Ganesh Murugappan

I am a masters student at the College of Computing in the Georgia Institute of Technology, where I study computer science with a specialization in machine learning. I am a graduate teaching assistant for CS 4641/7641: Machine Learning with Mahdi Roozbahani.

This past summer, I was a quantitative trading intern at Susquehanna International Group. In the summer of 2022, I worked as a software engineering intern at Meta, implementing duplicate candidate detection as a part of the Recruiting Machine Learning & Data team. Before that, in the summer of 2021, I was a software development engineering intern at Amazon, working on what is now Alexa Together.

I have a BS in computer science (specializations in artificial intelligence and theory and minor in economics) from Georgia Tech, where I was a research assistant for Thad Starner and a teaching assistant for Prof. Roozbahani.

Email / GitHub / LinkedIn / Resume

Projects

I'm interested in machine learning, especially as it applies to natural language processing and reinforcement learning.

	BetaZero Ganesh Murugappan Work in progress, 2023 Self-play deep reinforcement learning algorithm for tic-tac-toe and Connect Four, augmented with Monte Carlo tree search. Inspired by AlphaZero.
	Maching Thinking, Fast and Slow Ganesh Murugappan 2023 Designed neural network architecture based on Daniel Kahneman’s System 1 and System 2. Resulted in 25% faster inference time with only 0.8% drop in performance.
	BiDeT: Bidirectionally Decoding Translator Ganesh Murugappan, Tejas Lokeshrao CS 4650: Natural Language Processing, 2023 paper / code / slides Novel sequence-to-sequence architecture with left-to-right and right-to-left decoding. Demonstrated improvement on translation task.
	Giggle Gauge Ganesh Murugappan 2023 website / code DistilBERT-based regression model to estimate humor of text input based on dataset from r/Jokes subreddit. Deployed in Flask web app for public use.
	Transfer Learning for Machine Translation Quality Estimation Ganesh Murugappan, Alexander Hobmeier, David Gordon, Cameron Bennett CS 4644: Deep Learning, 2022 paper / code Transfer learning with multilingual-BERT-based estimator for machine translation quality estimation. Achieved 0.39 Pearson coefficient on MLQE dataset.
	CopyCat: Using Sign Language Recognition to Help Deaf Children Acquire Language Skills Dhruva Bansal, Prerna Ravi, Matthew So, Pranay Agrawal, Ishan Chadha, Ganesh Murugappan, Colby Duke ACM Conference on Human Factors in Computing Systems (CHI), 2021 paper / code American Sign Language recognition system using Hidden Markov Models, acheving 90.6% user independent word accuracy.
	TamilNet Ganesh Murugappan 2020 website / code Handwritten Tamil Character Recognition system using a convolutional neural network. Achieved 90% accuracy on the IWFHR Competition test set.