All Years Seminars
[INGI] 2026-10-08 (13:00) : 2 INGI seminars in one: Abbas Mohsenpour & Samuele Quinzi (ICTEAM)
At shannon, Maxwell a.105
Section 1:ChatBGP: A System for Fast, Accurate, and Intuitive Querying of Large-Scale BGP Datasets
Speaker :
Abbas Mohsenpour (ICTEAM-INGI)
Abstract : BGP data collected from operational Internet routers is essential for understanding Internet connectivity, routing, and security. However, its growing volume makes querying and analyzing large datasets challenging with existing tools. We present ChatBGP, a domain-specific chatbot that allows users to ask questions about BGP data in natural language and automatically generates efficient code to retrieve and analyze the required information. By combining a custom database design, a simple API, and LLM-based code generation, ChatBGP enables fast and intuitive exploration of large-scale BGP datasets, supporting network operators, researchers, and students.
Section 2:With or Without RPKI: Measuring the Impact of RPKI on the BGP Updates Volume
Speaker :
Samuele Quinzi (ICTEAM-INGI)
Abstract : The Resource Public Key Infrastructure (RPKI) is the primary defense against BGP route misorigination. It offers some barriers against origin hijacks, flavors of route leaks, and misconfigurations. Since its deployment in 2011, the adoption of RPKI by Internet Service Providers has shown continuous growth, a trend that persists to this day. As this growth continues it is important to measure its effect on BGP stability. BGP is a chatty protocol with many updates that can result from a single configuration change on one router. For instance, the addition of a new Route Origin Authorization in the RPKI may result in a change in the validity of a route advertised in BGP, and consequently in different routing decisions that are then propagated in BGP. Here we aim to estimate the volume of updates observed in BGP upon such changes. We identify events resulting from ROA changes and estimate the amount of BGP updates observed in a public BGP repository during these events. We observe that as RPKI adoption rises, the volume of updates generated by RPKI-related changes grows at a similar rate. This growth also mirrors the expansion of the routing table, increasing as more address space is announced and protected by RPKI. However, we also have good news: despite this growth, RPKI-induced updates remain only a very small fraction, corresponding to less than 1% of the total volume of updates.
[INMA] 2026-10-06 (14:00) : Newcomers Seminar
At Euler building (room A.002)
Section 1:Anthony Couthures
Speaker :
Anthony Couthures (UCLouvain)
Abstract : I will first discuss my Ph.D. research at Université de Lorraine on multi-agent systems and environmental modeling. I will give a high-level introduction to opinion dynamics and explain how I used game theory and network approaches to model the feedback loop between social polarization and climate change negotiations, investigating phenomena like the "oscillatory tragedy of the commons."
Next, I will introduce my current postdoctoral work with Julien M. Hendrickx and Gianluca Bianchin on robust control and optimization. We are adapting the Performance Estimation Problem (PEP) framework, which was originally developed for analyzing optimization algorithms, to evaluate control systems. I will show how we use this framework to quantify the exact finite-horizon robustness of Linear Quadratic Regulators (LQR) under LTI uncertainties by reformulating the worst-case search as a Semidefinite Program (SDP).
Section 2:Manifolds everywhere: from derivative-free optimization to data fitting.
Speaker :
Sacha Benarroch (UCLouvain)
Abstract : This talk is in two parts. First, a known limitation of direct search algorithms for derivative-free optimization is that of solving problems with equality constraints. This talk investigates the use of Riemannian optimization tools to solve derivative-free problems in the case where the feasible set for equality constraints makes up a Riemannian submanifold of R^n. Theoretical and practical performance of the obtained feasible method are addressed. Leaving the realm of optimization, the talk then moves on to Riemannian data fitting. While methods to interpolate manifold-valued data exist, they suffer from some shortcomings, typically restrictive assumptions about the so-called data sites and approximations due to the use of Euclidean data fitting techniques on the tangent spaces. The second part of this talk provides an overview of these shortcomings and somes avenues to address them.
Section 3:Sum-of-Squares Programming via Optimization on Manifolds
Speaker :
Charles Van Hees (UCLouvain)
Abstract : For decades, the nonnegativity of polynomials has been of concern to mathematicians across several fields: global optimization, control theory, probability, etc. Nevertheless, in the general case, determining whether a polynomial is nonnegative or not is a NP-hard problem. For univariate polynomials however, nonnegativity is equivalent to being a sum of two squares: there exist two polynomials such that the sum of their squares equals the given polynomial.
