All Years Seminars
[INGI] 2026-02-19 (13:00) : Why AI is eating the planet.
At BARB94
Speaker :
Romain Rouvoy (Full Professor within the Faculty of Science and Technology at the University of Lille and the head of the Computer Science department)
Abstract : This presentation aims to raise awareness of the environmental costs of our personal and professional activities, which are increasingly assisted by AI agents. In particular, throughout this presentation, we aim to provide greater transparency into the conditions under which these tools are deployed, enabling a more accurate estimate of the consumption attributable to their use. To guide this approach, we study the production of a line of source code as a functional unit, enabling us to approximate the cost of an AI assistant's impact across the many infrastructure layers involved in its usage phase.
[ELEN] 2026-02-17 (11:00) : Noise feedback in circuits and application to the design of a noise-based amplifier
At Nyquist
Speaker :
Bertrand Reulet (Université de Sherbrooke)
Abstract : Electronics is based on the idea that the current-voltage characteristics of a component are intrinsic and do not depend on the circuit the component is embedded into. This idea makes it easy to simulate and build circuits based on the characteristics of the individual components. We show that this is incorrect, because of noise feedback. The effect of the circuit is to transform the current fluctuations generated by each component into voltage fluctuations across itself. This feedback mechanism leads to changes in the current-voltage characteristics. We provide a simple theory to describes this as well as an experiment on an avalanche diode to confirm it. Then we build an audio amplifier where the noise feedback mechanism leads to a region with negative resistance in a Zener diode, which we use to build an audio amplifier.
[INMA] 2026-02-17 (14:00) : Newcomers Seminar
At Euler building (room A.002)
Section 1:Fleet Rebalancing: Scalable Feedback Optimization for Autonomous Mobility-on-Demand via null-space projection
Speaker :
Arthur Mélot (UCLouvain)
Abstract : While state-of-the-art Model Predictive Control (MPC) approaches for Autonomous Mobility-on-Demand (AMoD) achieve scalability through flow-based modeling , they remain computationally burdened by iterative solvers , the need for a pre-computed static equilibrium to track and also a non-negligible prediction horizon to ensure stability. This presentation introduces a horizon-free feedback optimization algorithm that also eliminates the need for offline equilibrium computation. By treating the economic cost minimization as a dynamic feedback process, our approach steers the fleet directly toward the optimal operating point in real-time and does not track a pre-computed reference signal. The preliminary results provide a negligible execution time, outperforming the execution time of standard MPC. This project is still in progress and all the results provided are preliminary results.
Section 2:Mechanical determinants of tactile perception in the human fingertip
Speaker :
Viktoriia Kozadaeva (UCLouvain)
Abstract : Human tactile perception enables discrimination of features with microscale precision. This remarkable sensitivity arises from dense innervation of mechanoreceptors within the fingertip that transduce small skin deformations into neural signals. While neural innervation of the fingertip has been widely studied, the mechanical mechanisms enabling the microscale sensations remain underexplored. My research project aims to establish a relationship between psychophysical performance and mechanical determinants underlying microscale tactile perception. Specifically, we examine how fingerpad deformation relates to perceptual detection of small surface features.
Section 3:Reinforcement Learning for Petri-Net based Discrete Event Systems: Application to Aircraft Maintenance Scheduling
Speaker :
Margot Devillers (UCLouvain)
Abstract : Efficient aircraft maintenance scheduling is critical to maximizing fleet availability while ensuring regulatory compliance and cost efficiency. The problem involves grouping periodic tasks into maintenance projects and deciding when to ground aircraft under strict deadline and resource constraints. The combinatorial nature of these decisions makes classical optimization approaches challenging at fleet scale.
Building on a Colored Petri Net (CPN) digital twin of the aircraft maintenance operations, this work formulates the scheduling process as a discrete-event dynamical system and investigates the use of Reinforcement Learning (RL) to address it. An RL agent is currently being developed to interact with the CPN environment and learn fleet-level policies that balance aircraft utilization and operational feasibility over a finite planning horizon.
[INGI] 2026-02-12 (10:45) : Data Analytics : Data-driven consumer centric services
At BARB 94
Speaker :
Christophe Robyns (Agilytic)
Abstract : Massive internal and external data are available to better understand customer sentiment and anticipate or influence his behaviour. Having experience both in commercial and non-commercial organisations, the speaker will explain how data can be used to influence customer behaviour and enhance services. The speaker will also present steps that are required to build a big data platform that provides reliable and up-to-date information.
[ELEN] 2026-02-12 (13:00) : Mixed-signal neuromorphic circuits and systems for extreme-edge computing
At Nyquist
Speaker :
Dr. Ariana Rubino (ETH Zurich)
Abstract : Brain-inspired computing architectures offer a promising solution for
integrating Internet of Things (IoT) sensors with intelligent local
processing in edge computing applications. In particular, mixed-signal
event-based neuromorphic designs inherently meet key requirements of
edge computing: power efficiency, compactness, low latency, real-time
processing, and adaptability via on-chip learning.
Despite these advantages, several challenges remain. These include the
development of powerful and effective spike-based learning mechanisms,
the adoption of advanced Complementary metal-oxide-semiconductors (CMOS)
technology nodes, and the significant silicon area required to implement
neural dynamics with long time constants and synaptic plasticity.
In my talk, I will showcase the research conducted during my PhD to
advance the state of the art in mixed-signal neuromorphic processors for
edge computing. In particular, I will highlight the potential of
multi-compartment neuron models to enable dendritic spike-based learning
rules driven by target activities - an approach well suited to compact
and resource constrained edge devices. To support the development of
more compact and low-power systems, and to address the limitations of
scaled bulk-CMOS technologies, I will present our investigation into
Fully Depleted-Silicon on Insulator (FDSOI) transistor technology for
neuromorphic circuit design, demonstrating improved energy efficiency,
reduced mismatch, and better support for long synaptic dynamics.
Finally, to further optimize compactness at the synaptic array, I will
discuss emerging memory technologies, with a focus on energy-efficient
edge-compatible Ferroelectric Tunneling Junction (FTJ) memristive
synapses.
For each of these three aspects, I will present the mixed-signal
neuromorphic processors I designed and/or contributed to - ranging from
prototype test platforms to large-scale multi-core architectures - along
with both circuit-level simulation results and chip-level measurement
data.
Seminars
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