Hi, I'm Murali 👋

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.

Conducted large-scale electricity consumption and generation mining using XGBoost, TFT, and deep learning models, achieving improved demand forecasting through advanced feature engineering. Modeled the U.S. power grid as a weighted directed graph and applied max-flow min-cut analysis to identify critical transmission bottlenecks and simulate cascading failure scenarios for Efficient Resource Allocation.

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 LSTM models 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.

Built an AI-powered Flask app that generates audio descriptions from images using Salesforce's BLIP image captioning model. Converts uploaded images to speech using a text-to-speech engine, making it a potential assistive tool for visually impaired users.