Welcome to Our Lab
My research interest is to study and apply computational and theoretical principles to accelerate knowledge discovery from data. The recent algorithmic work from my group could be found on transfer learning, multitask learning, and boosting with structural sparsity. In theory, we are interested in the application of geometric and algebraic approaches in statistical inference and model selection.
The current application focus in my group is in the health care. We aim to advance the understanding of the connections between the disease physiology and biological systems, and to evaluate the clinical and social impacts of the understanding at multiple levels.
- At the molecular level, we focus on mapping out biomolecule interactions at the whole genome level and identifying the connections between biomolecule interactions and clinic endpoints such as disease diagnostics and personalized medicine development.
- At the system level, we focus on identifying the dynamic control mechanisms of complex systems to improve the modeling and engineering of biological systems.
- At the population level, we focus on identifying biomarkers for disease diagnostics, prognostics, and treatment.
In our investigations we rely on high-throughput and low-throughput experimental data. These data include genome structural variations, protein-ligand interactions, chemical toxicity, disease phenotypes, clinical trials, and social networks in the health care.
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 in vector and kernel spaces, and learning generative and discriminative models on 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 large-scale knowledge discovery.
Contact: Dr. Jun (Luke) Huan (jhuan at ittc.ku.edu).
Latest News
5/4/12 Our paper "Inductive Multi-Task Learning with Multiple View Data" by the graduate student Jintao Zhang was accepted by SIGKDD 2012.
11/11/11 Our paper "Structured Feature Selection and Task Relationship Inference for Multi-Task Learning" by the graduate student Hongliang Fei won the Best Student Paper Award at the IEEE International Conference on Data Mining (ICDM), 2011.