Advances in the biological sciences are accelerating at an unparalleled rate, producing massive amounts of data and providing unprecedented opportunities for major advances in medical and environmental domains. Sophisticated computer science, statistical, and mathematical tools are indispensable for reasoning about these data and unveiling hidden truths that are unreachable using experimental and theoretical approaches alone. Computational biology, biostatistics, and bioinformatics are overlapping fields aimed at filling this demand. More broadly, the adaptation and fusion of combinatorial optimization, artificial intelligence, statistics and applied mathematics, imaging, evolutionary computation, machine learning, mathematical programming, data mining and deep learning techniques provide potential for the production of novel specialized solutions. Our current research includes (a) network modeling, mixed integer linear programming, statistics, and clustering applied to combinatorial genetics, gene co-expression, combinatorial proteomics, and haplotype inference problems arising in the research of Alzheimer disease, psoriasis, hypertensive heart failure, vitamin D metabolism, and human population genetics, and (b) protein structure modeling and protein contact prediction using techniques like simulated annealing algorithm, deep learning techniques, and convolutional-neural networks. The following ffaculty members involved in biological data research: Badri Adhikari and Sharlee Climer