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.

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Biomedical Informatics

Develop efficient and effective machine learning techniques to build predictive models and perform computation phenotyping from large-scale electronic medical records.

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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
Limnology

Develop spatiotemporal analytics tools to fuse heterogeneous data from different scales and model the dynamics of lake nutrients in United States.

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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
Boyang Liu
Boyang Liu   Lab member since 2017, Ph.D. Student


Areas of Study: spatiotemporal modeling, limnology
Links: GitHub

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


Areas of Study: Natural Language Processing and Machine Learning

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


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

Mengying Sun
Mengying Sun   Lab member since 2016, Ph.D. Student


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

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


Areas of Study: privacy and machine learning

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

Zhuangdi Zhu
Zhuangdi Zhu   Lab member since 2018, Ph.D. Student


Areas of Study: applied machine learning
Links: Linkedin


Visitor
Abhi Shukul
Abhi Shukul   High School Research Volunteer since 2020, Okemos High


Areas of Study: behavior analysis
Links: Github

He Zhu
He Zhu   Consultant since 2018, Cisco Systems


Areas of Study: computer networks, optimization, blockchain
Links: Website, Google Scholar

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


Areas of Study: natural language and dementia

Jun Chen
Jun Chen   Visiting since 2017, Ph.D. student in UMich


Areas of Study: multi-source information fusion, disease progression
Links: Website, Github

Matthew Dirisio
Matthew Dirisio   Professorial Assistant since 2020, MSU


Areas of Study: natural language and dementia

Pranav Shukla
Pranav Shukla   Volunteer since Fall 2021


Areas of Study: machine learning, algorithmic trading

Ugo Uchendu
Ugo Uchendu   Volunteer since Fall 2021


Areas of Study: machine learning


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


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

Jiahua Chen
Jiahua Chen   Visited in 2018, Master student in UMich


Areas of Study: natural language processing, deep learning
Links: Website, 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

Laura Danila
Laura Danila   Summer Intern in 2019


Areas of Study: predictive modeling, limnology

Lifan Zeng
Lifan Zeng   Lab member since 2018, Undergraduate Student


Areas of Study: deep learning, satellite image analysis
Links: GitHub, LinkedIn

Lisa Kelly
Lisa Kelly   Volunteered 2018-2019, graduate from MSU


Areas of Study: web development, natural language interface
Links: Linkedin, Github

Liyang Xie
Liyang Xie   Lab member since 2016, M.Sc graduated in 2019


Areas of Study: differential privacy, multi-task learning
Links: Website, Google Scholar, 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

Shu Wang
Shu Wang   Visiting/consultant since 2016, Ph.D. student in Rugters


Areas of Study: computer vision, deep learning
Links: Website, Google Scholar

Xiang Cheng
Xiang Cheng   Visited in 2017 summer, undergrad in Uni. of Glasgow


Areas of Study: Natural languge processing, medical informatics
Links: Github

Preprints
Selected Publications
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