Research Highlights

Jiayu has a broad research interest in large-scale machine learning and data mining, and biomedical informatics. Please visit ILLIDAN Lab for projects, publication and research activities.

Learning from Multiple Tasks

Design learning formulations and optimization algorithms to learn multiple related machine learning tasks, performing inductive knowledge transfer among the tasks and improving generalization performance.

MALSAR Software Tutorial

Progression of Alzheimer's Disease

Build machine learning models that understand disease progression and identify signaling biomarkers, from multiple data sources, including medical imaging, genotypes, and other clinical information.

More Info

Teaching

Jiayu is developing new curriculums at both undergraduate and graduate levels, that incorporate the state-of-the-art machine learning research into classroom. Check out the website for my courses Machine Learning @ MSU.

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

Jiayu would like to thank the teaching resource and support from GitHub Education, Google Cloud Platform Education Grant, and Amazon AWS Educate program.

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 Neurocomputing 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. Besides, Jiayu regularly reviews manuscripts for the following journals: Journal of Machine Learning Research (JMLR), IEEE Transactions on Knowledge and Data Engineering Data (TKDE), IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transactions on Neural Network Learning Systems (TNNLS), Data Mining and Knowledge Discovery (DMKD), Computational Statistics and Data Analysis (CSDA), Knowledge and Information Systems (KAIS), ACM Transactions on Knowledge Discovery from Data (TKDD), Annals of Applied Statistics (AOAS), BMC Bioinformatics, Pattern Recognition Letters (PRL), International Journal on Artificial Intelligence Tools (IJAIT). Jiayu also served as TPC or organizing committee in the following major conferences:

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.