Apr 21, 2017
Lopata Hall, Room 101
Large-Scale Machine Learning for Imaging Genetics
Department of Computer Science and Engineering
University of Texas at Arlington
Data science is accelerating the translation of biological and biomedical data to advance the detection, diagnosis, treatment, and prevention of diseases, including the recently announced BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative and Precision Medicine Initiative. Sparsity is one of the intrinsic properties of real-world data, thus the sparse learning has recently emerged as a powerful tool to obtain models of high-dimensional data with high degree of interpretability at low computational cost, and provide great opportunities to analyze the big, complex, and diverse datasets. To address the challenging problems in current biomedical data science, we proposed several novel large-scale structured sparse learning models for multi-dimensional data integration, heterogeneous multi-task learning, group/graph structured data analysis, and longitudinal feature learning. Meanwhile, to deal with the big data computations, we proposed distributed asynchronous stochastic gradient and coordinate descent methods for efficiently solving convex and non-convex problems.
We applied our new structured sparse learning models to analyze the multi-modal and longitudinal neuroimaging and genome-wide array data in Imaging Genetics and discover the phenotypic and genotypic biomarkers to characterize the neurodegenerative process in the progression of Alzheimer's disease and other complex brain disorders. We also utilized our new machine learning models to analyze the Electronic Medical Records (EMR) for predicting the heart failure patients' readmission and drug side effects, identify the histopathological image markers and the multi-dimensional cancer genomic biomarkers in The Cancer Genome Atlas (TCGA) for precision medicine, predict performance and guide design of nanoparticle synthesis in Materials Genome research, and detect the DTI and fMRI based brain circuitry patterns in Human Connectome.
Dr. Heng Huang is a Professor of Computer Science and Engineering (CSE) at University of Texas at Arlington (UTA), and an Adjunct Professor of Clinical Sciences at University of Texas Southwestern Medical Center (UTSW). Dr. Huang received the PhD degree in Computer Science at Dartmouth College and then joined UTA as an assistant professor. His research areas include imaging genetics, medical image analysis, bioinformatics, machine learning and data mining, health informatics, neuroinformatics, and precision medicine. Dr. Huang has published more than 130 papers in top-tier conferences and many papers in premium journals, such as NIPS, ICML, KDD, RECOMB, ISMB, IPMI, MICCAI, IJCAI, AAAI, CVPR, ICCV, SIGIR, Bioinformatics, IEEE Trans. on Medical Imaging, Medical Image Analysis, TKDE, etc. As PI, Dr. Huang is leading an NIH funded $2 million R01 project on imaging genomics based complex brain disorder study, multiple (6 as PI and 3 as Co-PI) NSF funded projects on biomedical data science, neuroimaging, big data mining, precision medicine, electronic medical record data mining and privacy-preserving, computational biology, smart healthcare, cyber physical system, and also industry (e.g. Con Edison in New York City) funded projects on computational sustainability, smart metering, and smart grid.