A few years ago, Legat et al. derived an algorithm for finding a sum-of-squares decomposition of a univariate polynomial satisfying a set of linear constraints. The novel contribution of this Master’s thesis is the introduction of a linear objective we aim to minimize. This problem is traditionally modelled as an SDP and solved using, for instance, an Interior Point Method. However, SDPs quickly become intractable due to the number of variables, which grows quadratically in the size of the problem. Hence, we rely on an idea proposed by Burer and Monteiro at the beginning of the 21st century, by considering an equivalent nonlinear formulation whose variables are the two polynomials of the sum-of-squares decomposition. While introducing nonconvexity, it drastically reduces the size of the problem.
Among the various methods that exist to solve such problems (Augmented Lagrangian, Projected Gradient, etc.), we opted for an approach based on manifolds. We derive a first-order Riemannian optimization algorithm that can be used when the feasible set is a smooth manifold. Although the feasible set is surprisingly smooth in many cases (when the constraint operator is bijective, or near pairs of polynomials sharing at most one common root), it turns out that it is not a smooth manifold for every set of linear constraints defining it.
[INGI] 2026-10-06 (13:00) : Data Replication in geo-distributed systems
At shannon, Maxwell a.105
Speaker :
Cherif SI MOHAMMED (IMT Atlantique)
Abstract : The presentation will be done in English.
The shift from Cloud Computing to Edge Computing has renewed the challenges of managing data in geo-distributed environments, particularly for large-scale data processing. Data replication, initially introduced to improve resilience and availability, is becoming increasingly costly due to growing data volumes and the resulting storage and bandwidth requirements. This thesis investigates dynamic replication strategies to reduce these costs while maintaining application performance. First, we study the state of the art in existing approaches for optimizing data replication and identify the main factors that support decision-making for adapting replication strategies. Second, we address the scheduling of Bag-of-Tasks applications that access shared data. We propose heuristic approaches that dynamically manage data replication and demonstrate, through comparison with exact methods, their ability to achieve near-optimal solutions while reducing data movement and resource consumption. Finally, as our heuristics make decisions for one job at a time based only on currently available resources, we investigate decision-making mechanisms to improve global system performance by jointly considering data and task migration across the geo-distributed infrastructure. Overall, this work contributes to more adaptive and resource-efficient data replication management strategies for Bag-of-Tasks applications in geo-distributed computing systems.
[INMA] 2026-09-29 (14:00) : Hybrid artificial intelligence for solving computationally hard problems
At Euler building (room A.002)
Speaker :
Quentin Cappart (UCLouvain)
Abstract : Combinatorial optimization provides methods to make the
best possible decisions in complex scenarios, with practical
applications in areas such as transportation, logistics, and healthcare.
Traditionally, solving methods for combinatorial problems (such as
integer programming, constraint programming, or local search) have
focused on solving isolated problem instances, often overlooking the
fact that these instances frequently originate from related data
distributions. In recent years, there has been a growing interest in
leveraging machine learning, particularly neural networks, to enhance
combinatorial solvers by utilizing historical data. Despite this
interest, it remains unclear how to effectively integrate learning into
such engines to boost overall performance. In this presentation, I will
share my journey in tackling this challenge.
[ELEN] 2026-09-24 (11:00) : Emerging Memory Integration for Energy-Efficient Edge Computing
At Shannon
Speaker :
Erica Covi (Technical University of Munich )
Abstract : The shift toward edge computing has enabled real-time data processing closer to the source of data collection, reducing latency and improving overall efficiency. Yet this shift imposes strict constraints on power consumption, physical footprint, and computational performance. These constraints cannot be met by conventional hardware approaches alone. At the same time, logic and memory technologies face increasing complexity as continued scaling necessitates consideration of multiple physical processes to sustain device, circuit, and system reliability. This complexity drives the need for Design-Technology-Co-Optimization (DTCO) and System-Technology-Co-Optimization (STCO) approaches, in which systems, circuits, and devices are co-designed to improve performance and address critical development challenges.
Non-volatile memory (NVM) devices show strong potential for enabling energy-efficient, massively parallel computing, owing to their CMOS-compatible operating voltages and analogue behaviour. Incorporating such emerging memory technologies at the back-end-of-line (BEOL) of CMOS circuits or within 3D array configurations opens significant opportunities for next-generation memory and computing systems. Realising this potential, however, requires addressing a set of critical obstacles: device variability, endurance limitations, fabrication compatibility, scalability, adequate compact models supporting circuit design, and system-level integration. These challenges are further compounded as SRAM and embedded DRAM scaling approach fundamental limits, increasing the relevance of Compute-In-Memory approaches and the emerging memory devices that underpin them.
This presentation highlights how DTCO and STCO provide the appropriate framework for navigating this complexity. By co-designing devices, circuits, and architectures in a holistic manner, and exploiting extended regimes of device behaviour, including low-power operation at low voltages, these approaches are of key importance for successfully integrating emerging memory technologies with CMOS circuits, enabling next-generation systems built on BEOL and 3D integration.
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