All projects Privacy Protection & CryptographyBlockchains & Smart ContractsSoftware VerificationDevice & System SecurityMachine LearningFinanceHealthGovernment & HumanitarianCritical InfrastructureDigital Information
Jul 2019 → Dec 2021 Project

Deep Learning, Jumps, and Volatility Bursts

Partner: Swissquote
Partner contact: Serge Kassibrakis
EPFL laboratory: Swiss Finance Institute @ EPFL
EPFL contact: Prof. Damir Filipovic, Alexis Marchal

We develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the current benchmark on simulated data. We use a long short- term memory (LSTM) neural network that is trained on labelled data generated by a process that experiences both jumps and volatility bursts. As a result, the network learns how to disentangle the two. Then it is applied to out-of-sample simulated data and delivers results that considerably differ from the benchmark: we obtain fewer spurious detection and identify a larger number of true jumps. When applied to real data, our approach for jump screening allows to extract a more precise signal about future volatility.

TopicsMachine LearningFinance

Jan 2019 → Dec 2021 Project

Monitoring, Modelling, and Modifying Dietary Habits and Nutrition Based on Large-Scale Digital Traces

Partner: Microsoft
Partner contact: Ryen W. White
EPFL laboratory: Data Science Lab
EPFL contact: Prof. Robert West, Kristina Gligoric

The overall goal of this project is to develop methods for monitoring, modeling, and modifying dietary habits and nutrition based on large-scale digital traces. We will leverage data from both EPFL and Microsoft, to shed light on dietary habits from different angles and at different scales. Our agenda broadly decomposes into three sets of research questions: (1) Monitoring and modeling, (2) Quantifying and correcting biases and (3) Modifying dietary habits. Applications of our work will include new methods for conducting population nutrition monitoring, recommending better-personalized eating practices, optimizing food offerings, and minimizing food waste.

TopicsMachine LearningHealth

Nov 2018 → Dec 2021 Project

Digitalizing search for missing persons

Partner: CICR, FLO
Partner contact: Fabrice Lauper
EPFL laboratory: Distributed Information Systems Laboratory (LSIR)
EPFL contact: Prof. Karl Aberer, Rémi Lebret

Armed conflicts, violence and migration are causing large scale separation of family members, dislocation of family links and missing persons. People must receive help to know what happened to reconnect to their loved ones as rapidly as possible. The ICRC and LSIR through its partnership have set themselves a challenge to analyse publicly available data through analytics techniques to identify missing persons that would arguably not have been identified using current, conventional methods. The goal of this project is to facilitate the search for missing individuals by building scalable, accurate systems tailored for that purpose.

TopicsMachine LearningGovernment & Humanitarian

Jul 2018 → Oct 2018 Project


Partner: Cisco
Partner contact: Frank Michaud
EPFL laboratory: Signal Processing Laboratory (LTS4)
EPFL contact: Prof. Pascal Frossard, Apostolos Modas

SafeAI aims to develop cyber-security solutions in the context of Artificial Intelligence (AI). With the advent of generative AI, it is possible to attack AI enhanced applications with targeted cyberattacks, and also to generate cyberattacks that are automated and enhanced via the use of AI. The main goal of SafeAI is the development of a software that enables automated generation of adversarial attacks and defences using AI.

TopicsDevice & System SecurityMachine Learning