Jun Huan
Academic Editor

Jun Huan


Summary

Dr. Jun (Luke) Huan is a Professor in the Department of Electrical Engineering and Computer Science at the University of Kansas. He directs the Data Science and Computational Life Sciences Laboratory at KU Information and Telecommunication Technology Center (ITTC). He holds courtesy appointments at the KU Bioinformatics Center, the KU Bioengineering Program, and a visiting professorship from GlaxoSmithKline plc. Dr. Huan received his Ph.D. in Computer Science from the University of North Carolina.
Dr. Huan's research is recognized internationally. He was a recipient of the prestigious National Science Foundation Faculty Early Career Development Award in 2009. His group won the Best Student Paper Award at the IEEE International Conference on Data Mining in 2011 and the Best Paper Award (runner-up) at the ACM International Conference on Information and Knowledge Management in 2009. His work appeared at mass media including Science Daily, R&D magazine, and EurekAlert (sponsored by AAAS). Dr. Huan's research was supported by NSF, NIH, DoD, and the University of Kansas.
Starting January 2016, Dr. Huan serves as a Program Director in NSF/CISE/IIS and is on leave from KU.

Bioinformatics Computational Biology Data Mining & Machine Learning Data Science

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Work details

Professor

University of Kansas
Department of Electrical Engineering and Computer Science
My research interest is to study and apply computational and theoretical principles for accelerating knowledge discovery from data and for enabling actions on important societal problems. We work extensively on predictive analytics, aiming to generate actionable and testable hypotheses based on data and previous experience. The recent algorithmic work from my group could be found on multi-view learning, transfer learning, multi-task learning, boosting with structural sparsity, and learning with big graph data. The current application focus in my group is in the translational science. We aim to advance data analytics in the better understanding of the connections between biological systems, disease physiology, intervention, and therapeutics, and to evaluate the clinical and social impacts of the understanding at multiple levels. Though the problem set is diverse, the common threads of our work are geometric and probabilistic representations of data, effective feature generation, multimodal data integration, sparse model selection and averaging, and learning generative and discriminative models on (Riemannian) manifolds. Much of our work addresses three core problems in machine learning and data mining: stable pattern identification with structured input and output, information fusion with multiple data sources, and system support for big data analytics.

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