Experience

Democratizing institutional-grade market intelligence for retail investors

• Built a financial intelligence SaaS platform monitoring 20+ volatility sources 24/7, analyzing 5,000+ stocks with real-time alerts in under 1 minute, giving retail traders hedge-fund-speed market awareness.
• Pioneered Natural Language Orders (NLO): users set buy/sell triggers using plain English, with automatic execution when conditions match across 27+ major brokerages including Robinhood, Fidelity, and Charles Schwab.
• Secured backing from Gener8tor's gBETA program; scaled to 200+ active users with $250K+ invested via platform insights, a 4.8-star rating across 200 reviews, priced at $9.99/month.
• Led technical architecture integrating SEC filings, Reddit discussions, financial news (CNBC, NY Times), and breaking events, with AI-powered event monitoring and bank-level encryption for brokerage integration.

Space research at one of the world's leading scientific organizations

• Conducting research at NASA Goddard Space Flight Center on cutting-edge space science and technology.

AI Copilot for First Response Medicine at Purdue University

• Built YOLOv11 computer vision models for medical tools with 96% mAP@[0.5:0.95]; applied quantized models and focal loss to handle critical class imbalances in medical data.
• Slashed data annotation needs by 80% with an active learning pipeline using uncertainty sampling (PyTorch + Ray for distributed processing).
• Crafted a mixed reality system for HoloLens that visualizes ML inferences in real time; contributed statistical analysis to peer-reviewed research papers.

Technical leadership for a social-impact organization

• Led technical development and architecture for social impact initiatives, guiding teams through product decisions and engineering challenges.

Diving deep into data science and machine learning algorithms

• Engineered clustering algorithms (K-means, DBSCAN, GMMs) for customer segmentation, achieving a 0.78 silhouette score.
• Implemented DQN (reinforcement learning), gradient boosting, random forests, and CNN/LSTM models for time-series prediction.
• Developed a stacked ensemble framework that significantly improved medical data prediction accuracy.