About Illidan Lab

At the Intelligent Data Analytics (ILLIDAN) Lab @ University of Michigan, we are at the forefront of advancing data science and AI to tackle some of the most critical real-world challenges, commonly known as AI+X, with a special emphasis on health informatics. Our research is anchored in the vision of Unified Knowledge Integration - an innovative approach that harnesses AI to unify insights from large-scale, noisy, multimodal, and heterogeneous datasets. By seamlessly integrating domain knowledge with novel machine learning and generative AI techniques, we aim to transform various real-world domains, enhance decision making for data science through establishing the closed-loop flow of informatics among key components of human, data, and analytics. In the domain of health informatics, we develop novel tools and systems for improving the understanding, diagnosis, and intervention of diseases, especially Alzheimer’s.

News

• [2024] ILLIDAN Lab has moved to the University of Michigan!
• [2024] Check out our latest preprint that explores how to leverage large-language models (LLMs) to help on early diagnosis of Alzheimer’s. Are larger and domain fine-tuned models always better? We provided some exciting and surprising results based on our study on 2.5 million patients.
• [2024] [Call for Participation] Please join our first FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics, co-located with KDD’24, August 25-26th, Centre de Convencions Internacional de Barcelona, Spain.
• [2024] Check out our topology-aware federated learning survey accepted by ACM Computing Surveys and selected in the ACM Showcase.

Opening: ILLIDAN lab is currently looking for motivated Ph.D. students and post-doctoral researchers on machine learning and AI + Health research. Interested candidates please apply to our PhD in Informatics program following the instructions here and mention me in your application.

Research Highlights

ILLIDAN lab designs scalable, distributed, robust and privacy-preserving machine learning algorithms, creates open source machine learning software, and develops powerful machine learning for applications in health informatics and other scientific areas.

ILLIDAN Lab would like to thank support from National Science Foundation, National Institutes of Health, and Office of Naval Research, through the following research projects:

Active Projects

• Privacy-Preserving and Robust Federated Learning, Office of Naval Research (N00014-24-1-2168), Co-PI, PI: Anil K. Jain, 2024-2027.
• A Consolidated Framework of Computational Privacy and Machine Learning, NSF IIS Core Program / Medium (IIS-2212174), PI, 2022-2026.
• Virtual Compound Screening using Gene Expression, National Institute of General Medical Sciences (1R01GM145700), PI, 2022-2025.
• Identification of Mild Cognitive Impairment using Machine Learning from Language and Behavior Markers, National Institute of Aging (1RF1AG072449), Leading PI, 2021-2026. Media Coverage: MSU Today
• Intelligent Closed-Loop Neural Interface System for Studying Mechanisms of Somatosensory Feedback in Control of Functional and Stable Locomotion, NSF NCS Program (ECCS-2024270), Co-PI, PI: Wen Li, 2020-2023.
• Repurpose open data to discover therapeutics for understudied diseases, National Institute of General Medical Sciences (1R01GM134307), Co-PI, PI: Bin Chen, 2019-2024.
• Career: Harness the Big Data via Large-Scale Lifelong Learning, NSF CAREER Program (IIS-1749940), PI, 2018-2023. Media Coverage: MSU Today, Fox News
• A Macrosystems Ecology Framework for Continental-Scale Prediction and Understanding of Lakes, NSF MSB Program (MSU Site: EF-1638679), Co-PI, PI: Patricia Soranno, 2016-2021. Media Coverage: MSU Today

Completed Projects

• Deep Learning: Integrating Domain Knowledge and Interpreting the Network Decisions, Office of Naval Research (N00014-20-1-2382), Co-PI, PI: Anil K. Jain, 2020-2023.
• Unsupervised Feature Selection in the Era of Big Data, NSF IIS Core Program (IIS-1714741), Co-PI, 2017-2020.
• Large-Scale Information Fusion from Multiple Modalities, Office of Naval Research (N00014-17-1-2265), Co-PI, 2017-2020.
• Structured Methods for Multi-Task Learning, NSF IIS Core Program (MSU Site: IIS-1615597), Leading PI, 2016-2019.
• Integrating Domain Knowledge via Interactive Multi-Task Learning, NSF CRII (IIS-1565596), PI, 2016-2018.
• Large-Scale Metric Learning, Office of Naval Research (N00014-14-1-0631), Co-PI, 2014-2017.

