Faculty Member and Assistant Professor, Pathology
School of Medicine

Faculty Member and Professor, Biomedical Engineering
The Henry Samueli School of Engineering

Faculty Member and Professor, Electrical Engineering and Computer Science
The Henry Samueli School of Engineering

Computer Science, Biomedical Engineering, Generative AI in Healthcare, Computational Pathology, Cancer Diagnostics, Infectious Diseases, Regulatory Science, Deep Learning, Real-Time Clinical Decision Making, Companion Diagnostic Assays, Computer Vision

https://faculty.sites.uci.edu/pratikshahlab

https://www.pratikfacultylab.org/join-us/

https://www.pratiks.info/

https://www.linkedin.com/in/pratikshahUCI

Dr. Shah's research program focuses on hypothesis-driven deep learning, generative AI, biomedical engineering, and clinical research to develop technologies for diagnosing and treating cancer, infectious diseases, and neurological disorders. The research group combines and invents novel deep learning, biological, and statistical methods to tackle challenges in personalized medicine. Dr. Shah's work includes advancements in generating medical images for unbiased, patient-centered care, cancer diagnostics at the cellular level using companion diagnostic assays, and evaluating the impact of clinical decision-making on infectious diseases and antimicrobial therapy — all within a unified theoretical and methodological framework. Dr. Shah and his team are developing a dynamic, living-systems framework for computational medicine, transitioning from static models to real-time, systems-wide perspectives on molecular and clinical processes. Their goal is to invent and deploy equitable medical technologies that improve patient outcomes and public health.

Dr. Pratik Shah is a professor at the University of California and the principal investigator and director of a computational medicine research program. He holds tenure-track faculty appointments in the academic senate lines of Pathology & Laboratory Medicine, Electrical Engineering & Computer Science, and Biomedical Engineering. Recent work from his lab has been published in Cell Press, Nature Digital Medicine, Journal of American Medical Association, leading machine learning conferences, and workshop proceedings of The National Academies of Science Engineering and Medicine. Previously, Dr. Shah was a Principal Investigator at the Massachusetts Institute of Technology (MIT), where he led a computational medicine program at the MIT Media Lab. He also served as the principal investigator for a memorandum of understanding with the U.S. FDA, aimed at establishing AI and machine learning research ecosystems for clinical development. Dr. Shah holds a BS, MS, and PhD in biological and data sciences and completed fellowship training at Massachusetts General Hospital, the Broad Institute of MIT and Harvard, and Harvard Medical School.

1. Deep learning with uncertainty quantification for predicting the segmentation dice coefficient of prostate cancer biopsy images. 23^rd IEEE International Conference on Machine Learning and Applications (ICMLA). 2024 December 18-20. DOI: 10.1109/ICMLA61862.2024.00178. Xie A, El Fatimi E, Ghosal S, Shah P*. [https://ieeexplore.ieee.org/document/10903494]*=Senior author supervising research

2. Uncertainty quantified deep learning and regression analysis framework for image segmentation of skin cancer lesions. 23^rd IEEE International Conference on Machine Learning and Applications (ICMLA). 2024 December 18-20. DOI: 10.1109/ICMLA61862.2024.00080. El Fatimi E, Shah P*. [https://ieeexplore.ieee.org/document/10903456]*=Senior author supervising research

3. A deep-learning toolkit for visualization and interpretation of segmented medical images. Cell Reports Methods. 2021 Nov 22; 1(7):100107. Ghosal S, Shah P*. [https://www.cell.com/cell-reports-methods/fulltext/S2667-2375(21)00166-1]*=Senior author supervising research

4. Use of deep learning to develop and analyze computational hematoxylin and eosin staining of prostate core biopsy images for tumor diagnosis. JAMA Network. 2020 May 01; 3(5):e205111. Rana A, Lowe A, Lithgow M, Horback K, Janovitz T, Da Silva A, Tsai H, Shanmugam V, Bayat A, Shah P*. [https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2766071]*=Senior author supervising research

5. Artificial intelligence and machine learning in clinical development: a translational perspective. Nature Digital Medicine . 2019; 2:69. Shah P*, Kendall F, Khozin S, Goosen R, Hu J, Laramie J, Ringel M, Schork N. [https://www.nature.com/articles/s41746-019-0148-3]*=Senior author supervising research

6. Artificial intelligence for clinical trial design. Cell Trends Pharmacol Sci. 2019 Aug; 40(8):577-591. Harrer S, Shah P, Antony B, Hu J. [https://www.cell.com/trends/pharmacological-sciences/fulltext/S0165-6147(19)30130-0]*=Senior author supervising research

7. Reinforcement learning with action-derived rewards for chemotherapy and clinical trial dosing regimen selection. Proceedings of Machine Learning Research 2018; 85:161–226. Gregory Yauney, Pratik Shah*. [https://proceedings.mlr.press/v85/yauney18a.html]*=Senior author supervising research

8. Technology-enabled examinations of cardiac rhythm, optic nerve, oral health, tympanic membrane, gait and coordination evaluated jointly with routine health screenings. The British Medical Journal. 2018 Apr 20; 8(4):e018774. Shah P*, Yauney G, Gupta O, Patalano Ii V, Mohit M, Merchant R, Subramanian SV. [https://bmjopen.bmj.com/content/8/4/e018774.long]*=Senior author supervising research

Virtual Digital Histopathology with Explainable Deep Learning for Prostate Diagnosis. NIH/NCI R21 Sep 1, 2024 - Aug 31, 2026. Role: Principal Investigator

Biomedical Engineering

Cellular and Molecular Biosciences

Electrical Engineering and Computer Science

Link to this profile
https://faculty.uci.edu/profile/?facultyId=7154

Last updated
05/23/2025