Tel: +1 803-777-7304
Fax: +1 803-777-3767

Storey Innovation Center 2240
Department of Computer Science and Engineering
University of South Carolina
Columiba, SC, 29208

Welcome to Machine Learning and Evolution Laboratory (MLEG) at the Department of Computer Science and Engineering, University of South Carolina. Our research focuses on development of machine learning, data mining, and evolutionary algorithms for knowledge discovery and innovation in bioinformatics, material science, medical and health sciences, engineering designs, and etc. We have worked on DNA regulatory motif discovery, microarray analysis, phenotype prediction/computational disease diagnosis, and gene/protein function prediction, X-ray data based phase mapping, protein-DNA, protein-ligand binding, breast cancer diagnosis based on image processing, cell segmentation, DFT based material doping, and etc.

Currently, we are working on protein-ligand binding for drug design, X-ray highthroughtput data analysis in material informatics, breast cancer diagnosis based on histological images, deep learning and its applications in computer vision, Natural Language Processing, Audio/Sound Pattern Recognition, and fault diagnosis. We seek to develop and apply the latest artificial intelligence algorithms such as machine learning, deep learning, genetic algorithms, genetic programming, dimension reduction, non-linear mapping, sparse coding, and etc to solve challenging real-world problems.

keywords: Machine learning, Data mining, Deep learning, Bioinformatics, Material Informatics, Health Informatics;

New software Online GPhase: Phase Mapping Program. (Feb 11, 2017).

Lewis Cawthorne won the University Magellan Scholarship on developing text mining system for protein sorting motif analysis.

Dr. Hu was awarded NSF Career Award for CAREER: Computational Analysis and Prediction of Genome-Wide Protein Targeting Signals and Localization.

New Bioinformatics Server AAEnrich for Physichemical property enrichment test for protein sorting motifs.