I'm Marie Le Guilly, currently working as a computer science teacher. After a few years as a data science researcher, I decided to dedicate my carreer to share my knowledge on the topics I'm passionnate about. My main interest is on artifical intelligence and machine learning with Python, but I also teach and create lessons on Java programming, databases and mathematics.
Teacher for computer science students
Apprentissage automatique pour la cybersécurité
Research internship at the Data Systems group, with Prof. Ihab Ilyas
Introduction to programmation, and java programming. Undergraduate students.
Research internship on data exploration using SQL query completion.
Analysis of the data from a boring machine, prediction of soil compaction based on the machine’s parameters
Development of a test platform for biometric data
Could Functional Dependencies Help to Identify Balanced Classification Datasets ?
SQL query completion for data exploration
Guide data selection for predictive models (Link to dissertation)
Databases and machine learning (ML) have historically evolved as two separate domains: while databases are used to store and query the data, ML is devoted to predictive models inference, clustering, etc. Despite its apparent simplicity, the “data preparation” step of ML applications turns out to be the most time-consuming step in practice. Interestingly this step encompasses the bridge between databases and ML. In this setting, we raise and address three main problems related to data selection for building predictive models. First, the database usually contains more than the data of interest: how to separate the data that the analyst wants from the one she does not want? We propose to see this problem as imbalanced classification between the tuples of interest and the rest of the database. We develop an undersampling method based on the functional dependencies of the database. Second, we discuss the writing of the query returning the tuples of interest. We propose a SQL query completion solution based on data semantics, that starts from a very general query, and helps an analyst to refine it until she selects her data of interest. This process aims at helping the analyst to design the query that will eventually select the data she requires. Third, assuming the data has successfully been extracted from the database, the next natural question follows: is the selected data suited to answer the considered ML problem? Since getting a predictive model from the features to the class to predict amounts to providing a function, we point out that it makes sense to first assess the existence of that function in the data. This existence can be studied through the prism of functional dependencies, and we show how they can be used to understand a model’s limitation, and to refine the initial data selection if necessary.
Evaluating classification feasibility using functional dependencies, Marie Le Guilly, Jean-Marc Petit and Vasile-Marian Scuturici, Transactions on Large-Scale Data and Knowledge-Centered Systems (TLDKS), 27 pages (to appear) Access paper
A First Experimental Study on Functional Dependencies for Imbalanced Datasets Classification, Marie Le Guilly, Jean-Marc Petit and Vasile-Marian Scuturici, « Communications in Computer and Information Science » series, Springer, Revised Selected Papers from the International Workshop ISIP, Japan, 2018, 17 pages
Contextualisation of Datasets for better classification models: Application to Lyon Airbus Helicopters Flight Data 2020, Marie Le Guilly, Daouayry Nassia, Pierre-Loic Maisonneuve, Ammar Mechouche, Jean-Marc Petit and Marian Scuturici, 24th European Conference on Advances in Databases and Information Systems (ADBIS), 10 pages (to appear)
Attempt to better trust classification models: Application to the Ageing of Refrigerated Graz Transport Vehicles (industrial paper), Marie Le Guilly, Claudia Capo, Marian Scuturici, Jean Marc Petit, Rémi Revellin, Jocelyn Bonjour, Gérald Cavalier 25th International Symposium on Methodologies for Intelligent Systems (ISMIS’20), 10 pages (to appear) Access paper
ExplIQuE: Interactive Databases Exploration with SQL (demo paper), Marie Le Guilly, Jean-Marc Petit, Vasile-Marian Scuturici and Ihab Ilyas, 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, China, 2019, 4 pages Access paper
Ageing of refrigerated transport vehicles: development of a numerical predictive model, Marie Le Guilly, Claudia Capo, Léo Pape, Marian Scuturici, Jean Marc Petit, Rémi Revellin, Jocelyn Bonjour, Gérald Cavalier, 6th IIR conference on sustainability and the cold chain, 9 pages (to appear)
Evaluation de la faisabilité de classification en utilisant les dépendances fonctionnelles, Marie Le Guilly, Jean-Marc Petit and Vasile-Marian Scuturici, BDA 2019, 35ème conférence sur la Gestion de Données — Principes, Technologies et Applications, 12 pages
ExplIQuE : Exploration Interactive de Bases de Données en SQL, Marie Le Guilly, Jean-Marc Petit, Vasile-Marian Scuturici and Ihab Ilyas, BDA 2019 35ème conférence sur la Gestion de Données — Principes, Technologies et Applications, 4 pages
Partitioning queries for data exploration using query extensions, Marie Le Guilly, Ihab Ilyas, Jean-Marc Petit and Vasile-Marian Scuturici, BDA 2018, 34ème conférence sur la Gestion de Données — Principes, Technologies et Applications
Langages de requêtes interactifs pour l’exploration de données, Marie Le Guilly, BDA 2017, 33ème conférence sur la Gestion de Données — Principes, Technologies et Applications (article Doctorant)
Retour d’expérience sur l’analyse des données d’un tunnelier, Marie Le Guilly, Jean-Marc Petit, Vasile-Marian Scuturici, BDA 2017 33ème conférence sur la Gestion de Données — Principes, Technologies et Applications
Completion de requêtes SQL ; Marie Le Guilly, Jean-Marc Petit, Vasile-Marian Scuturici, French patent FR1757682
Python, Java, C/C++
PL/SQL, MysQL, Oracle
Latex, Git, SVN