I鈥檝e always been torn between the elegant certainty of mathematics and the beautiful complexity of biology, so I decided to master both. My obsession with healthcare data science began at IIT Delhi, where I spent my Master鈥檚 thesis translating the abstract constructs of evolution into mathematical models. That research spark eventually took me to Harvard University, building computational drug discovery frameworks for DARPA-funded projects, followed by a summer at Vertex Pharmaceuticals while finishing Data Science Degree at Columbia University. Today, I鈥檓 a Senior Data Scientist on the Experimental AI team at AbbVie, where I use complex statistical theory and AI to build enterprise-ready solutions that aid decision-making for clinical leadership. Whether it鈥檚 decoding disease at the discovery stage or building audit-ready AI solutions, I live for the moments where rigorous algorithms meet clinical innovation.
Experience across Drug Lifecycle
Industry Experience
AbbVie Inc (2023 - Present)
Site Anomaly & Fraud Detection
Engineered GLMM-based unsupervised models to identify atypical site behavior and fraud. Deployed portfolio-wide across global trials to ensure high-fidelity data oversight.
Professional Patient Detection
Developed novel algorithms to identify duplicate or "professional" trial participants, safeguarding data integrity across international clinical sites.
Medical Monitoring
Engineered ensemble models for unsupervised patient monitoring, reducing clinical review and medical escalation times by 90%.
Wyss Institute, Harvard University (2019 - 2022)
AI Drug Discovery
Built AI-driven drug discovery framework using Markov Random Fields and multiomics data to model protein鈥搕arget interactions for DARPA-funded therapeutic projects.
Portfolio Projects
Something fun I've been working on
KDrama Sense
Agentic taste and theme-aware K-Drama assistant built by combining a custom enrichment pipeline and a RAG-based personalised assessment engine. Extracted and synthesised structured metadata, specialist reviews, and community discussions from TMDB, MyDramaList, and Reddit r/KDRAMA into 1,500 enriched drama profiles with LLM-derived thematic labels.
Trial Guard
Real-time adverse event prediction system for enrolled clinical trial patients using a temporal transformer trained on 13,000+ ALS patient trajectories from the PRO-ACT database. Implements timestamp-based positional encoding and an agentic pipeline integrating SHAP-based feature attribution and CTCAE v5.0 grading criteria to generate grounded clinical alerts at each patient visit.
