Overview
Projects
Computers
Research Stories
Collaboration
Software & Servers
Our research focuses on development of deep learning, machine learning, data mining, and evolutionary algorithms for scientific discovery and engineering innovation in materials science, bioinformatics, health informatics, function genomics, medical and health sciences, engineering designs, intelligent manufacturing and etc. We are specialized in developing state-of-art pattern recognition, classification, regression, predictive modeling, inverse design using state-of-the-art models such as deep neural network models, generative adversarial networks, graph neural networks, genetic programming, symbolic regression. We welcome collaborators with focus on experimental or computational studies of materials or biological mechanisms. Just drop me an email at: jianjunh at cse.sc.edu
Funding Source

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NSF



EAGER
Inverse Design of inorganic materials and MOF: given a target property, how to search the materials composition and structure that meets this property target. We use deep learning and generative adversarial network approach to learn the chemical composition and crystal structure rules to generate new materials composition and structures.
NSF



USC
Thermal Materials Discovery via Deep Learning based High-Throughput Computational Screening.
DOE



USC
Data-science enabled investigation of the mechanisms for multiscale ion transport in functional electrolytes
NIH



USC
Big data analytics of HIV treatment gaps in south carolina: identification and prediction. Here we develop machine learning and big data algorithms to mine the large medical records data of HIV patients in South Carolina to identify missed care opportunities.
SC EPScoR



GEAR
Development of Biomaterials Informatics for Human Cardiac Organogenesis. We apply image segmentation and Bayesian optimization algorithm to predict the optimize the favoriable growth conditions to grow biomaterials.
SC EPScoR.



GEAR-CRP
Deep Learning for Discovery of Noncentrosymmetric Materials with Second-order Nonlinear Optical Behavior.
SC DOT.



GEAR-CRP
South Carolina Department of Transportation, "Big data analytics of SCDOT equipment and vehicles". We apply data mining and machine learning algorithms to make prediction models of the SCDOT equipment maintenance demand. Django based web service is developed to facilitate the query and analytics of yearly report.
NSF



CAREER
NSF CAREER Award: Computational Analysis and Prediction of Genome-Wide Protein Targeting Signals and Localization.