Computer Science and Engineering
Michigan State University
Email: [Turn on javascirpt to check the link]
Office: Engineering Building 2134
Mail: 428 S Shaw Ln Rm 3115,
East Lansing, MI 48824
Short Biography Jiayu Zhou is an assistant professor at Department of Computer Science and Engineering, Michigan State University. Before joining MSU, Jiayu was a staff research scientist at Samsung Research America. Jiayu received his Ph.D. degree in computer science at Arizona State University in 2014. Jiayu has a broad research interest in large-scale machine learning and data mining, and biomedical informatics.
[Apr 2018] Call for Participation: 1st Int'l Workshop on Big Traffic Data Analytics (BigTraffic 2018), in conjunction with SDM 2018. May 5, 2018, San Diego.
[May 2017] Received Isadore & Margaret Mezey Junior Investigator Travel Award from Michigan Alzheimer's Disease Center, University of Michigan
[Dec 2016] One paper received IEEE Big Data 2016 Best Paper Award. Congratulations to the authors.
[Apr 2016] Student Qi Wang received ISBI 2016 Best Student Paper Award.
Jiayu has a broad research interest in large-scale machine learning and data mining, and biomedical informatics.
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.
Large-Scale Metric Learning,
Office of Naval Research (N00014-14-1-0631), Co-PI, PI: Anil K. Jain, 2014-2017.
Jiayu would also like to thank
Didi Chuxing and
VeChain Foundation for research gifts, and NVIDIA Corporation for the donation of GPU cards. Jiayu is supported by Michigan Alzheimer's Disease Center, Unversity of Michigan as a trainee under NIH/NIA center grant P30AG053760, where Jiayu received Isadore & Margaret Mezey Junior Investigator Travel Award in 2017.
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.
[2018 Fall] CSE 491 Introduction to Machine Learning
[2018 Spring] CSE 847 Machine Learning
[2017 Fall] CSE 491 Introduction to Machine Learning
[2017 Spring] CSE 847 Machine Learning
[2016 Fall] CSE 491 Introduction to Machine Learning
[2016 Spring] CSE 847 Machine Learning
Jiayu would like to thank the teaching resource and support from GitHub Education, Google Cloud Platform Education Grant, and Amazon AWS Educate program. Besides classroom teaching, Jiayu delivered tutorials on his research topics at conferences. The slides can be found below:
Mining Structured Sparsity Beyond Convexity at ICDM 2015 [Slides]
Multi-Task Learning: Theory, Algorithms, and Applications (with Dr. Jieping Ye) at SDM 2012 [Slides]
For recent preprints please check out the publication list of ILLIDAN Lab.
For the full publication list see Jiayu's Google Scholar. The * symbol indicates that the paper is done when the first author was an intern mentored by Jiayu.
Retaining Privileged Information for Multi-Task Learning.
Fengyi Tang, Cao Xiao, Fei Wang, Jiayu Zhou, and Li-Wei Lehman. KDD 2019, Accepted.
MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records.
Xi Zhang, Andy Tang, Hiroko Dodge, Jiayu Zhou and Fei Wang. KDD 2019, Accepted.
GCN-MF: Disease-Gene Association Identification By Graph Convolutional Networks and Matrix Factorzation.
Peng Han, Peng Yang, Peilin Zhao, Shuo Shang, Yong Liu, Jiayu Zhou, Xin Gao and Panos Kalnis. KDD 2019, Accepted.
Boosted Sparse and Low-Rank Tensor Regression.
Lifang He, Kun Chen, Wanwan Xu, Jiayu Zhou, and Fei Wang. NIPS 2018. [Paper]
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].
Efficient Multi-Task Feature Learning with Calibration.
Pinghua Gong, Jiayu Zhou, Jieping Ye. KDD 2014 [Paper][Code]
Lasso Screening Rules via Dual Polytope Projection.
Jie Wang, Jiayu Zhou, Peter Wonka, Jieping Ye. NIPS 2013 [Paper]Spotlight
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]
Integrating Low-Rank and Group-Sparse Structures for Robust Multi-Task Learning.
Jianhui Chen, Jiayu Zhou, Jieping Ye. KDD 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]
Jiayu serves as an Associate Editor for Neurocomputing, 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),
Pattern Recognition Letters (PRL),
International Journal on Artificial Intelligence Tools (IJAIT).
Jiayu also served as TPC or organizing committee in the following major conferences:
2nd International Workshop on Biomedical Informatics With Optimization And Machine Learning (BOOM 2017), in conjunction with IJCAI 2017, Aug 21, 2017, Melbourne, Australia
1st International Workshop on Biomedical Informatics With Optimization And Machine Learning (BOOM 2016), in conjunction with IJCAI 2016, July 9, 2016, New York, New York, USA
2nd International Workshop on Machine Learning Methods for Recommender Systems (MLRec 2016), in conjunction with SDM 2016, May 7, 2016, Miami, Florida, USA
1st International Workshop on Machine Learning Methods for Recommender Systems (MLRec 2015), in conjunction with SDM 2015, May 2, 2015, Vancouver, British Columbia, Canada
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.