Research Experience 📚

Publications 📚

1. Portfolio Optimization with Stochastic Return Functions: An Algorithmic Approach

• Research Advisor: Dr. Roy E Welsch
• Status: Ongoing
• Lab: Laboratory for Information & Decision Systems, Massachusetts Institute of Technology (MIT)

2. Revisiting the Equity Premium Puzzle: Time Series Forecasting 1990–2012

• Status: Ongoing
• Independent Research: Department of Computer Science, Harvard University

3. Algorithmic Trading: Comparative Analysis of Quadrant Strategies

• Status: Ongoing
• Independent Research: Department of Computer Science & Statistics, Harvard University

4. Comparative Analysis of Time-Series Regression Techniques

• Independent Research: Department of Computer Science & Statistics, Harvard University
• Status: January 2025 [Under Publication Review]
• Details:
  • Analyzed Ridge, Lasso, Weighted Norm, and Quantile Regression on financial time-series data using MSE, MAE, and R^2.
  • Demonstrated Ridge’s predictive accuracy and Weighted Norm Regression’s robustness to outliers.

5. Enhancing Financial Factor Analysis with IPCA and Procrustes Alignment

• Research Advisor: Dr. Alexander Young – Department of Statistics, Harvard University
• Status: December 2024 [Under Publication Review]
• Details:
  • Applied IPCA to estimate latent factors in high-dimensional financial data, leveraging observable characteristics.
  • Enhanced factor interpretability with Procrustes alignment, reducing cross-validation discrepancies by 70%.

6. Cost Efficient Stock Using Forecasting with Enhanced LightGBM and Optuna

• Achievement: IEEE International Conference MoSICom
• Advisor: Dr. Tamizharasan PS (Ph.D. – CSE, National Institute of Technology, Tiruchirappalli)
• Publication: Click Here
• Optimized LightGBM model using Optuna, achieving a 15.2% annualized return and a 3.24 Sharpe ratio, significantly outperforming benchmark returns.
• Developed cost-awareness strategy to reduce false-positive errors, enhancing prediction reliability and lowering investment costs.

7. Dynamic Beta Variability in Foreign Exchange Returns Using Instrumented PCA

• Achievement: 2nd Place, National Undergraduate Research Competition, 2022 - Peer Reviewed By Abu Dhabi University.
• Advisor: Dr. Tamizharasan PS (Ph.D. – CSE, National Institute of Technology, Tiruchirappalli)
• Publication: Click Here
• Applied IPCA to build a flexible factor model, reducing FX data dimensionality and accommodating time-varying betas for superior out-of-sample predictability.
• Demonstrated economic significance by showing IPCA-based trading strategies outperformed PCA by 8%, especially for the Swiss Franc and Australian Dollar.

8. Deep Learning-Based Smart Parking Management System and Business Model

• Achievement: Published in Springer Conference - CVIP 2020, Singapore
• Advisor: Dr. Tamizharasan PS (Ph.D. – CSE, National Institute of Technology, Tiruchirappalli)
• Publication: Click Here
• Architected the workflow of ensemble techniques for detecting and classifying parking occupancy with 95% precision.
• Used TensorFlow for training and evaluation, improving F1 score, recall, and precision metrics.

9. Credit Risk Assessment Model for UAE’s Commercial Banks: A Machine Learning Approach

• Achievement: 2nd Place in Undergraduate Research Competition, 2021 - Peer Reviewed By Abu Dhabi University.
• Advisor: Dr. Parizad Dungore (Dubai Business School, University of Dubai)
• Publication: Click Here
• Developed a ML based credit risk model using Linear Discriminant Analysis and Adaboost, achieving 95.2% accuracy.
• Implemented and tested models like Logistic Regression and Decision Trees on 7M+ records, identifying key risk factors through feature selection.

10. Lithium-Ion Battery Life Prediction Based on Initial Stage-Cycles Using Machine Learning

• Achievement: Granted Intellectual Property Rights
• Advisor: Dr. Vilas Haridas Gaidhane (PhD - Delhi University)
• Publication: Click Here
• Developed a Gradient Boosting Trees model to predict lithium-ion battery life using initial 50-cycle charge/discharge data.
• Applied Kernel PCA to project data into higher-dimensional space, enhancing model robustness and prediction accuracy.

11. Real-Time Drowsiness Detection Framework Using Computer Vision to Prevent Car & Road Accidents

• Achievement: Granted Intellectual Property Right
• Advisor: Dr. Raja Muthalagu
• Publication: Click Here
• Developed and implemented a real-time drowsiness detection system using OpenCV’s Haar Cascade Classifier, achieving an accurate detection rate of drowsiness and distraction.
• Utilized Raspberry Pi 4+ and NoIR-V2 Pi camera for hardware implementation, ensuring efficient real-time processing and low energy consumption.

For further inquiries or collaborations, feel free to reach out to me at adityasaxena@g.harvard.edu ✉️