One of the key researches in the domain of quantitative information flow (QIF) is to effectively estimate information leaks in a system in order to prevent adversarial attacks. Most existing approaches are based on the white-box approach. However, this approach is often impractical due to the size or complexity of its internals, or the presence of unknown factors. This and other challenges forced a shift in focus to investigate methods for measuring a system’s leakage in a black-box manner.
While scientists have successfully reduced the size and costs of electronic components, a major challenge faced by such tiny devices is the absence of an optimum thermal and energy management technology. To bridge that gap, Elison Matioli and his colleagues at EPFL’s Power and Wide-band-gap Electronics Research Laboratory (POWERlab) have developed a novel microchannel network that not only cools electronic components but also makes them energy efficient.
Facebook and EPFL have initiated a collaborative program that aims to carry out seminal research with common meeting points for both organizations. Facebook seeks to leverage EPFL’s proven expertise in Computer Science and Engineering to enable the flow of technology from one of the most renowned research institutions to the leading American social media conglomerate. The collaboration will also help the latter strengthen its position in Switzerland and gain access to some of the best academic minds in Europe.
We are investigating new stochastic theories and analytics including statistical learning techniques, machine training and inference systems and their applications to IoT, social media and big data platforms.
We are investigating technologies to maximize efficiency with in-memory data services, in-situ query processing on streamed data, and on-demand query engine customization using multi-objective compiler optimization.
We are investigating a three-pronged approach to holistic data platforms and datacenter efficiency: vertical integration to minimize data-movement; specialization to optimize work per service; and approximation to tailor work for output quality.
We are investigating technologies spanning from decentralized trust and cryptography to robustness and resiliency of natural processes to strengthen security, privacy and trust in data platforms and systems.