Preprint
Introducing SCOUBI, a novel method that unlocks insights from the extrasomatic space of spatial transcriptomics data.
About Me
I currently am a final year Bioinformatics PhD student at Georgia Institute of Technology, working in the Sinha Lab. My work focuses on developing computational methods for spatial transcriptomics and single-cell omics. I build tools to better understand cellular characterization, subcellular transcript localization, and cell-cell communication, among other aspects. I am broadly interested in disentangling stochastic effects from biological signals to better understand what things change and how.
Before Georgia Tech, I completed my B.Tech. in Computer Science and Biosciences at IIITD.
What's New
Publication
Our CellSP paper is now out in Communications Biology, introducing an interpretable framework for identifying transcript localization patterns in subcellular spatial transcriptomics data.
Publications
MANTIS: Analytics toolkit for spatial metabolomics with matching spatial transcriptomics data
bioRxiv · 2026
CELLWHISPER: Inference of Direct Cell-Cell Communication from Spatial Transcriptomics
bioRxiv · 2026
CellSP enables module discovery and visualization for subcellular spatial transcriptomics data
Communications Biology · 2025
Nature Communications · 2025
Intracellular spatial transcriptomic analysis toolkit (InSTAnT)
Nature Communications · 2024
Cell Reports · 2024
Hierarchical clustering of world cuisines
IEEE ICDEW · 2020
Projects
Study of inter-regional relations for various disease symptoms generated by Google search trends
Performed spatiotemporal analysis of Google search trends for various disease symptoms to forecast regional COVID-19 cases using machine learning.
Outbreak analysis of Escherichia coli O157
Created a bioinformatics web interface combining genome assembly, functional annotation, and comparative genomics to enable rapid outbreak analysis of E. coli strains.
Conditional VAE based on the RoBERTa encoder architecture to transform recipes across cuisines
Developed an open-source Python library for performing pairwise lipid force distribution analysis in molecular dynamics simulations.
Other Fun Work
Older builds that were scrappy, hands-on, and a lot of fun to make.
A heads-up navigation cap designed using Raspberry Pi to surface walking directions in the user’s field of view, combining wearable prototyping, route guidance, and interface experimentation.
A ML-based image correction browser extension for color vision deficiency, combining simulation, correction models, and an extension workflow to make on-screen content easier to distinguish.
Georgia Institute of Technology · Atlanta, GA · baggarwal9@gatech.edu
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I am a computational biologist focused on developing robust and interpretable methods for analyzing omics data, with the goal of generating meaningful biological insights. My work spans deep learning, statistical modeling, and software development for both single-cell and multi-omic datasets. My PhD research is specialized in spatial transcriptomics, with a particular emphasis on methods for analyzing single-molecule spatial data. I have experience working independently as well as collaborating within interdisciplinary teams, including internships across diverse research and applied settings.
Education
Georgia Institute of Technology
Aug 2022 — Present
Ph.D. Bioinformatics (Expected May 2027) · M.S. Bioinformatics, 2023 · GPA 3.91 · Atlanta, GA
Indraprastha Institute of Information Technology
Aug 2018 — Jun 2022
B.Tech. Computer Science & Biosciences · GPA 7.91/10 · Delhi, India
Experience
Georgia Institute of Technology — Sinha Lab
Jan 2023 — Present
Researcher · Atlanta, GA
- Developing computational methods for spatial transcriptomics, single-cell omics, and multimodal biological datasets.
- Published 7 papers in journals including Nature Communications, Communications Biology, and Cell Reports.
GlaxoSmithKline
May 2023 — Aug 2023
Computational Biology Intern · Collegeville, PA
- Designed statistical approaches to map genetic perturbations to disease signatures (MegaMap project).
- Infered robust gene-disease relationships and validated them with clinical data.
Indian Institute of Science — ATCG Lab
May 2021 — Dec 2021
Research Intern · Remote, India
- Implemented novel distance metrics and engineered distributed genomic distance framework with Ray, improving indexing time by 90%.
Wikilimo
February 2021 — April 2021
Machine Learning Intern · Remote, India
- Created explainable AI models for wildfire modeling using SMOS-derived products.
Skills
Python
R
PyTorch
Ray
Scikit-Learn
Hugging Face
W&B
Git
JavaScript
C/C++
SQL
Keras
React
Django
GCP
AWS