ILLIDAN Lab would also like to thank Xkool.ai, Didi Chuxing and VeChain Foundation for research gifts, and NVIDIA Corporation for the donation of GPU cards.

Investigator

Lab Members

Visitor

Alumni

Friends

Recent Preprints

• Distributed In-Context Learning under Non-IID Among Clients .
  Siqi Liang, Sumyeong Ahn, and Jiayu Zhou.
  Preprint, 2024 [arXiv]
• Augmented Risk Prediction for the Onset of Alzheimer’s Disease from Electronic Health Records with Large Language Models.
  Jiankun Wang, Sumyeong Ahn, Taykhoom Dalal, Xiaodan Zhang, Weishen Pan, Qiannan Zhang, Bin Chen, Hiroko H. Dodge, Fei Wang, and Jiayu Zhou.
  Preprint, 2024 [arXiv]
• Large Language Models in Medical Term Classification and Unexpected Misalignment Between Response and Reasoning.
  Xiaodan Zhang, Sandeep Vemulapalli, Nabasmita Talukdar, Sumyeong Ahn, Jiankun Wang, Han Meng, Sardar Mehtab Bin Murtaza, Aakash Ajay Dave, Dmitry Leshchiner, Dimitri F. Joseph, Martin Witteveen-Lane, Dave Chesla, Jiayu Zhou, Bin Chen.
  Preprint, 2024 [arXiv]
• Towards Stability of Parameter-free Optimization .
  Yijiang Pang, Shuyang Yu, Bao Hoang, and Jiayu Zhou.
  Preprint, 2024 [arXiv]
• Stochastic Two Points Method for Deep Model Zeroth-order Optimization.
  Yijiang Pang and Jiayu Zhou.
  Preprint, 2024 [arXiv]
• Who Leaked the Model? Tracking IP Infringers in Accountable Federated Learning.
  Shuyang Yu, Junyuan Hong, Yi Zeng, Fei Wang, Ruoxi Jia, Jiayu Zhou.
  Preprint, 2023 [arXiv]

