About Me

I am a Ph.D. candidate advised by Dr. Bharat Bhargava at the Department of Computer Science at Purdue University. My current research focuses on developing reinforcement learning techniques to build agents capable of detecting and adapting to novel situations(unseen during training) in multi-agent environments.

Previously, I earned my Masters Degree in Computer Science from Johns Hopkins University where I worked on Causal Inference with Dr. Ilya Shpitser affiliated with the Malone Center for Engineering in Healthcare.

Before that, I worked at Cummins Inc. and Tata Consultancy Services Ltd. as a Software Engineer for 5 years. I received my Bachelors in Technology in Instrumentation and Control Engineering from Bharati Vidyapeth College of Engineering, New Delhi, India.

Research

My main areas of interest are Reinforcement Learning, Machine Learning, Natural Language Processing, and Causal Inference. Broadly, I'm interested in Artificial Intelligence, Cognitive Science, and Philosophy of Science.

Publications

Bonjour, Trevor, et al. "Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach." accepted in IEEE Transactions on Emerging Topics in Computational Intelligence (2022)

Bonjour, Trevor, Aggarwal, Vaneet, and Bharat Bhargava “Information Theoretic Approach to Detect Collusion in Multi-Agent Games”, accepted in AAAI Symposium (2022)

Projects

asl-a: American Sign Language Assistant

A software package to convert ASL gestures to text/audio. The project aims to create a product for people with speech impairments to communicate with people who may not know ASL. We have implemented gesture recognition and conversion for the 26 alphabets for the English language. Currently, we are working on adding simple phrases.

Forum Topic Extraction

Performed topic modeling for online clickstream data using Automatic Differentiation Variational Inference to bundle forum discussions by topics for online learning platforms.

MLaaS: Machine Learning as a Service for exploring large datasets

Used deep neural networks to learn on BPTI protein trajectories for multiple users leveraging commonalities.

Classification of Piano music by Composers

Used various machine learning techniques like SVM, Naïve Bayes, k-fold Cross-Validation to classify and analyze the results. Found some interesting results, especially using PCA to show similarities in composers.

Teaching

Teaching Assistant, Purdue University

CS 448: Introduction to Database Systems - Spring 2023
CS 177: Programming With Multimedia Objects - Fall 2022
CS 182: Computer Science Fundamentals - Spring 2020
CS 180: Introduction to Java - Fall 2019
CS 373: Data Mining and Machine Learning - Fall 2017 and Spring 2018

Teaching Assistant, Johns Hopkins University

EN 676: Data to Models: Machine Learning - Spring 2017
EN 226: Data Structures - Fall 2016 and Summer 2016