Machine Learning-based Algorithms for the Futur Upstream Tracker Standalone Tracking Performance of LHCb at the LHC
This proposal focuses on enhancing tracking performance for the LHCb experiments during Run 5 at the Large Hadron Collider (LHC) through the exploration of various machine learning-based algorithms. The Upstream Tracker (UT) sub-detector, a crucial component of the LHCb tracking system, plays a vital role in reducing the fake track rate by filtering out incorrectly reconstructed tracks early in the reconstruction process. As the LHCb detector investigates rare particle decays, studies CP violation in the Standard Model, and study the Quark-Gluon plasma in PbPb collisions, precise tracking becomes increasingly important.
With upcoming upgrades planned for 2035 and the anticipated increase in data rates, traditional tracking methods may struggle to meet the computational demands, especially in nucleus-nucleus collisions where thousands of particles are produced. Our project will investigate a range of machine learning techniques, including those already demonstrated in the LHCb’s Vertex Locator (VELO), to enhance the tracking performance of the UT. By applying diverse methods, we aim to improve early-stage track reconstruction, increase efficiency, and decrease the fake track rate. Among these techniques, Graph Neural Networks (GNNs) are a particularly promising option, as they can exploit spatial and temporal correlations in detector hits to improve tracking accuracy and reduce computational burdens.
This exploration of new methods will involve development work tailored to the specific hardware selected for deployment, whether it be GPUs, CPUs, or FPGAs, all part of the futur LHCb’s data architecture. We will benchmark these algorithms against current tracking methods to quantify improvements in performance, scalability, and computational efficiency. Additionally, we plan to integrate the most effective algorithms into the LHCb software framework to ensure compatibility with existing data pipelines.
Foundations of Semantic Reasoning for Enhanced AI Cooperation in 6G Multi-Agent Communications
This PhD research project focuses on pioneering foundational theories in semantic communications, specifically by advancing the compositional learning and reasoning capabilities of AI agents within multi-agent environments. The goal is to enable AI-driven systems to selectively extract, interpret, and exchange semantically meaningful information in a goal-oriented and context-sensitive manner, moving beyond traditional data-oriented communications. Integrated Sensing and Communication (ISAC) will serve as a validation ground, introducing unique constraints related to resource optimization, multi-modal sensing, and environmental adaptability. However, the primary aim is to establish broadly applicable methodologies that enhance semantic information exchange and cooperation among AI agents. This involves rethinking the combination of modeling, numerical simulation, and experimental validation to effectively implement AI-driven semantic information exchange.
Advancing Semantic Representation, Alignment, and Reasoning in Multi-Agent 6G Communication Systems
Semantic communications is an emerging and transformative research area, where the focus shifts from transmitting raw data to conveying meaningful information. While initial models and design solutions have laid foundational principles, they often rest on strong assumptions regarding the extraction, representation, and interpretation of semantic content. The advent of 6G networks introduces new challenges, particularly with the growing need for multi-agent systems where multiple AI-driven agents interact seamlessly.
In this context, the challenge of semantic alignment becomes critical. Existing literature on multi-agent semantic communications frequently assumes that all agents share a common understanding and interpretation framework, a condition rarely met in practical scenarios. Misaligned representations can lead to communication inefficiencies, loss of critical information, and misinterpretations.
This PhD research aims to advance the state-of-the-art by investigating the principles of semantic representation, alignment, and reasoning in multi-AI agent environments within 6G communication networks. The study will explore how agents can dynamically align their semantic models, ensuring consistent interpretation of messages while accounting for differences in context, objectives, and prior knowledge. By leveraging techniques from artificial intelligence, such as machine learning, ontology alignment, and multi-agent reasoning, the goal is to propose novel frameworks that enhance communication efficiency and effectiveness in multi-agent settings. This work will contribute to more adaptive, intelligent, and context-aware communication systems that are key to the evolution of 6G networks.
Enhancing Communication Security Through Faster-than-Nyquist Transceiver Design
In light of the growing demand for transmission capacity in communication networks, it is essential to explore innovative techniques that enhance spectral efficiency while maintaining the reliability and security of transmission links. This project proposes a comprehensive theoretical modeling of Faster-Than-Nyquist (FTN) systems, accompanied by simulations and numerical analyses to evaluate their performance in various communication scenarios. The study will aim to identify the necessary trade-offs to maximize transmission rates while considering the constraints related to implementation complexity and transmission security, a crucial issue in an increasingly vulnerable environment to cyber threats. This work will help identify opportunities for capacity enhancement while highlighting the technological challenges and adjustments necessary for the widespread adoption of these systems for critical and secure links.
3D ultrasound imaging using orthogonal row and column addressing of the matrix array for ultrasonic NDT
This thesis is part of the activities of the Digital Instrumentation Department (DIN) in Non-Destructive Testing (NDT), and aims to design a new, fast and advanced 3D ultrasound imaging method using matrix arrays. The aim will be to produce three-dimensional ultrasound images of the internal volume of a structure that may contain defects (e.g. cracks), as realistically as possible, with improved performance in terms of data acquisition and 3D image computation time. The proposed method will be based on an approach developed in medical imaging based on Row and Column Addressed (RCA) arrays. The first part will focus on the development of new data acquisition strategies for matrix arrays and associated ultrafast 3D imaging using RCA approach in order to deal with conventional NDT inspection configurations. In the second part, developed methods will be validated on simulated data and evaluated on experimental data acquired with a conventional matrix array of 16x16 elements operating in RCA mode. Finally, a real-time proof of concept will be demonstrated by implementing the new 3D imaging methods in a laboratory acquisition system.