Hi, I'm Murali 👋

Spotify Wrapped for your friends' shopping, built at HackMIT 2026 with a four-person team; I co-built the Fastify + TypeScript backend. Taste match is computed from real purchases, Claude picks gifts that cite items the recipient saved, and Visa Direct splits the payment. Shortlisted among 25 of 200 teams in the first round of the Meta and Visa challenges.

Implemented floor vs. non-floor pixel/region classification on indoor images using the CMM dataset and Support Vector Machines. Compared three feature-engineering approaches: RGB-only pixels, RGB plus spatial coordinates, and KMeans-based region-level features. Method 3 (KMeans regions) achieved ~92.5% test accuracy with the fastest training.

Physics-informed forecasting for power grids. Stage 1, KAT-PatchTST, is my first-author paper published at IEEE APPEEC 2026: PatchTST with a Kirchhoff conservation regularizer reaches 3.55% demand MAPE across six US balancing authorities at ~0.6M parameters, 3-10x leaner than comparable models. Stages 2 and 3 (grid bottleneck analysis and a grid-partition policy) are in development, targeting ICLR.

Implemented a distributed system using MPI (Message Passing Interface) in C++ to parallelize log file parsing and anomaly detection across multiple nodes, reducing analysis time by 70% for large-scale server logs. Integrated parallel reduction techniques for aggregating metrics like error rates and response times, enabling real-time monitoring and scalable debugging in cloud environments.

Developed a Monte Carlo simulation to estimate stock portfolio values, modeling returns with Cholesky decomposition. Simulated 100 portfolio projections over 100 days to assess risk and future performance.

Developed a neural option pricing system integrating deep sequence architectures with GARCH volatility, benchmarked against Black-Scholes, enabling quantitative comparison between ML based and Analytical pricing. Built an Interactive Dashboard with heatmaps, sensitivity analysis (price vs spot, volatility) and Multiple Models Trains with different features to visualize pricing behavior and model errors across market conditions.