Posts by Collection

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.

Quantification of the suitable rooftop area for solar panel installation from overhead imagery using Convolutional Neural Networks

Published in Journal of Physics, 2021

The integration of solar technology in the built environment is realized mainly through rooftop-installed panels. In this paper, we leverage state-of-the-art Machine Learning and computer vision techniques applied on overhead images to provide a geo-localization of the available rooftop surfaces for solar panel installation. We further exploit a 3D building database to associate them to the corresponding roof geometries by means of a geospatial post-processing approach. The stand-alone Convolutional Neural Network used to segment suitable rooftop areas reaches an intersection over union of 64% and an accuracy of 93%, while a post-processing step using building database improves the rejection of false positives. The model is applied to a case study area in the canton of Geneva and the results are compared with another recent method used in the literature to derive the realistic available area.

Download here

Talk 1 on Relevant Topic in Your Field

This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!

Tutorial 1 on Relevant Topic in Your Field

More information here

Talk 2 on Relevant Topic in Your Field

More information here

Conference Proceeding talk 3 on Relevant Topic in Your Field

This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.