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.
