Selected research and technical projects spanning machine learning, computer vision, and decision systems.
Evidence-grounded multimodal prognosis with local inference and systematic model-failure analysis.
Bias Detection through Unsupervised Learning 30 Nov 2024 – Current Executive Summary This innovative research project addresses a critical challenge in today’s digital information ecosystem: detecting and quantifying political bias in news articles and social media content without requiring labeled training data. The framework introduces a novel fully unsupervised pipeline that combines geometric embedding-based techniques with a multi-agent evaluation system to provide a comprehensive, nuanced assessment of media bias across two dimensions - political leaning and rhetorical bias intensity.
Enhancing A2C with DAPD across Classic Control and Atari Environments 1 Oct 2024 – 10 Dec 2024 Project Overview This project investigates parameter-efficient reinforcement learning through the integration of Dynamic Sparse Training with Pathways (DAPD) into the Advantage Actor-Critic (A2C) algorithm. We compare A2C+DAPD against standard dense A2C and Proximal Policy Optimization (PPO) across three benchmark environments—LunarLander-v2, Breakout, and Pong—to evaluate trade-offs between computational efficiency and learning reliability.
Medial Axis Transform 1 Nov 2024 – 15 Dec 2024 Project Overview The Medial Axis Transform project is a final project for COMP 558 (Computer Vision) at McGill University. The main goal is to replicate and build upon the findings of the paper “Shape-Based Measures Improve Scene Categorization” by exploring additional geometric properties in images.
Quick Draw 1 Oct 2023 – 25 Nov 2023 Project Overview The Quick Draw project is a collaborative initiative developed during the MAIS202 Bootcamp. The project focuses on implementing a machine learning model capable of predicting animal labels based on user-uploaded images. Leveraging the Google Quick Draw dataset, the application accurately classifies images into 10 distinct animal categories.