Selected Publications

• Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization.
  Bao Hoang, Yijiang Pang, Siqi Liang, Liang Zhan, Paul Thompson, and Jiayu Zhou.
  KDD 2024. [accepted]
• On the Generalization Ability of Unsupervised Pretraining.
  Yuyang Deng, Junyuan Hong, Jiayu Zhou, and Mehrdad Mahdavi.
  AISTATS 2024. [paper]
• Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey.
  Jiajun Wu, Fan Dong, Henry Leung, Zhuangdi Zhu, Jiayu Zhou and Steve Drew.
  ACM Computing Surveys 2024. [paper]
• Safe and Robust Watermark Injection with a Single OoD Image.
  Shuyang Yu, Junyuan Hong, Haobo Zhang, Haotao Wang, Zhangyang Wang, and Jiayu Zhou.
  ICLR 2024. [paper]
• Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation.
  Suraj Rajendran, Weishen Pan, Mert R. Sabuncu, Yong Chen, Jiayu Zhou, and Fei Wang.
  Patterns 2024. [paper]
• Comparison of Prompt Engineering and Fine-Tuning Strategies in Large Language Models in the Classification of Clinical Notes.
  Xiaodan Zhang, Nabasmita Talukdar, Sandeep Vemulapalli, Sumyeong Ahn, Jiankun Wang, Han Meng, Sardar Mehtab Bin Murtaza, Dmitry Leshchiner, Aakash Ajay Dave, Dimitri F. Joseph, Martin Witteveen-Lane, Dave Chesla, Jiayu Zhou, and Bin Chen.
  AMIA Informatics Summit 2024. [accepted]
• Federated Learning's Blessing: FedAvg has Linear Speedup.
  Zhaonan Qu, Kaixiang Lin, Jayant Kalagnanam, Zhaojian Li, Jiayu Zhou, and Zhengyuan Zhou.
  Journal of Artificial Intelligence Research, 2023 [paper]
• Understanding Deep Gradient Leakage via Inversion Influence Functions.
  Haobo Zhang, Junyuan Hong, Yuyang Deng, Mehrdad Mahdavi, and Jiayu Zhou.
  NeurIPS 2023. [paper]
• Subject Harmonization of Digital Biomarkers: Improved Detection of Mild Cognitive Impairment from Language Markers.
  Bao Hoang, Yijiang Pang, Hiroko H. Dodge, and Jiayu Zhou.
  PSB 2024. [paper]
• Transfer Learning in Deep Reinforcement Learning: A Survey.
  Zhuangdi Zhu, Kaixiang Lin, and Jiayu Zhou.
  TPAMI 2023. [paper]
• TransCell: In silico characterization of genomic landscape and cellular responses from gene expressions through a two-step deep transfer learning.
  Shan-Ju Yeh, Shreya Paithankar, Ruoqiao Chen, Jing Xing, Mengying Sun, Ke Liu, Jiayu Zhou, and Bin Chen.
  Genomics Proteomics and Bioinformatics 2023. [preprint]
• How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts.
  Haotao Wang, Junyuan Hong, Jiayu Zhou, and Zhangyang Wang.
  Transactions on Machine Learning Research 2023. [paper]
• Revisiting Data-Free Knowledge Distillation with Poisoned Teachers.
  Junyuan Hong, Yi Zeng, Shuyang Yu, Lingjuan Lyu, Ruoxi Jia, and Jiayu Zhou.
  ICML 2023. [paper]
• Harmony: Heterogeneous Multi-Modal Federated Learning through Disentangled Model Training.
  Xiaomin Ouyang, Zhiyuan Xie, Heming Fu, Sitong Cheng, Li Pan, Neiwen Ling, Guoliang Xing, Jiayu Zhou, and Jianwei Huang.
  MobiSys 2023. [paper]
• Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection.
  Shuyang Yu, Junyuan Hong, Haotao Wang, Zhangyang Wang, and Jiayu Zhou.
  ICLR 2023. [paper]
• MECTA: Memory-Economic Continual Test-Time Model Adaptation.
  Junyuan Hong, Lingjuan Lyu, Jiayu Zhou, and Michael Spranger.
  ICLR 2023. [paper]
• Predicting Progression from Normal to MCI and from MCI to AD using Clinical Variables in the National Alzheimer’s Coordinating Center Uniform Data Set Version 3: Application of Machine Learning Models and a Probability Calculator.
