Rate My Professor Mario Flores

Mario Flores, Ph.D., serves as an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Texas at San Antonio (UTSA), with a pioneering joint appointment in the Department of Biomedical Engineering within the Klesse College of Engineering and Integrated Design. He joined the UTSA faculty in the spring 2020 semester. His professional expertise encompasses bioinformatics, digital signal processing, and artificial intelligence. Before assuming his current position, Flores was a Research Fellow at the National Center for Biotechnology Information (NCBI) at the National Institutes of Health (NIH) in Bethesda, Maryland, concentrating on computational biology and the identification of human genomic regulatory regions. During his doctoral studies, he conducted research at the Greehey Children’s Cancer Research Institute at UT Health San Antonio, where he contributed to the development of MeT-DB, the first published database of transcriptome methylation in mammalian cells.

Flores obtained his Ph.D. in electrical engineering from UTSA in 2015, an M.S. in applied mathematics from UTSA, and a B.S. in electronic engineering from Universidad Autónoma Metropolitana in Mexico City. His research focuses on computational biology and evolution, spatial transcriptomics, cancer biology networks, RNA methylation, deep learning applications in genomics and transcriptomics, and machine learning for predicting disease phenotypes such as diabetic foot ulcers and COVID-19 lung pathology. Key publications include 'Timing of surgery following SARS-CoV-2 infection: an international prospective cohort study' (Anaesthesia, 2021), 'Global variation in postoperative mortality and complications after cancer surgery' (The Lancet, 2021), 'Deep learning tackles single-cell analysis—a survey of deep learning for scRNA-seq analysis' (Briefings in Bioinformatics, 2022), 'MeT-DB: a database of transcriptome methylation in mammalian cells' (Nucleic Acids Research, 2015), 'Transformer and graph variational autoencoder to identify microenvironments: A deep learning protocol for spatial transcriptomics' (2025), 'Multimodal Identification of Molecular Factors Linked to Severe Diabetic Foot Ulcers Using Artificial Intelligence' (2024), and contributions to AI models predicting dental composite performance for personalized dental care (2025). With over 300 citations across more than 30 publications, his work influences interdisciplinary advancements in biomedical engineering and computational analysis. Flores also mentors graduate students and participates in programs like S-STEM and MARC.