Hello. I am Aditya! I build computational methods that turn complex biological data into mechanistic insights. My work focuses on integrating multimodal spatial measurements—sequencing, imaging, and proteomics to understand how cells organize within tissues and how that organization shapes health and disease.
I am currently a Postdoctoral Associate at Duke University’s Discovery AI and the Department of Cell Biology. in the Department of Cell Biology, working with Rohit Singh and Purushothama Rao Tata. Previously, I was a Sr. Data Scientist at Akoya Biosciences Inc. and at the Broad Institute of MIT and Harvard.
Research Interests
- Spatial Biology: Multimodal integration of spatial transcriptomics, proteomics, and metabolomic
- Foundation Models: Adapting pretrained models for out-of-distribution biological data
- Regulatory Networks: Benchmarking and inference of gene regulatory networks from single-cell data
- Combinatorial Optimization: Optimal experimental design strategies
Education
Ph.D. in Computer Science ∙ Virginia Tech ∙ 2015–2020
M.S. in Computer Science ∙ Virginia Tech ∙ 2015–2017
M.S. in Computational Science ∙ IIT Madras ∙ 2013–2015
B.Tech. in Electrical & Electronics Engineering ∙ VIT Vellore ∙ 2008–2012
Selected Publications
Pratapa A, Singh R, Tata PR. (2025) Transforming Biological Foundation Model Representations for Out-of-Distribution Data. RECOMB’26 .
Pratapa A, Mansouri S, Nikulina N, Matuck B, et al. (2025) SAME: Topology-flexible transforms enable robust integration of multimodal spatial omics. bioRxiv .
Pratapa A, Jalihal AP, Law JN, Bharadwaj A, Murali T. (2020) Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nature Methods 17:147–154.
Pratapa A, Doron M, Caicedo JC. (2021) Image-based cell phenotyping with deep learning. Current Opinion in Chemical Biology 65:9–17.