Physics brain. Product instincts. I find the story in the data, then figure out what to do about it.
I graduated with an Integrated MSc in Physics from BITS Pilani Hyderabad and completed my Master's thesis at IIT Bombay, where I built a real-time Gamma Ray Burst detection pipeline for AstroSat satellite data.
Along the way, I realised the thing I love most is turning complex, noisy signal into clear insight. Whether that signal is coming from a neutron star or a business dashboard, the problem-solving muscle is the same. That's what brought me to data and product.
Master's thesis at IIT Bombay. Designed a production-grade detection system for Gamma Ray Bursts using live satellite data from CZTI aboard AstroSat. Engineered four independent detection algorithms (N-Sigma, Top-N, CuSum, Sum-Threshold) — each addressing different statistical regimes. The system handles signal noise, false-positive suppression, and real-time throughput constraints, directly mirroring how AI systems balance precision vs. recall in high-stakes pipelines.
Full-stack analytics pipeline — raw CSV to production dashboard. Wrote complex SQL (CTEs, window functions, aggregations) against SQLite, processed in Python, and visualised in Tableau with KPI tiles, content growth timeline, country distribution, and a genre treemap. Built for business decision-making, not just aesthetics.
Built an end-to-end agentic workflow using n8n that automates the job search pipeline — scraping listings, matching against a target role profile, drafting personalised outreach messages, and tracking application status. Demonstrates applied understanding of LLM orchestration, prompt chaining, and multi-step agentic logic in a real-world product context.
End-to-end product case study on Spotify's podcast discovery problem. Defined user personas (Casual Commuter, Deep Diver, Lapsed Listener), mapped friction points, prioritised a feature roadmap, and designed two solutions: a Smart Discovery Feed and a Podcast Taste Graph. Includes success metrics, pitfall mitigation, and business impact framing.
Demonstrated galaxy classification at 94% accuracy using a hybrid quantum-classical model on NASA imagery. Divided images into 16×16 pixel patches, encoded via a Parameterized Quantum Circuit (PQC) chosen for high expressibility and entanglement. Trained with Cross-Entropy loss and L-BFGS optimisation, combining PyTorch and Qiskit Machine Learning.
Krittika Astronomy Club project using MCMC with a broken power law model to fit neutron star afterglow light curves. Applied Bayesian parameter estimation to constrain physical decay properties post-merger. Implemented entirely from scratch in Python.
news, but make it readable.
AI-powered news app with 9 personality-driven channels — from "Trump Did What Now" to "Big Bang Bulletin". Click a channel, Groq summarises the latest headlines in that channel's Gen Z voice. Built with React, Vite, GNews API and Groq.
Wordle, but make it music.
Daily song-guessing game where you identify a track from increasingly obvious clues. Built in ~3.5 hours as a portfolio project. Uses React, Vite, Tailwind, Vercel serverless functions and Upstash Redis for daily state.
Physics taught me to start with priors, update on evidence, and never mistake noise for signal. That's exactly how I approach product decisions, business problems, and analytical work. The methods scale.