Matthieu Jimenez

Matthieu Jimenez
  • Doctor of Philosophy
  • Research Associate at University of Luxembourg

About

19
Publications
3,905
Reads
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539
Citations
Introduction
I received an Engineering degree in Computer Science with a major in Information Security from Polytech’Nice Sophia in 2014. I got my PhD in October 2018 at the University of Luxembourg defending a thesis on the Evaluation of Vulnerability Prediction Models. My topics of interests include Information Security, Machine Learning and Testing.
Skills and Expertise
Current institution
University of Luxembourg
Current position
  • Research Associate
Additional affiliations
August 2020 - present
University of Luxembourg
Position
  • Research Associate
October 2018 - July 2020
DataThings
Position
  • Engineer
Description
  • Fullstack developer on various data analytics projects
October 2014 - October 2018
University of Luxembourg
Position
  • PhD Student
Description
  • Teaching assistant
Education
October 2014 - October 2018
University of Luxembourg
Field of study
  • Computer Science
September 2011 - July 2014
Polytech Nice Sophia
Field of study
  • Computer Science

Publications

Publications (19)
Poster
Full-text available
This paper analyzes the robustness of state-of-the-art AI-based models for power grid operations under the N − 1 security criterion. While these models perform well in regular grid settings, our results highlight a significant loss in accuracy following the disconnection of a line.Using graph theory-based analysis, we demonstrate the impact of node...
Preprint
Full-text available
This paper analyzes the robustness of state-of-the-art AI-based models for power grid operations under the $N-1$ security criterion. While these models perform well in regular grid settings, our results highlight a significant loss in accuracy following the disconnection of a line.%under this security criterion. Using graph theory-based analysis, w...
Conference Paper
Full-text available
Energy demand forecasting is one of the most challenging tasks for grids operators. Many approaches have been suggested over the years to tackle it. Yet, those still remain too expensive to train in terms of both time and computational resources, hindering their adoption as customers behaviors are continuously evolving. We introduce Transplit, a ne...
Article
Full-text available
Vulnerability prediction refers to the problem of identifying system components that are most likely to be vulnerable. Typically, this problem is tackled by training binary classifiers on historical data. Unfortunately, recent research has shown that such approaches underperform due to the following two reasons: a) the imbalanced nature of the prob...
Preprint
Full-text available
Much of software-engineering research relies on the naturalness of code, the fact that code, in small code snippets, is repetitive and can be predicted using statistical language models like n-gram. Although powerful, training such models on large code corpus is tedious, time-consuming and sensitive to code patterns (and practices) encountered duri...
Preprint
Full-text available
Vulnerability prediction refers to the problem of identifying system components that are most likely to be vulnerable. Typically, this problem is tackled by training binary classifiers on historical data. Unfortunately, recent research has shown that such approaches underperform due to the following two reasons: a) the imbalanced nature of the prob...
Preprint
Full-text available
Vulnerability prediction refers to the problem of identifying the system components that are most likely to be vulnerable based on the information gained from historical data. Typically, vulnerability prediction is performed using manually identified features that are potentially linked with vulnerable code. Unfortunately, recent studies have shown...
Conference Paper
Full-text available
Previous work on vulnerability prediction assume that predictive models are trained with respect to perfect labelling information (includes labels from future, as yet undiscovered vulnerabilities). In this paper we present results from a comprehensive empirical study of 1,898 real-world vulnerabilities reported in 74 releases of three security-crit...
Conference Paper
Background: Code is repetitive and predictable in a way that is similar to the natural language. This means that code is "natural" and this "naturalness" can be captured by natural language modelling techniques. Such models promise to capture the program semantics and identify source code parts that `smell', i.e., they are strange, badly written an...
Thesis
Full-text available
Today almost every device depends on a piece of software. As a result, our life increasingly depends on some software form such as smartphone apps, laundry machines, web applications, computers, transportation and many others, all of which rely on software. Inevitably, this dependence raises the issue of software vulnerabilities and their possible...
Conference Paper
Full-text available
Modern analytics solutions succeed to under- stand and predict phenomenons in a large diversity of software systems, from social networks to Internet-of-Things platforms. This success challenges analytics algorithms to deal with more and more complex data, which can be structured as graphs and evolve over time. However, the underlying data storage...
Conference Paper
In widely used mobile operating systems a single vulnerability can threaten the security and privacy of billions of users. Therefore, identifying vulnerabilities and fortifying software systems requires constant attention and effort. However, this is costly and it is almost impossible to analyse an entire code base. Thus, it is necessary to priorit...
Conference Paper
Full-text available
The Internet of Things (IoT) relies on physical objects interconnected between each others, creating a mesh of devices producing information. In this context, sensors are surrounding our environment (e.g., cars, buildings, smartphones) and continuously collect data about our living environment. Thus, the IoT is a prototypical example of Big Data. T...

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