  Yijiang Pang, Roger L. Albin, Mary Sano, Walter A Kukull, Shangyu Shen, Jiayu Zhou, and Hiroko H Dodge.
  Journal of Prevention of Alzheimer's Disease 2023. [accepted]
• Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning.
  Junyuan Hong, Haotao Wang, Zhangyang Wang, and Jiayu Zhou.
  AAAI 2023. [preprint]
• USDNL: Uncertainty-based Single Dropout in Noisy Label Learning.
  Yuanzhuo Xu, Xiaoguang Niu, Jie Yang, Steve Drew, Jiayu Zhou, and Ruizhi Chen.
  AAAI 2023. [to appear]
• Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork.
  Haotao Wang, Junyuan Hong, Aston Zhang, Jiayu Zhou, and Zhangyang Wang.
  NeurIPS 2022. [paper]
• Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling.
  Junyuan Hong, Lingjuan Lyu, Jiayu Zhou, and Michael Spranger.
  NeurIPS 2022. [paper]
• Robust Unsupervised Domain Adaptation from a Corrupted Source.
  Shuyang Yu, Zhuangdi Zhu, Boyang Liu, Anil Jain, and Jiayu Zhou.
  ICDM 2022. [to appear][code]
• Cosmo: Contrastive Fusion Learning with Small Data for Multimodal Human Activity Recognition.
  Xiaomin Ouyang, Xian Shuai, Ivy Wang Shi, Jiayu Zhou, Jianwei Huang, and Guoliang Xing.
  MobiCom 2022. [paper]
• MolSearch: Search-based Multi-objective Molecular Generation and Property Optimization.
  Mengying Sun, Jing Xing, Han Meng, Huijun Wang, Bin Chen, and Jiayu Zhou.
  KDD 2022. [paper]
• Resilient and Communication Efficient Learning for Heterogeneous Federated Systems.
  Zhuangdi Zhu, Junyuan Hong, Steve Drew, and Jiayu Zhou.
  ICML 2022. [paper]
• Dynamic Privacy Budget Allocation Improves Data Efficiency of Differentially Private Gradient Descent.
  Junyuan Hong, Zhangyang Wang, and Jiayu Zhou.
  FAccT 2022. [paper] [ArXiv]
• Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization.
  Junyuan Hong, Haotao Wang, Zhangyang Wang, and Jiayu Zhou.
  ICLR 2022. [paper][code]
• Unsupervised Deep Anomaly Detection by Robust Density Estimation.
  Boyang Liu, Pang-Ning Tan, and Jiayu Zhou.
  AAAI 2022. [paper]
• Self-Adaptive Imitation Learning: Learning Tasks with Delayed Rewards from Sub-Optimal Demonstrations.
  Zhuangdi Zhu, Kaixiang Lin, Bo Dai, and Jiayu Zhou.
  AAAI 2022. [paper]
• Persona Authentication through Generative Dialogue.
  Fengyi Tang, Lifan Zeng, Fei Wang, Jiayu Zhou.
  Preprint, 2021 [arXiv]
• FedDL: Federated Learning via Dynamic Layer Sharing for Human Activity Recognition.
  Linlin Tu, Xiaomin Ouyang, Jiayu Zhou,, Yuze He, and Guoliang Xing.
  SenSys 2021 [paper]
• Inferring the Importance of Product Appearance with Semi-supervised Multi-modal Enhancement: A Step Towards the Screenless Retailing.
  Yongshun Gong, Jinfeng Yi, Dong-Dong Chen, Jian Zhang, Jiayu Zhou, Zhi-Hua Zhou.
  MM 2021 [paper]
• Automatic Detection of Alzheimer’s Disease Us- ing Spontaneous Speech Only.
  Jun Chen, Jieping Ye, Fengyi Tang, and Jiayu Zhou.
  Interspeech 2021 [paper]
• FedFace: Collaborative Learning of Face Recognition Model.
  Divyansh Aggarwal, Jiayu Zhou, and Anil K. Jain.
  IJCB 2021 [paper]
• Federated Adversarial Debiasing for Fair and Transferable Representations.
  Junyuan Hong, Zhuangdi Zhu, Shuyang Yu, Zhangyang Wang, Hiroko Dodge, and Jiayu Zhou.
  KDD 2021 [paper][code]
• MoCL: Contrastive Learning on Molecular Graphs with Multi-level Domain Knowledge.
  Mengying Sun, Jing Xing, Huijun Wang, Bin Chen, and Jiayu Zhou.
  KDD 2021 [paper][code]
• Data-Free Knowledge Distillation for Heterogeneous Federated Learning.
