Medical Imaging AI
- Multimodal AI Fusion Framework – First-author framework integrating CT imaging and clinical data to predict 10-year major adverse cardiac events (MACE) via XGBoost fusion, achieving ROC up to XXX and PRC up to XXX.
- Deep Learning Pipeline Beyond CAC – Developed image-based pipeline that identifies MACE beyond traditional calcium and risk scores, achieving diagnostic accuracy up to XXX% using CT-derived imaging features.
- Sequential Feature Learning with Weighted XGBoost – Designed a model using XGBoost with weighted loss, reporting ROC of 88% on a cohort of 27K+ patients.
- Active Learning Strategy for Label Efficiency – Created an active learning approach reducing labeling burden by 75%, requiring only 15.4% of data for binary and 23.1% for multi-class classification to match full-data performance.
- Human-in-the-Loop Eye-Tracking Model – Co-authored model leveraging radiologists’ eye-tracking to guide AI focus on pulmonary regions, improving interpretability and alignment with clinical attention.
- Clinical Collaboration & Validation – Partnered with Emory doctors (De Cecco, van Assen, Ardeshir-Larijani, Quyyumi, Krupinski) to ensure robustness and translational impact across all AI models.
Integrated AI-Based Discovery & Innovation
- Ensemble Portfolio Optimization – Pioneered LSTM + Deep Q-Learning ensemble, outperforming equal-weighted portfolios by 200%, SPX by 100%, and QQQ by 50%.
- Rebalancing via Reinforcement Learning – Replicated and advanced institutional portfolio rebalancing papers, achieving fast convergence on 512 assets using Linux, PyTorch, and GPU acceleration.
- 100-Dimensional Ellipsoid Sampling – Conducted research on 100-dim sampling with Ellipsoid method under 20 minutes and analyzed stock universe volatility across look-back periods.
- Implied Volatility Curve Modeling – Created robust curves for derivatives, driving profitable trading strategies.
- Generative AI for Trading Decisions – Developed and optimized LLM-based models for trading reasoning.