The proposed project seeks to better understand and study the effective application of data processing tools and quality assurance systems in clinical research. Data from clinical trials is crucial in the pharmaceutical industry because it provides indicators of safety and efficacy of drug treatments for patients. However, processing and reporting on the large amounts of […]

The goal of the project is to develop a predictive model that will help to make a better estimate of the relative altitude using only the barometer sensor inside the Notio device. This measurement of a precise relative altitude is crucial since it is used to compute the slope, which is an important data for […]

Attacks on connected vehicles require special attention and there is a need for new sophisticated security solutions that will cover the integration of different domains in connected vehicles and help proactively address potential threats to connected vehicles. The overall goal of this project is to provide various security solutions for integrity, access control, availability, and […]

Le projet consiste à développer des algorithmes permettant de créer des secteurs pour les clients de Fastercom. Afin d’y parvenir, différentes méthodes de Machine Learning seront évaluées et implémentées. Cette création de secteurs permettra d’améliorer la performance des algorithmes de la compagnie en découpant le gros problème d’optimisation de tournées de véhicules en petits problèmes […]

Among the applications of computer science in the field of health care and biomedicine, the processing of clinical patient records is one of the increasingly important topics for improving the Electronic Health Records (EHR) systems. A practical use of EHR systems is to help improve the decision making process for the physicians. The goal of […]

Machine learning (ML) has recently achieved impressive success in many applications. As ML starts to penetrate into safety-critical domains, security/robustness concerns on ML systems have received lots of attention lately. Very surprisingly, recent work has shown that current ML models are vulnerable to adversarial attacks, e.g. by perturbing the input slightly ML models can be […]

The central role of the Internet in modern society creates challenges of efficiency, flexibility, and security, especially as usage intensifies due to proliferation of mobile devices and the Internet of Things (IoT). Wireless network-based technologies such as Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC), introduce new challenges. In particular, shifting locations and hardware […]

Safety critical software systems such as those that control nir navigntion nre subject to very high standards of quality. They need to explicitly provide system requirements nnd make sure there nre enough test cases that nssure an acceptable level of quality, per requirement. However, with the current fnst pace ofsollworo development, sometimes the program and […]

The advent of 5G (fifth generation) telecommunication networks also brings new security challenges, in addition to many benefits to the community. Such is exemplified by its special nature of technology (as well as the new business model) and its deep involvement in people’s everyday life, hence more critical. We need proper security metrics to tell […]

Machine learning algorithms are being used in a wide range of applications. It is a branch of computer science where the system can learn from the data and make decisions. Financial fraud is an increasing hazard in the financial industry, and it is important to detect a fraudulent transaction. Machine learning algorithms can be used […]

Our project develops novel machine learning algorithms for interpreting complex, multi-agent scenarios in sports analytics. The collaboration with our industrial partner SPORTLOGiQ will tackle open problems in deep reinforcement learning to build novel capabilities in sports analytics for ice hockey. Deep reinforcement learning is a breakthrough technology with prominent successes in games such as Go […]

In this project, we propose a continual learning approach to face the problem of catastrophic forgetting in online image classification problems. Concretely, we propose a model that learns how to mask a series of general modules in a deep learning architecture, so that generalization emerges through the composition of those modules. This is of vital […]