All Years Seminars
[INGI] 2026-03-12 (13:00) : When AI co-designs and implements a programming language : the case of Elo
At Shannon, Maxwell a.105
Speaker :
Bernard Lambeau (Klaro Cards)
Abstract : I launched Claude Code on the design and implementation of the Elo data expression language the 24th of December. By the 1st of January, the compiler, website and documentation were ready for pre-production. I did not touch a single line of code, yet a week later I was confident enough embedding Elo in production systems. In this talk I'll share my motivation for creating Elo, the methodology I used with Claude Code, and lessons learned about integrating AI in Software Engineering processes in my companies.
[INMA] 2026-03-10 (14:00) : From Learning to Optimize to Learning Optimization Algorithms
At Euler building (room A.002)
Speaker :
Camille Castera (University of Bordeaux)
Abstract : Towards designing learned optimization algorithms that are usable beyond their training setting, we identify key principles that classical algorithms obey, but have up to now, not been used for Learning to Optimize (L2O). Following these principles, we provide a general design pipeline, taking into account data, architecture and learning strategy, and thereby enabling a synergy between classical optimization and L2O, resulting in a philosophy of Learning Optimization Algorithms. As a consequence our learned algorithms perform well far beyond problems from the training distribution. We demonstrate the success of these novel principles by designing a new learning-enhanced BFGS algorithm and provide numerical experiments evidencing its adaptation to different settings at test time.
[INGI] 2026-03-09 (13:00) : Development of digital twins of reference softwares for testing/certification in realistic environment.
At BARB 94
Speaker :
Ahmed Bokri (ERM)
Abstract : Security certification of detection systems requires testing environments that are realistic but also fully controlled. This presentation describes the development of a digital twin of the Multi-Agent System for APT Detection (MASFAD), deployed on the KYPO Cyber Range to support structured testing and certification activities.
The digital twin reproduces the software architecture of MASFAD and the environment in which it operates. It includes a realistic enterprise-like infrastructure, background traffic generation, and controlled APT-inspired persistence scenarios. All system and security events are centrally collected through the Elastic Stack to ensure clear visibility of system activity and detection results.
The platform ensures reproducibility, traceability, and controlled evidence generation. Experiments can be repeated under the same conditions, and the results can be verified. This work shows how a digital twin can be used as a practical and structured environment for evaluating detection capabilities in view of formal security certification.
[INGI] 2026-03-05 (13:00) : xPUBench: Scalable and Energy-Efficient GPU and DPU-Accelerated Network Functions
At Nyquist Maxwell a.164
Speaker :
Maxime Vanliefde (ICTEAM)
Abstract : The rapid increase in network speeds makes packet processing on general-purpose CPUs increasingly challenging. At 100 Gbps and beyond, CPUs struggle to sustain complex network functions without dedicated acceleration. This trend motivates the exploration and measurement of alternative compute platforms such as GPUs and embedded CPUs in Network Interface Cards (NICs). Modern NICs provide tighter integration with GPUs, with the ability to write received packets directly to GPU memory. SmartNICs, also known as DPUs, further feature embedded ARM or RISC cores capable of offloading NFV packet processing entirely. In this work, we introduce xPUBench, a benchmarking environment that systematically measures the performance and energy efficiency of packet processing across CPUs, GPUs, and DPUs. We evaluate several (co-)processing models relevant to Network Function Virtualization, including CPU+GPU hybrid, DPU-only, and GPU-only approaches. Our measurements show that, for a computation-heavy workload, current CPU-only implementations manage to handle up to 50% of the 100 Gbps NIC rate. In contrast, GPU implementations can saturate it. We also show that SmartNICs’ most powerful embedded cores can replace the main CPU for some traditional packet processing, alleviating the load on the host, which can now be entirely dedicated to running applications. We finally propose a novel energy-efficiency dimension, showing that DPUs outperform traditional CPUs for low-throughput processing, requiring only 24 W to sustain 10 Gbps, and that GPUs outperform CPUs for high-throughput processing. Our findings emphasize the need to assess both performance and energy in heterogeneous packet-processing pipelines, given the growing diversity of “xPUs” in networked systems.
[INMA] 2026-03-03 (14:00) : Consensus is a myth: Human label variation in Natural Language Inference
At Euler building (room A.002)
Speaker :
Marie-Catherine de Marneffe (CENTAL, UCLouvain)
Abstract : Recently, NLP researchers have increasingly begun to acknowledge that humans often diverge in their interpretations of various NLP tasks, and that such variation should be captured if robust language understanding is to be achieved. In this talk, I will focus on analyzing human label variation in the Natural Language Inference (NLI) task, in which, given a premise, one identifies whether a hypothesis sentence is true, false, or undetermined. For instance, if one says: “My friend often travels with a heavy suitcase”, can it be inferred that “My friend often travels with a light suitcase”? I will examine the various sources of NLI label variation and investigate whether or not they can be captured by current LLMs, arguing that, in the presence of variation, labels without explanations are not sufficiently meaningful.
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