  Zhuangdi Zhu, Junyuan Hong, and Jiayu Zhou.
  ICML 2021 [paper]
• Learning Deep Neural Networks under Agnostic Corrupted Supervision.
  Boyang Liu, Mengying Sun, Ding Wang, Pang-Ning Tan, and Jiayu Zhou.
  ICML 2021 [paper]
• Federated Learning's Blessing: FedAvg has Linear Speedup.
  Zhaonan Qu, Kaixiang Lin, Jayant Kalagnanam, Zhaojian Li, and Jiayu Zhou.
  DPML 2021, ICLR 2021 Workshop [paper]
• RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection.
  Boyang Liu, Ding Wang, Kaixiang Lin, Pang-Ning Tan, and Jiayu Zhou.
  IJCAI 2021. [paper]
• ClusterFL: A Similarity-Aware Federated Learning System for Human Activity Recognition.
  Xiaomin Ouyang, Zhiyuan Xie, Jiayu Zhou, Jianwei Huang, and Guoliang Xing.
  MobiSys 2021. [paper]
• Learning Model-Based Privacy Protection under Budget Constraints.
  Junyuan Hong, Haotao Wang, Zhangyang Wang, and Jiayu Zhou.
  AAAI 2021. [paper]
• Off-Policy Imitation Learning from Observations.
  Zhuangdi Zhu, Kaixiang Lin, Bo Dai, and Jiayu Zhou.
  NeurIPS. 2020 [paper]
• Model-Protected Multi-Task Learning.
  Jian Liang, Ziqi Liu, Jiayu Zhou, Xiaoqian Jiang, Changshui Zhang, and Fei Wang.
  IEEE TPAMI 2020 [paper]
• Adversarial Precision Sensing with Healthcare Applications.
  Fengyi Tang, Lifan Zeng, Fei Wang, and Jiayu Zhou.
  ICDM 2020.
• Robust Collaborative Learning with Noisy Labels.
  Mengying Sun, Jing Xing, Bin Chen, and Jiayu Zhou.
  ICDM 2020.
• Multimodal Learning with Incomplete Modalities by Knowledge Distillation.
  Qi Wang, Liang Zhan, Paul Thompson, and Jiayu Zhou.
  KDD 2020.
• Scalable Diagnostic Screening of Mild Cognitive Impairment using AI Dialogue Agent.
  Fengyi Tang, Ikechukwu Uchendu, Fei Wang, Hiroko H. Dodge, and Jiayu Zhou.
  Nature - Scientific Reports, 2020 [paper]
• Ranking Policy Gradient.
  Kaixiang Lin and Jiayu Zhou.
  ICLR 2020. [Paper]
• Shoreline: Data-Driven Threshold Estimation of the Online Reserves of Cryptocurrency Trading Platforms.
  Xitong Zhang, He Zhu, and Jiayu Zhou.
  AAAI 2020. [paper]
• Boosted Trajectory Calibration for Traffic State Estimation.
  Xitong Zhang, Liyang Xie, Zheng Wang, and Jiayu Zhou.
  ICDM 2019.
• Retaining Privileged Information for Multi-Task Learning.
  Fengyi Tang, Cao Xiao, Fei Wang, Jiayu Zhou, and Li-Wei Lehman.
  KDD 2019.
• 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.
• 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.
• Augmented Multi-Task Learning by Optimal Transport.
  Boyang Liu, Pang-Ning Tan, and Jiayu Zhou.
  SDM 2019.
• Deep Multi-view Information Bottleneck.
  Qi Wang, Claire Boudreau, Qixing Luo, Pang-Ning Tan, and Jiayu Zhou.
  SDM 2019.
• Online Active Learning cross Multiple Tasks.
  Peng Yang, Peilin Zhao, Jiayu Zhou, and Xin Gao.
  AAAI 2019.
• Differentially Private Generative Adversarial Network.
  Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou.
  Preprint, 2018 [arXiv]
• Boosted Sparse and Low-Rank Tensor Regression.
  Lifang He, Kun Chen, Wanwan Xu, Jiayu Zhou, and Fei Wang.
  NIPS 2018. [Paper]
• HHNE: Heterogeneous Hyper-Network Embedding.
  Inci M Baytas, Cao Xiao, Fei Wang, Anil K. Jain, and Jiayu Zhou.
  ICDM 2018. [Paper] [Code]
• Imputing Structured Missing Values in Spatial Data with Clustered Adversarial Matrix Factorization.
  Qi Wang, Pang-Ning Tan, and Jiayu Zhou.
  ICDM 2018. [Paper]
• Distributed Data Vending on Blockchain.
  Jiayu Zhou, Fengyi Tang, He Zhu, Ning Nan, and Ziheng Zhou.
