About
Biography
Surya Prasath, PhD, is a mathematician with expertise in the application areas of image processing and computer vision. He received his PhD in mathematics from the Indian Institute of Technology Madras, India in 2009 (defended in March 2010). He has been a postdoctoral fellow at the Department of Mathematics, University of Coimbra, Portugal, for two years. From 2012 to 2017 he was with the Computational Imaging and VisAnalysis (CIVA) Lab at the University of Missouri, USA and worked on various mathematical image processing and computer vision problems. He had summer fellowships/visits at Kitware Inc. NY, USA, The Fields Institute, Canada, and IPAM, University of California Los Angeles (UCLA), USA. His main research interests include nonlinear PDEs, regularization methods, inverse and ill-posed problems, variational, PDE based image processing, and computer vision with applications in remote sensing, biomedical imaging domains.
BSc: Mathematics, University of Madras, India, 1999-2002.
MSc: Mathematics, Indian Institute of Technology Madras, Chennai, India, 2002-2004.
PhD: Mathematics, Indian Institute of Technology Madras, Chennai, India, 2004-2009.
Postdoc: Mathematics, University of Coimbra, Portugal, 2009-2011.
Postdoc: Computer Science, University of Missouri-Columbia, Columbia, MO, 2012-2015.
Interests
Image processing; computer vision; biomedical image analysis; machine learning
Research Areas
Biomedical Informatics
Publications
Machine Learning-Based Prediction of Pediatric Ulcerative Colitis Treatment Response Using Diagnostic Histopathology. Gastroenterology. 2024; 166:921-924.e4.
Artificial intelligence image-based prediction models in IBD exhibit high risk of bias: A systematic review. Computers in Biology and Medicine. 2024; 171:108093.
LungMAP Portal Ecosystem: Systems-level Exploration of the Lung. American Journal of Respiratory Cell and Molecular Biology. 2024; 70:129-139.
maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks. PLoS Computational Biology. 2023; 19:e1010863.
Artificial intelligence in bronchopulmonary dysplasia- current research and unexplored frontiers. Pediatric Research. 2023; 93:287-290.
CellDrift: inferring perturbation responses in temporally sampled single-cell data. Briefings in Bioinformatics. 2022; 23:bbac324.
DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity. Briefings in Bioinformatics. 2021; 22:bbab160.
DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity. Briefings in Bioinformatics. 2021; 22:bbab160.
DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity. Briefings in Bioinformatics. 2021; 22:bbab160.
From the Blog
DeepImmuno: a Tool for Creating Custom T-cell Vaccines
Surya Prasath, PhD, Nathan Salomonis, PhD5/19/2021
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