About Illidan Lab

The Intelligent Data Analytics (ILLIDAN) Lab @ Michigan State University, directed by Prof. Jiayu Zhou, conducts cutting-edge research on machine learning methodologies for big data analytics. The main research theme of ILLIDAN Lab is Convergent Data Science: enhancing decision making for data science through establishing the closed-loop flow of informatics among key components of human, data, and analytics.

News

Opening: ILLIDAN lab is currently looking for motivated Ph.D. students and post-doctoral researchers on machine learning research. Check the whitepaper for details of research, life at ILLIDAN lab. Interested candidates please email your CV and transcripts.

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.

Multi-task and Transfer Learning

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
Drug Repurposing

Develop data-driven molecular fingerprints, molecular generation models, and predictive models for predicting molecular properties and gene regulation, which lead to drug discovery and repurposing.

Data-Driven Fingerprint

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
Completed Projects

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
Jiayu Zhou
Jiayu Zhou   Lab Head, Associate Professor


Areas of Interest: Machine learning, data mining, biomedical informatics
Links: Website, Google Scholar, GitHub


Lab Members
Guangliang Liu
Guangliang Liu   Lab member since 2021, Ph.D. Student


Areas of Study: Natural Language Processing and Machine Learning

Han Meng
Han Meng   Lab member since 2022, M.Sc. Student


Areas of Study: Bioinformatics

Haobo Zhang
Haobo Zhang   Lab member since 2022, Ph.D. Student


Areas of Study: Federated Learning, Privacy-Preserving Learning

Junyuan Hong
Junyuan Hong   Lab member since 2018, Ph.D. Student


Areas of Study: subspace learning, privacy
Links: Website, Google Scholar, GitHub

Shuyang Yu
Shuyang Yu   Lab member since 2020, Ph.D. Student


Areas of Study: privacy and machine learning

Siqi Liang
Siqi Liang   Lab member since 2022, Ph.D. Student


Areas of Study: Federated Learning, Robust Learning
Links: Google Scholar, Github, Homepage

Yijiang Pang
Yijiang Pang   Lab member since 2021, Ph.D. Student


Areas of Study: data science, efficient machine learning
Links: Website

Zhiyu Xue
Zhiyu Xue   Lab member since 2021, Ph.D. Student


Areas of Study: privacy and machine learning
Links: Website, Google Scholar, Linkedin


Visitor
Hoàng Cao Bảo
Hoàng Cao Bảo   Professorial Assistant since 2021, MSU


Areas of Study: natural language and dementia


Alumni
Andy Tang
Andy Tang   Lab member since 2016, Ph.D. Graduated in 2021


Areas of Study: biomedical informatics
Links: Website, Google Scholar, GitHub

Boyang Liu
Boyang Liu   Lab member since 2017, Ph.D. Graduated in 2022


Areas of Study: spatiotemporal modeling, limnology
Links: GitHub

Ikechukwu Uchendu
Ikechukwu Uchendu   Lab member since 2018, M.S. Graduated in 2020


Areas of Study: natural language processing
Links: GitHub, LinkedIn

Inci Baytas
Inci Baytas   Lab member since 2015, Ph.D. Graduated in 2019


Areas of Study: recurrent neural network, hyper-edge network, medical informatics
Links: Website, Google Scholar, GitHub

Jianpeng Xu
Jianpeng Xu   Lab member since 2015, Ph.D. graduated in 2016


Areas of Study: multi-task learning, spatiotemporal analysis
Links: Website, Google Scholar, LinkedIn

Kaixiang Lin
Kaixiang Lin   Lab member since 2015, Ph.D. Graduated in 2020


Areas of Study: deep reinforcement learning, traffic analytics
Links: Website, Google Scholar, GitHub

Mengying Sun
Mengying Sun   Lab member since 2016, Ph.D. Graduated in 2022


Areas of Study: statistical modeling, imaging genetics
Links: Website, GitHub

Qi Wang
Qi Wang   Lab member since 2015, Ph.D. Graduated in 2020


Areas of Study: multi-modality data fusion, limnology, biomedical informatics
Links: GitHub

Zhuangdi Zhu
Zhuangdi Zhu   Lab member since 2018, Ph.D. Graduated in 2022


Areas of Study: applied machine learning
Links: Linkedin


Friends
Abhi Shukul   High School Research Volunteer since 2020, Okemos High
He Zhu   Consultant since 2018, University of Calgary
Jiahua Chen   Visited in 2018, Master student in UMich
Jun Chen   Visiting since 2017, Ph.D. student in UMich
Laura Danila   Summer Intern in 2019
Lifan Zeng   Lab member since 2018, Undergraduate Student
Lisa Kelly   Volunteered 2018-2019, graduate from MSU
Liyang Xie   Lab member since 2016, M.Sc graduated in 2019
Matthew Dirisio   Professorial Assistant since 2020, MSU
Pranav Shukla   Volunteer since Fall 2021
Shu Wang   Visiting/consultant since 2016, Ph.D. student in Rugters
Ugo Uchendu   Volunteer since Fall 2021
Xiang Cheng   Visited in 2017 summer, undergrad in Uni. of Glasgow
Preprints
Selected Publications
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