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Real-time rule induction in fraud detection and HEP
by Tristan Beau - 26 April 2022
Title : Real-time rule induction in fraud detection and HEP
Supervisor : Mélissa Ridel
Co-supervisor : Bogan Malaescu
LPNHE Team : ATLAS
Description :
The goal of this PhD project is to automate the learning of a decision model by new combinations of statistical and knowledge based models applied to fraud detection and in high energy physics. Real time decision making combines today’s analytics and knowledge based models for fraud detection, notably in banking. Payment platforms detect in real time fraudulent transactions by combining recognition of human created patterns articulated on their banking symbolic knowledge model, and predictive models run to discover emerging fraud patterns by detecting new trends and anomalies from the data. In this thesis the student will work on new combinations of statistical and knowledge based models for a better decision automation in fraud detection and in high energy physics, for the recognition of human-created (fraud) and non-human-created patterns (in particle collision recorded with the ATLAS detector). While machine learning has been highly popular during the last years, their black box approach raises interpretability and explainability challenges. On the other hand, symbolic models, including rules, have been successful in making decisions more interpretable. Nevertheless they require to capture an existing knowledge or theory. In the context of real time decision automation, the student involved in this project will test the proposed numeric to symbolic model inferences to detect anomalies, patterns and anti-patterns, combining the efficiency of the numerical machine learning and the explainability of the symbolic approach. They will inject theory (knowledge from the Standard Model in physics, fraud detection patterns in financial transactions) and combine it with predictive models to classify observations and add an interpretability layer. Different angles in how we intend to combine numerical and knowledge models will be explored.
Web link :
[https://www.smarthep.org/positions/esr2/]
Workplace :
LPNHE, Paris and belgium
Also in this section :
- Mesure de l’évolution du taux d’expansion de l’univers par la combinaison des relevés de supernovae ZTF et Subaru
- Extending the search potential for axion-like particles decaying into two photons with the ATLAS detector at the LHC
- Préparation de l’expérience Hyper-Kamiokande - un observatoire unique pour des événements rares dans l’Univers
- Révéler le mystère des rayons cosmiques par la radio : modélisation et analyse de signaux radios détectés par GRAND
- Recherche de la diffusion élastique cohérente des neutrinos solaires par l’expérience de matière noire XENONnT
- Etalonnage des jets, mesures de sections efficaces et extraction d’alpha_S dans ATLAS et au Futur Collisionneur Circulaire au CERN (FCC-ee)
- Mesures des paramètres d’oscillations de neutrinos avec le détecteur proche upgradé de T2K
- Tester l’invariance de Lorentz avec les sources astrophysiques de haute énergie : l’aube d’une nouvelle ère
- Réseaux de neurones et apprentissage profond pour la détection et la reconstruction des rayons cosmiques dans le domaine radio
- Développement d’algorithmes de reconstruction de particules fondés sur l’intelligence artificielle
- Recherche de la diffusion élastique cohérente de neutrinos issus de supernovæ avec l’expérience XENONnT
