Welcome to the laboratory of Saurabh Sinha at Georgia Tech.
Our research lies in the interdisciplinary field of bioinformatics, which provides the computational foundation for modern molecular biology as it rapidly develops into a quantitative science. Our current research centers on spatial omics, spatial multi-omics, and single-cell analysis, while also investigating the regulatory mechanisms underlying molecular and cellular behavior. Our goal is to understand how molecular states, cellular interactions, and tissue organization vary across biological conditions.
Our research spans multiple spatial scales, from the organization of RNA molecules within individual cells, to communication between neighboring cells, to coordinated transcriptomic and metabolic patterns across tissues. We develop rigorous and interpretable methods for comparative spatial analysis, subcellular RNA organization, cell–cell communication, spatial multi-omics integration, and the computational integration of microscopy with molecular measurements.
Our work draws on machine learning, causal inference, probabilistic modeling, spatial statistics, and optimization. Students in the lab work at the interface of bioinformatics, data science, and quantitative biology, developing new computational frameworks and applying them to real biological problems in close collaboration with experimental researchers.
We welcome students who are excited to formulate fundamental computational questions, develop broadly useful methods and open-source tools, and use emerging single-cell and spatial technologies to investigate biological systems in health and disease.
Principal Investigator: Saurabh Sinha.
We are looking for new graduate students (Fall 2026 and Fall 2027) and post-docs to join the lab at Georgia Tech. Please send me an email if you are interested. saurabh.sinha@bme.gatech.edu
FEATURED RESEARCH:
Spatial Transcriptomics: subcellular colocalization of genes
Anurendra Kumar, Alex W Schrader, Bhavay Aggarwal, Ali Ebrahimpour Boroojeny, Marisa Asadian, JuYeon Lee, You Jin Song, Sihai Dave Zhao, Hee-Sun Han, Saurabh Sinha. Intracellular spatial transcriptomic analysis toolkit (InSTAnT). Nature Communiucations 2024. [Free full text]
Comparative Spatial Transcriptomics + Machine Learning
Yeojin Kim, Abhishek Ojha, Alex Schrader, Juyeon Lee, Zijun Wu, Ian M Traniello, Gene E Robinson, Hee Sun Han, Sihai D Zhao, Saurabh Sinha. SpaceExpress: a method for comparative spatial transcriptomics based on intrinsic coordinate systems of tissues. [biorxiv preprint]
Causal Inference + Machine Learning + Gene Networks
P. Dibaeinia, S. Sinha. CIMLA: Interpretable AI for inference of differential causal networks. arXiv:2304.12523 [Free full text] (Published in Science Advances '25.)
Machine Learning + Optimization + Protein Design
S. Ghaffari, E. Saleh, A. G. Schwing, Y. Wang, M. D. Burke, S. Sinha (2024). Robust Model-Based Optimization for Challenging Fitness Landscapes. ICLR '24. [Arxiv preprint] (Published in Proceedings of ICLR '24.)
Spatial Transcriptomics: subcellular gene modules
Bhavay Aggarwal, Saurabh Sinha. CellSP: Module discovery and visualization for subcellular spatial transcriptomics data. [biorxiv Preprint]
Bayesian deconvolution of RNA-seq data
S Ghaffari, K J Bouchonville, E Saleh, R E Schmidt, S M Offer, S Sinha (2023). BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures. Genome Biology 24(1). [Free full text]
Multi-omics integration identifies regulators of colorectal cancer invasiveness
S. Ghaffari, C. Hanson, R.E. Schmidt, K.J. Bouchonville, S.M. Offer, S. Sinha (2021). An integrated multi-omics approach to identify regulatory mechanisms in cancer metastatic processes. Genome Biology 22(19). [Free full text]
Perspective on Gene Regulatory Networks in Behavior
S. Sinha, B.M. Jones, I.M. Traniello, ... G.E. Robinson (2020). Behavior-related gene regulatory networks: A new level of organization in the brain. PNAS , 201921625. [Free full text]
A Cloud-based knowledge engine for genomics
C. Blatti, A. Emad, M.J. Berry, ... C.B. Bushell, S. Sinha (2020). Knowledge-guided analysis of ‘omics’ data using the KnowEnG cloud platform. PLoS Biology 18(1): e3000583. [Free full text]