ML and Quant Research Projects

Apr 2026

Reasoning Model Failure Analysis, LLM Interpretability →

A controlled LLM evaluation pipeline spanning six reasoning models from 7B to 70B parameters, designed to disentangle reasoning length effects from forced re-entry interventions. The study measured a 36-point accuracy decline in Llama-distilled models while Qwen-distilled models remained robust. Multi-GPU inference was conducted with a bfloat16 KV cache on 4x GH200 GPUs.

KSE 2024, Published

Adversarial Robustness via Entropy Based Feature Selection in RL →

An entropy-based feature selection framework for reinforcement learning agents that achieved 94 and 95 percent accuracy on Lunar Lander and Bipedal Walker under adversarial perturbations, outperforming KL Divergence and Joint Entropy baselines across Gym environments.

CISS 2025, Published

Mouse Brain Cell Segmentation in Fluorescence Microscopy →

A deep learning segmentation pipeline for high-noise fluorescence microscopy images, comprising a CNN architecture and a custom preprocessing routine for automated cell boundary detection.

CISS 2025, Published

Virtual Yoga Instructor with Real Time Feedback →

A real-time pose estimation and corrective feedback system using normalized joint angle features and repetition counting. The system achieves sub-100 ms latency on standard hardware and remains robust to variations in body size and camera angle.

Jan 2026

Options Pricing Engine and Greeks Computation →

Black-Scholes closed-form and Monte Carlo pricers with 100K antithetic paths for European equity options. Delta, Gamma, and Vega are computed both analytically and via finite differences across strike and maturity grids.

Dec 2025

Statistical Pairs Trading Backtest →

An Engle-Granger market-neutral strategy applied to AAPL/MSFT, KO/PEP, and XOM/CVX over daily data from 2015 to 2023. The backtest incorporates walk-forward cointegration screening, out-of-sample hedge ratios, and a 1 bp transaction cost assumption. Sharpe ratio, maximum drawdown, turnover, and signal decay are reported.

May 2025

GARCH Volatility Modeling and Stochastic Time Series →

A GARCH(1,1) implementation applied to SPY and five single-name equities, validated using AIC, BIC, and Ljung-Box diagnostics. GARCH, LSTM, and rolling volatility baselines were benchmarked across the 2020 and 2022 stress periods through walk-forward error analysis.

Dec 2023

LSTM Based Financial Time Series Forecasting →

A stacked LSTM trained on over ten years of daily equity price and volume data, evaluated out-of-sample on the 2022 to 2023 period against ARIMA, GARCH, and random walk baselines using walk-forward error decomposition.