Raphaël Attias

Raphaël Attias

Software Engineer @ Databricks || Visiting Postgraduate Researcher @ Harvard || MS in Data Science and BS in Mathematics from EPFL.

Portfolio

What is the role of the media coverage in explaining stock market fluctuations?

Applied Data Analysis class, 100/100, 2021
During this semester long project, we studied in a group of 4 the fluctuation of the Apple stock price, volume and its relationship with quotes in media. Using Yahoo API, Quotebank Dataset and web parsing, we show a visible and quantifiable correlation in Apple coverage and the price of its stock. This project contains a full introduction of the datasets, a study of meaningful events in Apple recent history and a fitted time series prediction taking into account the multiple results of the project. The results have been presented on an interactive website, which required knowledge in data visualization to tell our data story.

Exploration of D-Cliques Variations and Edges Cases for Decentralized Federated Learning

Research Project, 90/100, 2021
Researched the decentralized implementation of a Federated Learning algorithm in the Scalable Computing Systems Laboratory at EPFL. Supervised by Prof. Karmarec, I explored and identify the computational implications of a Decentralized approach to a state-of-the-art topology (D-Cliques) in a research environment. D-Cliques (Bellet et al. 2021) is a recent approach to coordinate and structure a network for DFL. While a fully connected topology is not practical due to the number of edges increasing quadratically, D-Cliques provide an intuitive approach in providing locally fully connected neighborhoods.

Towards Robust and Adaptable Diagnosis of Pneumonia from Chest X-ray Data

Visual Intelligence, 90/100, 2021
Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that accurately diagnose pneumonia from a chest X-Ray images using deep neural networks when trained on a sufficient large and homogeneous amount of labelled images. However, the robustness and adaptability of these systems, trained minimizing the empirical risk (ERM), remains far way. In fact, ERM have no way of discard environment specific features creating an alarming situation in which the systems appear accurate, but fail when tested in new hospitals.