BTech 3rd year, building at the intersection of machine learning, quantitative finance, and alternative data research.
Focused on systematic alpha generation, event-driven strategies, and applying computer vision + NLP to financial signals. I work primarily in Python and enjoy turning messy, unconventional datasets into structured research frameworks.
Open to collaborating on quant research, ML for finance, and anything involving satellite imagery or SEC filings.
Oceanic-Edge Quantitative research framework generating stock alpha by mapping maritime AIS telemetry and satellite computer vision to global supply chain congestion signals.
SPLM End-to-end quantitative research framework generating stock alpha by detecting vehicle occupancy in satellite imagery using YOLOv8.
LedgerLens Python pipeline for Benford's Law analysis on SEC EDGAR 10-K filings — chi-square and MAD-based anomaly detection, suspicion scoring, heatmap visualizations, and ReportLab PDF audit reports.
macro-event-study-framework Cross-asset event study framework tracking CPI, NFP, PMI, and FOMC market reactions across equities, FX, rates, and volatility built with FRED API and yfinance.
