Conference on Machine Learning (ICML), 2024
  Truestworthy Learning on Graphs Workshop at the Web Conference (TrustLOG @ WWW), 2024 [pdf]
  [arXiv]


• Hierarchical Gaussian Mixture based Task Generative Model for Robust Meta-Learning
  Yizhou Zhang^★, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Liang Tong, Haifeng Chen, Yan Liu
  Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), 2023
  


• Superclass-Conditional Gaussian Mixture Model for Learning Fine-Grained Embeddings
  Jingchao Ni, Wei Cheng, Zhengzhang Chen, Takayoshi Asakura, Tomoya Soma, Sho Kato, Haifeng Chen
  The International Conference on Learning Representations (ICLR), 2022
  (Spotlight Presentation, 5%)
  


• FACESEC: A Fine-Grained Robustness Evaluation Framework for Face Recognition Systems
  Liang Tong, Zhengzhang Chen, Jingchao Ni, Wei Cheng, Dongjin Song, Haifeng Chen, Yevgeniy Vorobeychik
  Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), 2021
  [arXiv] [suppl] [code]


• Dynamic Gaussian Mixture based Deep Generative Model for Robust Forecasting on Sparse Multivariate Time Series
  Yinjun Wu^★, Jingchao Ni, Wei Cheng, Bo Zong, Dongjin Song, Zhengzhang Chen, Yanchi Liu, Xuchao Zhang, Haifeng Chen, Susan Davidson
  Proceedings of the AAAI International Conference on Artificial Intelligence (AAAI), 2021
  [arXiv] [code]


• Robust Graph Representation Learning via Neural Sparsification
  Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, Wei Wang
  Proceedings of the International Conference on Machine Learning (ICML), 2020
  [suppl]


• Inductive and Unsupervised Representation Learning on Graph Structured Objects
  Lichen Wang, Bo Zong, Qianqian Ma, Wei Cheng, Jingchao Ni, Wenchao Yu, Yanchi Liu, Dongjin Song, Haifeng Chen, Yun Fu
  The International Conference on Learning Representations (ICLR), 2020


• Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-based Recommendation
  Xin Dong^★, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Bo Zong, Dongjin Song, Yanchi Liu, Haifeng Chen, Gerard de Melo
  Proceedings of the AAAI International Conference on Artificial Intelligence (AAAI), 2020
  [arXiv]


• A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data
  C. Zhang, D. Song, Y. Chen, X. Feng, C. Lumezanu, W. Cheng, Jingchao Ni, B. Zong, H. Chen, N. Chawla
  Proceedings of the AAAI International Conference on Artificial Intelligence (AAAI), 2019
  [arXiv] (Most Influential Papers of AAAI 2019 by PaperDigest)