I am a PhD student in Computer Science in the Department of Computer and Information Sciences at the University of Delaware working under the supervision of Professor Beheshti within the healthy lAife lab.
I received my Bachelor of Engineering Science and my Master of Science in Engineering (Computational Finance track) from EFREI Paris in 2018 and 2020. When I'm not working you can find me playing tennis, taking photos, watching the Montreal Canadiens, or cheering for the French National Football (soccer) team.
My research iterests lie at the intersection of Machine Learning and Healthcare. Specifically, I am interested in developing robust Deep Learning techniques for Electronic Health Records to tackle problems related to Fairness, Missing Labels and Few-Shot Learning, Multi-Task Learning, or more generally, Cardiovascular Disease.
Our paper, "Graph Transformers on EHRs: Better Representation Improves Downstream Performance" has been accepted in ICLR 2024. [Paper]
I will be joining Pinterest (PinLabs) as an ML intern within the Inclusive AI team for the Summer 2023.
Our paper, "Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods" has been accepted in FAccT 2023. [Paper]
Our paper, "An Extensive Data Processing Pipeline for MIMIC-IV" has been accepted in ML4H 2022. [Paper]
Our paper, "Few-Shot Learning with Semi-Supervised Transformers for Electronic Health Records" has been accepted in MLHC 2022. [Paper]
Our paper, "Flexible-window Predictions on Electronic Health Records" has been accepted in IAAI 2022. [Paper]
Our paper, "Transformer-based Multi-target Regression on Electronic Health Records for Primordial Prevention of Cardiovascular Disease" has been accepted in BIBM 2021. [Paper]
Joined the healthy lAife lab @ University of Delaware as a PhD Student.
I built an end‑to‑end pipeline to quantify and analyze demographic biases in text‑to‑image generative models, and proposed multiple solutions to mitigate biases issues such as Textual Inversion, Prompt Engineering, and fair fine‑tuning through LoRA.
I built a testing automation tool using Python and Cucumber to help the Data Shop team running their unit tests during the development of the company's new on-demand market data platform.
I built an architecture dataflow visualization tool for their trading platform, Optiq to help engineers visualize how each applications communicate with each other and follow a specific data stream from its creation to its endpoint.
For our Master's Capstone Project, we designed a Portfolio Management tool that would create a personified portfolio according to the user's needs, takig into account which stocks can be included in the portfolio and the maximum amount of risk they are willing to take. The creation is made following the Markowitz's Optimal Portfolio using the returns we predicted using a Long Short-Term Memory RNN.
As a personal projet to introduce myself to Machine Learning algorithms, I used a Random Forest Regression model to predict NHL Players' salaries given their in-game statistics and personal information. I took in consideration the players' birthday year, average time on ice, and the team’s number of goals while the player was on the ice. I also used a Google News API to simulate the player’s popularity by getting the number of articles he appeared in during the season.
For our Junior's year Capstone project, we programmed a parking lot simulation populated by autonomous cars to teach them how to park using a genetic algorithm. We used Javascript for both the algorithm itself and the visuals of our applications with the P5 library.
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