Surya Prasath, PhD

Assistant Professor, UC Department of Pediatrics

surya.prasath@cchmc.org

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

Selected

Machine Learning-Based Prediction of Pediatric Ulcerative Colitis Treatment Response Using Diagnostic Histopathology. Liu, X; Prasath, S; Siddiqui, I; Walters, TD; Denson, LA; Hyams, JS; Kugathasan, S; Griffiths, AM; Collins, MH; Baldassano, RN; Sudel, B; Wali, P; Ziring, D; Dhaliwal, J. Gastroenterology. 2024; 166:921-924.e4.

Selected

Artificial intelligence image-based prediction models in IBD exhibit high risk of bias: A systematic review. Liu, X; Reigle, J; Prasath, VB S; Dhaliwal, J. Computers in Biology and Medicine. 2024; 171:108093.

Selected

LungMAP Portal Ecosystem: Systems-level Exploration of the Lung. Gaddis, N; Fortriede, J; Guo, M; Bardes, EE; Kouril, M; Tabar, S; Burns, K; Ardini-Poleske, ME; Loos, S; Schnell, D; Sabater-Lleal, M; Lowenstein, CJ; Smith, NL; Morrison, AC. American Journal of Respiratory Cell and Molecular Biology. 2024; 70:129-139.

Selected

MGS2AMR: a gene-centric mining of metagenomic sequencing data for pathogens and their antimicrobial resistance profile. Van Camp, PJ; Prasath, VB S; Haslam, DB; Porollo, A. Microbiome. 2023; 11:223.

Selected

maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks. Cazares, TA; Rizvi, FW; Iyer, B; Chen, X; Kotliar, M; Bejjani, AT; Wayman, JA; Donmez, O; Wronowski, B; Parameswaran, S; Kottyan, LC; Barski, A; Weirauch, MT; Surya Prasath, VB; Miraldi, ER. PLoS Computational Biology. 2023; 19:e1010863.

Selected

Artificial intelligence in bronchopulmonary dysplasia- current research and unexplored frontiers. Shah, M; Jain, D; Prasath, S; Dufendach, K. Pediatric Research. 2023; 93:287-290.

Selected

CellDrift: inferring perturbation responses in temporally sampled single-cell data. Jin, K; Schnell, D; Li, G; Salomonis, N; Prasath, VB S; Szczesniak, R; Aronow, BJ. Briefings in Bioinformatics. 2022; 23:bbac324.

Selected

DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity. Li, G; Iyer, B; Prasath, VB S; Ni, Y; Salomonis, N. Briefings in Bioinformatics. 2021; 22:bbab160.

Selected