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.
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.
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
Develop efficient and effective machine learning techniques to build predictive models and perform computation phenotyping from large-scale electronic medical records.More Info
Analyze trajectories behavior from big traffic data and develop high performance predictive models for traffic status prediction and dispatch optimization.More Info
Develop spatiotemporal analytics tools to fuse heterogeneous data from different scales and model the dynamics of lake nutrients in United States.More Info
Develop infrastructures and algorithmic foundations for efficient big data analytics on blockchain technology, especially on healthcare and network applications.More Info
ILLIDAN Lab is currently sponsored by National Science Foundation, Office of Naval Research, Didi Chuxing and VeChain Foundation. We would also like to thank NVIDIA Corporation for the donation of GPU cards.