Research

We propose integrating topological data analysis (TDA) with deep learning for satellite image classification. TDA extracts geometric features through persistence homology while addressing CNNs' tendency toward texture-based local features. The method achieves 99.33% accuracy on the EuroSAT dataset, exceeding prior results from larger models like ResNet50 and XL Vision Transformers. It also demonstrates 1.82% improvement over ResNet18 baseline on RESISC45. This represents the first application of TDA features in satellite scene classification combined with deep learning.

We tackle air pollution monitoring by predicting air-quality index values in 1 km² neighborhoods using the AirDelhi dataset. Employing spatio-temporal graph neural networks, we achieve a 79% reduction in error compared to previous approaches, with performance that generalizes to previously unseen locations. The work also identifies new patterns in AQI behavior, including repetitive short-term fluctuations and evolving spatial relationships in pollution distribution.

We present a framework for enhancing multi-agent cooperation through normative modules. These modules enable agents powered by large language models to recognize and adapt to the normative infrastructure of a given environment. The work addresses equilibrium selection in cooperation by drawing on game theory concepts like correlated equilibrium. Agents learn which institutions are considered authoritative through peer interactions, allowing them to coordinate sanctioning behaviors and achieve more stable cooperative outcomes. The evaluation demonstrates the system's capacity to disregard non-authoritative institutions and identify legitimate ones among alternatives.

We propose a computer vision system for monitoring dietary consumption using stationary cameras positioned to observe food on utensils. Rather than requiring users to photograph their completed meals from above, our method tracks food items on spoon surfaces in real-time. The approach proves particularly effective for mixed foods like soups and stews where ingredients submerged in a stew would otherwise remain invisible. We frame this as a non-invasive, user-friendly, and highly accurate dietary intake monitoring tool that eliminates perspective limitations of conventional meal photography approaches.

This paper presents a polynomial time recognition algorithm for (p,q)-split graphs, developed during a research term in Graph Theory under the supervision of Professor Sophie Spirkl at the University of Waterloo (January 2023 to April 2023).