  IEEE Blockchain 2018 [arXiv]
• 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, Accepted. [Preprint]
• Enhancing Predictive Modeling of Nested Spatial Data through Group-Level Feature Disaggregation.
  Boyang Liu, Pang-Ning Tan, and Jiayu Zhou.
  KDD 2018, Accepted.
• Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders
  Tengfei Ma, Cao Xiao, Jiayu Zhou, and Fei Wang.
  IJCAI 2018, Accepted.
• The added value of diffusion MRI in evaluating mild cognitive impairment: a multi-cohort validation.
  Qi Wang, Lei Guo, Paul M. Thompson, Clifford R. Jack Jr., Hiroko Dodge, Liang Zhan, and Jiayu Zhou.
  Journal of Alzheimer's Disease (JAD), 2018. [Paper]
• Predictive Modeling in Urgent Care: A Comparative Study of Machine Learning Approaches
  Fengyi Tang, Cao Xiao, Fei Wang, and Jiayu Zhou.
  Journal of the American Medical Informatics Association JAMIA Open 2018. [Paper]
• EdgeChain: Blockchain-based Multi-vendor Mobile Edge Application Placement.
  He Zhu, Changcheng Huang, and Jiayu Zhou.
  Accepted by NetSoft 2018 [arXiv]
• An MCEM Framework for Drug Safety Signal Detection and Combination from Heterogeneous Real World Evidence.
  Cao Xiao, Ying Li, Inci Baytas, Jiayu Zhou, and Fei Wang
  Nature - Scientific Reports 2018 [Paper] [Code]
• Multi-Task Learning based Survival Analysis for Predicting Alzheimer’s Disease Progression with Multi-Source Block-wise Missing Data. Yan Li, Tao Yang, Jiayu Zhou, and Jieping Ye.
  SDM 2018 [Paper]
• Collaborative Deep Reinforcement Learning.
  Kaixiang Lin, Shu Wang, and Jiayu Zhou.
  Preprint, 2017 [PDF]
• Recurrent Encoder-Decoder Networks for Time-Varying Dense Prediction.
  Tao Zeng, Bian Wu, Jiayu Zhou, Ian Davidson, and Shuiwang Ji.
  ICDM 2017 [Paper]
• Multi-Level Multi-Task Learning for Modeling Cross-Scale Interactions in Nested Geospatial Data.
  Shuai Yuan, Jiayu Zhou, Pang-Ning Tan, Emi Fergus, Tyler Wagner, and Patricia Soranno.
  ICDM 2017 [Paper]
• Multi-task Survival Analysis.
  Lu Wang, Yan Li, Jiayu Zhou, Dongxiao Zhu, and Jieping Ye.
  ICDM 2017 [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]
• Missing Modalities Imputation via Cascaded Residual Autoencoder.
  Luan Tran, Xiaoming Liu, Jiayu Zhou, and Rong Jin.
  CVPR 2017 [Paper]
• An RNN Architecture with Dynamic Temporal Matching for Personalized Predictions of Parkinson's Disease.
  Chao Che, Cao Xiao, Jian Liang, Bo Jin, Jiayu Zhou, and Fei Wang.
  SDM 2017 [Paper]
• WISDOM: Weighted Incremental Spatio-Temporal Multi-Task Learning via Tensor Decomposition.
  Jianpeng Xu, Jiayu Zhou, Pang-Ning Tan, Xi Liu, and Lifeng Luo.
  IEEE Big Data 2016 [Paper] Best Paper Award
• 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, and 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, and Jieping Ye.
  KDD 2014 [Paper] [Code].
• Efficient Multi-Task Feature Learning with Calibration.
  Pinghua Gong, Jiayu Zhou, and 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. [Paper]
• Active Matrix Completion.
  Shayok Chakraborty, Jiayu Zhou, Vineeth Balasubr., Sethuraman Panch., Ian Davidson, and Jieping Ye
  ICDM 2013 [Paper]
• Lasso Screening Rules via Dual Polytope Projection.
  Jie Wang, Jiayu Zhou, Peter Wonka, and 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, and 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. [Paper] [Code]
• 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, and 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]