Faculty and Staff

T. L. Wallace, Ph.D.

Chair, Biomedical Data Science Department
Professor of Computational Sciences

Curriculum Vitae


Ph.D., Tennessee State University
M.S.,  Tennessee Technological University
B. S., Tennessee Technological University

Dr. T. L. Wallace’s career background includes extensive experience in academics, industry and research. In the early years, Dr. Wallace served as an investigator and research consultant to both NASA and DOD in support of developing high performance models for atomic and molecular physics phenomenon via tensor processing deploying neural networks for near real-time predictive algorithms of electromagnetic spectra and reaction chemistry. Later experience included support via NSF, DOE, and NIH funded research as well as various non-governmental organizations in diverse areas such as genomics and protoemics.

Dr. Wallace received the Dean’s Award in 2015 for Researcher of the Year. His 2014 dissertation topics and published papers at Tennessee State University included award winning topics in subspace mathematical multivariate statistical machine learning algorithms in high dimensions with applications in radiological imaging algorithms and  genetic sequence deep learning. He is credited with more than 50 scientific and technical publications in various conferences, journals, technical reports and book chapters.

Dr. Wallace is the author of a textbook e-book for online teaching and learning © 2015-2021, now over 400 pages, entitled “Data Mining and Deep Learning, Theory and Practice: Lecture Notes with Julia, Python, and R”. The work includes:application and code examples from bioinformatics, biomedical image processing, genomics, and proteomics; methods from AI re-enforcement learning (RL), deep-learning via decision tree decomposition of images and higher rank tensors, and neural network examples; and, special attention to parallel processing. Additional topics are being added on a regular basis.

Research Interests

  • Machine Learning Algorithms and Applications in Medicine and Biology
  • Artificial Intelligence Algorithms and Models for Biomedical Applications
  • Computational and Theoretical Biology, Quantum Computing
  • Atomic, Molecular, and Protein Modeling, e.g. for drug efficacy

Research Labs

Biomedical Data Science Lab