Short Bio

I am an Applied Scientist at the AWS AI Labs. Prior to this, I was a Researcher at the Data Science Department of NEC Labs from 2018 to 2022. 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 and data science, with a focus on the development of robust and adaptable machine learning models in data-constrained scenarios, for reliable inference in environments and tasks that are subject to change. I am particularly interested in modeling the dynamics and structure of data, with research objects of time-varying data, including time series (e.g., sensor signals) and streaming entities (e.g., patients), and graph-structured data, including networks (e.g., bio-networks) and structured entities (e.g., molecules). My research on them has been extended to applications in healthcare (including personalized healthcare, press coverage: Science Japan, KeguanJP), biomedicine, cyber-physical systems, AIOps (e.g., deployed in AWS cloud systems), e-commerce and finance, and published in refereed conferences (e.g., ICLR, ICML, NeurIPS, AAAI, CVPR, KDD, WWW) and journals (e.g., IEEE TKDE, ACM TKDD), with more than 20 patents filed or granted.

Research Interests

• Machine learning: deep generative modeling for making predictions with complex data (probabilistic inference and parameter estimation); few-shot learning; meta-learning; time series modeling/forecasting, anomaly detection; out-of-distribution (OOD) analysis; graph representation learning (graph neural networks); self-supervised learning, multimodal LLMs
• Data science: AI for science (e.g., biomedicine, neuroscience), AI for social good (e.g., healthcare, cyber-physical systems, AIOps, e-commerce, finance)

News

I will join the Department of Computer Science at the University of Houston as an Assistant Professor in the Fall semester of 2024.

[Prospective Students] I am looking for self-motivated Ph.D. students to work together on machine learning and data science research. There are multiple fully funded RA/TA positions in my group starting from Spring 2025. If you are interested, please drop me an email at nijingchao [at] gmail [dot] com with your CV/resume, transcripts and any materials that you think are helpful.

Selected Publications

Full List | Google Scholar | DBLP

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

• 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%)
  [code]


• 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]


• Deep Multi-Graph Clustering via Attentive Cross-Graph Association
  Dongsheng Luo^★, Jingchao Ni, Suhang Wang, Yuchen Bian, Xiong Yu, Xiang Zhang
  Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM), 2020
  [code]


• 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


• Co-Regularized Deep Multi-Network Embedding
  Jingchao Ni, Shiyu Chang, Xiao Liu, Wei Cheng, Haifeng Chen, Dongkuan Xu, Xiang Zhang
  Proceedings of the International Conference on World Wide Web (WWW), 2018
  [code] [slides]


• ComClus: A Self-Grouping Framework for Multi-Network Clustering
  Jingchao Ni, Wei Cheng, Wei Fan, Xiang Zhang
  IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018


• Many Heads are Better than One: Local Community Detection by the Multi-Walker Chain
  Yuchen Bian, Jingchao Ni, Wei Cheng, Xiang Zhang
  Proceedings of the IEEE International Conference on Data Mining (ICDM), 2017
  (Best Paper Award Finalist)


• Flexible and Robust Multi-Network Clustering
  Jingchao Ni, Hanghang Tong, Wei Fan, Xiang Zhang
  Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2015
  [code] [slides] [poster]


• Inside the Atoms: Ranking on a Network of Networks
  Jingchao Ni, Hanghang Tong, Wei Fan, Xiang Zhang
  Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2014
  [code] [slides] [poster]

Professional Services

• Senior Program Committee Member:
  • AAAI'21, 23, 24


• Program Committee Member:
  • ICML'21, 22
  • NeurIPS'20, 21, 22
  • ICLR'20, 21, 22
  • SIGIR'20
  • TheWebConf (WWW)'20, 21
  • ECML PKDD'20
  • SDM'19, 20
  • AAAI'19, 20
  • ACM WSDM'19, 20, 21
  • ACM CIKM'18
  • IEEE BigData'18


• Journal Reviewer:
  • IEEE Transactions on Knowledge and Data Engineering (TKDE)
  • ACM Transactions on Knowledge Discovery from Data (TKDD)
  • IEEE Transactions on Big Data (TBD)
  • Transactions on Machine Learning Research (TMLR)
  • Neurocomputing
  • Data Mining and Knowledge Discovery (DAMI)
  • IEEE Access
  • ACM Big Data
  • Computational and Structural Biotechnology Journal (CSBJ)