Graduate Teaching Assistant - Fall 2022
Teaching Assistant for Math 203 Principles of Statistics taught by Professor Alia Sajjad
Quentin Fruytier
Ph.D. student in the Chandra department of Electrical Engineering (ECE-DICE) at UT Austin working under Professors Aryan Mokhtari, Sanjay Shakkottai, and Sujay Sanghavi.
M.Sc. Graduate from McGill University's department of Mathematics and Statistics supervised by Professor Abbas Khalili and Professor Tim Hoheisel.
Research Interests:
My research interests lie at the intersection of Optimization, Statistics and Artificial Intelligence: Machine Learning (ML). My thesis work focuses on the Expectation-Maximization Algorithm (EM), an iterative algorithm used for Maximum Likelihood Estimation (MLE) of statistical models with incomplete data. Meanwhile, I also enjoy spending time experimenting with Deep Learning frameworks on interesting problems such as traffic prediction and both low and high frequency stock trading.
Graduate Teaching Assistant - Winter 2022
Teaching Assistant for Math 141 (Calculus 2) at McGill under Professor Jerome Fortier
Teaching Assistant - Winter 2019
Teaching Assistant for Comp 202 (Foundations of Programming) in python at McGill under Dr. Giulia Alberini
Data Science Intern at Nectar - Summer 2021
Prototyping of supervised and unsupervised machine learning models on sensor data. Documenting model development, model iterations, and benchmarking results. Writing Python scripts to automate the data exploration process. Communicating results and insights to the operations and product teams
Software Engineer Intern at Akamai Technologies - Summer 2020
Developed and implemented software using existing API to ensure correctness of information in production network configuration file used for deploying software to production.
Software Engineer Intern at Matrox Electronics - Summer 2019
Matrox Electronics Systems Ltd. Software Quality Assurance Montreal, QC Created, improved and fixed automated tests for a product that encodes and sends images over a network to another product that displays it, using C# in Visual Studio.
Research Project: "A Case Against Using Elo or Glicko Algorithms for Rating Players in n vs n Games"
This course project investigates the intricate task of rating players in n vs n zero-sum games, particularly prevalent in the context of online esports titles where large player bases engage in diverse team-based matchups. We explore and evaluate three well-known rating algorithms: the Elo system, Glicko, and Glicko-2, each building upon its predecessor to account for additional variables. A simulation study is conducted to empirically demonstrate the inadequacies of these algorithms in n vs n scenarios where n > 1, highlighting the urgency for tailored solutions. Our findings reveal significant performance drawbacks in current rating systems, especially concerning the failure to consider the performance gap between players during a game. The necessity for a revamped system becomes evident, with a proposed introduction of a rating scale that incorporates game closeness to expedite accurate player rating. The Glicko rating's assumption of a Normal distribution is also challenged by empirical evidence, emphasizing the need for algorithmic adjustments. This project advocates for the development of a novel rating system tailored to n vs n games, emphasizing the incorporation of individual player performance metrics. Despite the challenges, existing performance rating systems in popular esports titles showcase promising avenues for future improvements in the dynamic landscape of player rating and matchmaking.
Master's Thesis: "A Review of the Expectation-Maximization Algorithm and its Applications to Mixture Models."
The Expectation-Maximization (EM) algorithm has long been recognized as a powerful tool for approximating the maximum likelihood estimator in parametric models with latent variables. This thesis provides a selective survey of the existing EM literature, spanning from its original formulation in the 1970s to its present-day developments, with the objective of creating a valuable resource for future research. By exploring the evolution of the EM algorithm, we present both earlier and recent results as well as practical applications in mixture models. Chapter 1 serves as a thorough introduction to the EM, contextualizing it within the broader framework of parameter estimation in parametric models with latent variables. In Chapter 2, we study the general convergence properties of the EM; in particular, we present conditions under which the algorithm’s fitted iterates converge inside a ball centered around the true parameter of the model. Meanwhile, in Chapter 3, we survey the existing literature on the EM algorithm as it relates to Gaussian mixture models and mixed linear regression models. Finally, in Chapter 4, we conclude with a discussion on important aspects such as initialization, SNR, parameterization, and new research directions for the EM algorithm. By collating the wealth of knowledge available on the EM algorithm, this thesis offers researchers a valuable reference for understanding, applying, and advancing the EM algorithm.
Traffic Prediction using DCRNN
This research project aims to analyse and propose improvements to Deep Learning methods for traffic prediction. Traffic Prediction problem is a difficult and important problem impacting multiple industries. Previous methods have struggled to adequatly model the spatial and temporal dependency as well as the non-stationarity of the problem to yield good long and short term predictions. Recently, Deep Learning has become the state of the art method for traffic prediction as it is unmatched for discovering hidden patterns in large datasets. More specifically, Recurrent Neural Networks (RNN), Graph Convolutions and Encoder-Decoder frameworks have led to very succesful tools such as DCRNN, 3D-TGCRNN and SLCNN. Here, I take a deeper look at DCRNN by studying the effect of feature space’s dimension on the METR-LA dataset and suggest improvements to the architecture.