About Illidan Lab

The Intelligent Data Analytics (ILLIDAN) Lab, affiliated with Computer Science & Engineering, Michigan State University conducts cutting-edge research on machine learning methodologies for big data analytics. ILLIDAN lab designs scalable machine learning algorithms, creates open source machine learning software, and develops powerful machine learning for applications in health informatics, big traffic analytics, and other scientific areas.

Opening: ILLIDAN lab is currently looking for motivated Ph.D. students and post-doctoral researchers on machine learning research. Interested candidates please email your CV and transcripts.

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Research Highlights

Research in ILLIDAN Lab tackles a broad range of machine learning challenges, including but not limited to deep learning, sparse learning for high dimensional data analysis, transfer learning (multi-task and lifelong learning), information fusion from multiple data modalities, matrix completion and collaborative filtering, spatiotemporal analysis.

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.

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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Big Traffic Data Analytics

Analyzing trajectories behavior from big traffic data and develop high performance predictive models for traffic status prediction and dispatch optimization.

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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 is currently sponsored by National Science Foundation and Office of Naval Research. We would also like to thank NVIDIA Corporation for the donation of GPU cards.

Investigator
Jiayu Zhou
Jiayu Zhou   Lab Head, Assistant Professor


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


Lab Member
Andy Tang
Andy Tang   Lab member since 2016, D.O-Ph.D. Student


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

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


Areas of Study: spatiotemporal modeling, limnology
Links: GitHub

Inci Baytas
Inci Baytas   Lab member since 2015, Ph.D. Candidate


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

Kaixiang Lin
Kaixiang Lin   Lab member since 2015, Ph.D. Student


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

Liyang Xie
Liyang Xie   Lab member since 2016, Ph.D. Student


Areas of Study: differential privacy, multi-task learning
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

Qi Wang
Qi Wang   Lab member since 2015, Ph.D. Student


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


Undergraduate Students

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


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


Alumni
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

Selected Publications
Last updated on Sept 2017