Actively developing a Python library implementing mathematical algorithms from coursework: graph algorithms, stochastic processes (Brownian motion, Markov chains, SDEs/SPDEs), optimization (gradient descent variants, simplex), abstract algebra, and reinforcement learning (value/policy iteration, Monte Carlo methods).

Full-stack IR engine with Scrapy crawler, TF-IDF inverted index with cosine similarity and semantic k-NN search (word2vec, FAISS), Flask query processor with Top-K ranking, NLTK spell correction, and WordNet expansion.

A distributed Monte Carlo RL framework that simulates ocean plastic drift and aggregates results from volunteer clients for large-scale model optimization, earning 3rd place in the Grainger Computing Innovation Prize.

A powerful orchestration agent designed to manage and coordinate multiple AI agents. Provides users with a unified chat experience, allowing seamless interaction with various specialized AI agents through a single window.

Collection of projects exploring natural language processing techniques, including semantic parsing, sentiment analysis, and neural language models. Coursework from AI Language Understanding class.

Designed and implemented a co-author network to analyze the influence of research papers on contemporary and future research, leveraging weighted and unweighted adjacency matrices with Erdős1 data. Explored Graph Theoretic principles, focusing on measuring influence and creating influence measurement algorithms.