🧬 Statistical Genomics | 🔬 Computational Biology | 🎓 PhD Applicant

I work at the intersection of statistics, machine learning, and molecular biology — focused on regulatory network inference, and the identifiability and robustness of computational biology pipelines. My background spans wet-lab molecular biology and structural biology through to dry-lab transcriptomics and pan-cancer genomics analysis.

I hold a dual degree (B.Tech/M.Tech) in Biotechnology and Biochemical Engineering from IIT Kharagpur, and I'm currently applying to PhD programs working at this statistics–genomics intersection.

• First-author publication, Journal of Translational Medicine (2023) — master regulator analysis across pan-cancer TCGA data
• Research experience at ISGlobal Barcelona, St. Jude Children's Research Hospital (with Dr. Raghvendra Mall), the University of Alberta (Newby Lab), and IIT Kharagpur (Master's thesis, Prof. Amit Das)
• Core interests: regulatory network inference, transcriptomics, statistical identifiability of computational pipelines, pipeline robustness under model misspecification

• Languages: Python, R, C, Bash
• Comp Bio / Genomics: Bioconductor, Galaxy, BLAST, TCGA pipelines, regulatory network inference
• ML / Stats: scikit-learn, TensorFlow, statistical modeling, identifiability analysis
• Structural Biology: MD simulations, UCSF ChimeraX
• Wet Lab: PCR, Western/Southern blot, ELISA, mammalian & microbial cell culture, plasmid work
• Tools: Git, Linux

• Google Scholar
• ORCID
• LinkedIn

Open to research collaborations and conversations on statistical genomics, regulatory networks, and computational biology pipelines.