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.
[Oct 2016] One post-doc position available immediately on Data Mining and Machine Learning. Please email your CV if you are interested in.
[Oct 2016] Two PhD positions available on Data Mining and Machine Learning. See details here.
[Sep 2016] Three papers accepted by ICDM 2016. Congratulations to the authors.
[May 2016] One paper accepted by KDD 2016. Congratulations to the authors.
[Apr 2016] Student Qi Wang received ISBI 2016 Best Student Paper Award.
[Dec 2015] Call for Participation: 1st Int'l Workshop on Biomedical Informatics with Optimization and Machine Learning (BOOM), in conjunction with IJCAI 2016. July, 2016, New York, NY.
[Dec 2015] Call for Participation: 2nd Int'l Workshop on Machine Learning Methods for Recommender Systems (MLRec), in conjunction with SDM 2016. May 2, 2016, Miama, FL.
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.
Jiayu would like to thank the teaching resource and support from GitHub Education, Google Cloud Platform Education Grant, and Amazon AWS Educate program. 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 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.
PhenoTree: Interactive Visual Analytics for Hierarchical Phenotyping from Large-Scale Electronic Health Records.
Inci M. Baytas, Kaixiang Lin, Fei Wang, Anil K. Jain and Jiayu Zhou. IEEE Transaction on Multimedia 2016 [Paper]
Interactive Multi-Task Relationship Learning.
Kaixiang Lin and Jiayu Zhou. ICDM 2016 [Paper]
Asynchronous Multi-Task Learning.
Inci M. Baytas, Ming Yan, Anil K. Jain and Jiayu Zhou. ICDM 2016 [Paper][Code]
Robust Convex Clustering Analysis.
Qi Wang, Pinghua Gong, Shiyu Chang, Thomas S. Huang and Jiayu Zhou. ICDM 2016 [Paper][Code]
Stochastic Convex Sparse Principal Component Analysis.
Inci M. Baytas, Kaixiang Lin, Fei Wang, Anil K. Jain and Jiayu Zhou.
EURASIP Journal on Bioinformatics and Systems Biology, 2016 [Paper]
Multi-Task Feature Interaction Learning.
Kaixiang Lin, Jianpeng Xu, Inci M. Baytas, Shuiwang Ji and Jiayu Zhou. KDD 2016 [Paper]
Discriminative Fusion of Multiple Brain Networks for Early Mild Cognitive Impairment Detection.
Qi Wang, Liang Zhan, Paul M. Thompson, Hiroko H. Dodge and Jiayu Zhou. ISBI 2016 [Paper]Best Student Paper Award
Synergies that Matter: Efficient Interaction Selection via Sparse Factorization Machine.
Jianpeng Xu, Kaixiang Lin, Pang-Ning Tan and Jiayu Zhou. SDM 2016 [Paper]
GSpartan: a Geospatio-Temporal Multi-task Learning Framework for Multi-location.
Jianpeng Xu, Lifeng Luo, Pang-Ning Tan and Jiayu Zhou. SDM 2016 [Paper]
Learning A Task-Specific Deep Architecture for Clustering.
Zhangyang Wang, Shiyu Chang, Jiayu Zhou, Meng Wang and Thomas Huang. SDM 2016 [Paper]
A Space Alignment Method for Cold-Start TV Show Recommendations.
*Shiyu Chang, Jiayu Zhou, Pirooz Chubak, Junling Hu and Thomas Huang. IJCAI 2015 [Paper]Samsung Best Paper Award 2014 finalist
Who, What, When, and Where: Multi-Dimensional Collaborative Recommendations
using Tensor Factorization on Sparse User-Generated Data.
*Preeti Bhargava, Thomas Phan, Jiayu Zhou, and Juhan Lee. WWW 2015 [Paper]
Factorized Bilinear Similarity for Cold-Start Item Recommendations.
*Mohit Sharma, Jiayu Zhou, George Karypis, and Junling Hu. SDM 2015 [Paper]
Formula: FactORized MUlti-task LeArning for Task Discovery in Personalized Medical Models.
*Jianpeng Xu, Jiayu Zhou, and Pang-Ning Tan. SDM 2015 [Paper]
Factorized Similarity Learning in Networks.
*Shiyu Chang, Guo-Jun Qi, Charu Aggarwal, Jiayu Zhou, Meng Wang, and Thomas Huang. ICDM 2014 [Paper]Best Student Paper Award
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]
Analysis of Sampling Techniques for Imbalanced Data: An N=648 ADNI Study.
Rashmi Dubey, Jiayu Zhou, Yalin Wang, Paul M. Thompson, and Jieping Ye. NeuroImage 2014 5-Year Impact Factor: 7.063.
Active Matrix Completion.
Shayok Chakraborty, Jiayu Zhou, Vineeth Balasubr., Sethuraman Panch., Ian Davidson, and Jieping Ye ICDM 2013
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 Multi-task Learning.
Jiayu Zhou, Jun Liu, Vaibhav A. Narayan, and Jieping Ye. NeuroImage 2013 5-Year Impact Factor: 7.063.
Patient Risk Prediction Model via Top-k Stability Selection.
Jiayu Zhou, Jimeng Sun, Yashu Liu, Jianying Hu, and Jieping Ye. SDM 2013 [Paper][Supplimental]
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.
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/S-TPC in the following major conferences:
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.