Research Highlights

Jiayu's research is anchored in the vision of Unified Knowledge Integration - harnessing AI to unify insights from large-scale, noisy, multimodal, and heterogeneous data, and integrating domain knowledge with novel machine learning and generative AI techniques to transform real-world domains. Please visit ILLIDAN Lab for projects, publication and research activities.

Machine Learning and AI+X Foundations

Jiayu develops machine learning methods that transfer and integrate knowledge across tasks, data sources, and locations. His work on multi-task and transfer learning performs inductive knowledge transfer among related learning tasks to improve their generalization performance simultaneously, and he is the author of the multi-task learning open-source software MALSAR. His lab has developed information fusion approaches that integrate multiple data sources/views together with domain knowledge, as well as a suite of federated learning algorithms that provide privacy-preserving and fair predictive modeling from multiple collaborating parties with heterogeneous data distributions.

AI+Health Informatics

Jiayu has developed advanced disease progression models for Alzheimer's that integrate electronic medical records (EHR) and brain imaging, tackling challenges such as data sparsity, inconsistency, and high-dimensional features. His team recently showed how large-language models (LLMs) can improve early diagnosis of Alzheimer's in a study using EHR from 2.5 million patients. Another major direction is creating digital biomarkers from conversations and daily behavior for affordable and accessible early dementia screening, including a chatbot that quickly gathers language markers and an LLM-based chatbot that provides therapeutic treatment. In drug discovery, his lab developed a data-driven molecular fingerprint that enables cost-effective virtual screening of chemical compounds, a framework to search new molecules with desired properties, and multi-modal foundation models for collaborative drug discovery with human experts.

Teaching

Jiayu is developing new curriculums at both undergraduate and graduate levels, that incorporate the state-of-the-art machine learning research into classroom.

• MSU CSE 480 [Spring 2022] [ Syllabus | Piazza | D2L ]
• MSU CSE 847 Machine Learning [Spring 2022] [ Syllabus | Piazza | D2L ] [Prev. Spring 2021 - 2016]
• MSU CSE 491/404 Introduction to Machine Learning [Prev. Fall 2020 - 2016]

Selected Publications

For recent preprints please check out the publication list of ILLIDAN Lab. For the full publication list see Jiayu's Google Scholar.

• Ranking Policy Gradient.
  Kaixiang Lin and Jiayu Zhou.
  ICLR 2020. Preprint 2019.
• Retaining Privileged Information for Multi-Task Learning.
  Fengyi Tang, Cao Xiao, Fei Wang, Jiayu Zhou, and Li-Wei Lehman.
  KDD 2019.
• Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning.
  Kaixiang Lin, Renyu Zhao, Zhe Xu and Jiayu Zhou.
  KDD 2018. [Paper]
• Identify Susceptible Locations in Medical Records via Adversarial Attacks on Deep Predictive Models.
  Mengying Sun, Fengyi Tang, Jinfeng Yi, Fei Wang and Jiayu Zhou.
  KDD 2018. [Paper]
• Subspace Network: Deep Multi-Task Censored Regression for Modeling Neurodegenerative Diseases.
  Mengying Sun, Inci M. Baytas, Liang Zhan, Zhangyang Wang and Jiayu Zhou.
  KDD 2018. [Paper]
• Enhancing Predictive Modeling of Nested Spatial Data through Group-Level Feature Disaggregation.
  Boyang Liu, Pang-Ning Tan, and Jiayu Zhou.
  KDD 2018. [Paper]
• Multi-Modality Disease Modeling via Collective Deep Matrix Factorization.
  Qi Wang, Mengying Sun, Liang Zhan, Paul Thompson, Shuiwang Ji and Jiayu Zhou.
  KDD 2017 [Paper]
• Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates.
  Liyang Xie, Inci Baytas, Kaixiang Lin and Jiayu Zhou.
  KDD 2017 [Paper]
• Patient Subtyping via Time-Aware LSTM Networks.
  Inci Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil Jain and Jiayu Zhou.
  KDD 2017 [Paper]
• Multi-Task Feature Interaction Learning.
  Kaixiang Lin, Jianpeng Xu, Inci M. Baytas, Shuiwang Ji and Jiayu Zhou.
  KDD 2016 [Paper]
• A Safe Screening Rule for Sparse Logistic Regression.
  Jie Wang, Jiayu Zhou, Jun Liu, Peter Wonka, Jieping Ye.
  NIPS 2014 [Paper]
• From Micro to Macro: Data Driven Phenotyping by Densification of Longitudinal Electronic Medical Records.
  Jiayu Zhou, Fei Wang, Jianying Hu, Jieping Ye.
  KDD 2014 [Paper] [Code].
• FeaFiner: Biomarker Identification from Medical Data through Feature Generalization and Selection.
  Jiayu Zhou, Zhaosong Lu, Jimeng Sun, Lei Yuan, Fei Wang, Jieping Ye.
  KDD 2013 [Paper] [Supplemental]
• Modeling Disease Progression via Fused Sparse Group Lasso.
  Jiayu Zhou, Jun Liu, Vaibhav A. Narayan, and Jieping Ye.
  KDD 2012 [Paper] [Code] Best Video Award [Info]
• Clustered Multi-Task Learning via Alternating Structure Optimization.
  Jiayu Zhou, Jianhui Chen and Jieping Ye.
  NIPS 2011 [Paper] [Code]
• A Multi-Task Learning Formulation for Predicting Disease Progression.
  Jiayu Zhou, Lei Yuan, Jun Liu and Jieping Ye.
  KDD 2011 [Paper] [Code]

Service

Jiayu serves as an Associate Editor for ACM Transactions on Computing for Healthcare, Neurocomputing. He and Journal of Alzheimer's Disease, and a Guest Editor for EURASIP Journal on Bioinformatics and Systems Biology and EURASIP Journal on Advances in Signal Processing. Jiayu is a dedicated peer reviewer for leading journals, including the Journal of Machine Learning Research (JMLR), IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), and IEEE Transactions on Neural Network Learning Systems (TNNLS), among others. His contributions extend to serving on the Technical Program Committee (TPC) and organizing committees for numerous top-tier conferences such as KDD, ICML, NIPS, ICLR, IJCAI, AAAI, and more, where he has held key leadership roles, including SPC, Program Vice-Chair, and Workshop Chair.

Some words keep me moving forward

A job well done is its own reward. You take pride in the things you do, not for others to see, not for the respect, or glory, or any other rewards it might bring. You take pride in what you do, because you're doing your best. If you believe in something, you stick with it. When things get difficult, you try harder.