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ESR2: Laura Boggia (IBM and Sorbonne University)

  • ESR1: Patin Inkaew (University of Helsinki)
  • ESR5: Fotis Giasemis (Sorbonne University)
  • ESR7: James Gooding (TU Dortmund)
  • ESR8: Micol Olocco (TU Dortmund)
  • ESR6: Daniel Magdalinski (VU Amsterdam)
  • ESR2: Laura Boggia (IBM and Sorbonne University)
  • ESR3: Leon Bozianu (University of Geneva)
  • ESR4: Sofia Cella (CERN and University of Geneva)
  • ESR11: Henrique Piñeiro Monteagudo (Verizon Connect and UniBo)
  • ESR12: Pratik Jawahar (University of Manchester)
  • ESR10: Joachim Carlo Kristian Hansen (Lund University)
  • ESR9: Carlos Cocha (University of Heidelberg)
Home Early Stage Researchers ESR2: Laura Boggia (IBM and Sorbonne University)

ESR2: Real-time rule induction in fraud detection and HEP

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Project 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 (particle collision) patterns. 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. ESR2 will explore different angles in how we intend to combine numerical and knowledge models.

Host country: France
Host beneficiary: IBM France
PhD-awarding institution: Sorbonne
Planned collaborations: CNRS

ESR: Laura Boggia

I am fascinated by particle physics and theoretical quantum computing. Further, I have a strong interest in the theory and numerical methods necessary for searches of particle physics beyond the Standard Model. Through the course of my PhD, I hope to learn more about interesting applications of machine learning models and the theoretical framework of particle physics.

Institute and supervisor information

IBM’s greatest invention is the IBMer. We believe that through the application of intelligence, reason and science, we can improve business, society and the human condition, bringing the power of an open hybrid cloud and AI strategy to life for our clients and partners around the world.

Restlessly reinventing since 1911, we are not only one of the largest corporate organizations in the world, we’re also one of the biggest technology and consulting employers, with many of the Fortune 50 companies relying on the IBM Cloud to run their business.

At IBM, we pride ourselves on being an early adopter of artificial intelligence, quantum computing and blockchain. Our employees join us on our journey to being responsible technology innovators and a force for good in the world.

IBM Research has set up a new team at the IBM Artificial Intelligence Co-Innovation Center in Paris-Saclay. It is be dedicated to the development of safe, explainable and responsible AI systems utilizing knowledge-based and data-driven AI techniques. The new team will work with IBM’s global Research team and will be co-located with the French software development teams in charge of IBM business rules, decision automation and decision optimization products.

Image of Pierre-Feillet

Pierre Feillet

Pierre Feillet is currently Chief Architect for Business Automation, Decisioning & AI Acceleration, IBM France.

His main responsibility is to innovate in the use and adoption of AI and Big Data in Business Automation to provide out-of-the-box intelligent features and integration points to accelerate client digital transformation.

In addition Pierre drives the architecture of the IBM core Decisioning technology in multiple dimensions – a) combining deterministic rules with predictive models, b) deliver OOTB rule mining to ease the bootstrap of new automation projects from data, c) empowering business users with analytics and large scale simulations to validate their decision logic against production grade data and d) achieving higher level of performance to support massive and pervasive use of decisions everywhere including at the edge.

LinkedIn

image of Christian-de-Sainte-Marie

Christian de Sainte Marie

Christian de Sainte Marie leads the Center for Advanced Studies in IBM France, and the IBM Research Paris-Saclay team. Rule induction is one of the main research subjects of the team, in particular exploring approaches that  combine neural networks and symbolic approaches.

Email Christian | LinkedIn

Bogdan Malaescu

Bogdan Malaescu is staff researcher at CNRS, based in the LPNHE laboratory at the Sorbonne University. His main areas of expertise include the Standard Model in particle physics, QCD, jet physics, statistics, inverse problems. He is currently Convener of the ATLAS Statistics Committee. He is former convener of the ATLAS Standard Model group, former convener of the “Jet energy scale and resolution” and “Jets and photons” subgroups in ATLAS, former convener of the ATLAS Statistics Forum.

Webpage | Email Bogdan

ABOUT US

SMARTHEP is a network that connects the fields of High Energy Physics (HEP) and Data Science, especially in relation to the challenges of  processing large datasets using real-time analysis.

SMARTHEP is intended as a consortium formed by academic and industrial partners on scientific, technological, and entrepreneurship aspects of both HEP and Data Science.

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SMARTHEP is funded by the European Union’s Horizon 2020 research and innovation programme, call H2020-MSCA-ITN-2020, under Grant Agreement n. 956086

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