2026's Seminars
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[INMA] 2026-09-01 (14:00) : Exploring the Impact of Memory on Network Controllability
At Euler building (room A.207)
Speaker :
Marco Peruzzo (University of Padova)
Abstract : Many natural and engineered dynamical systems can be modeled as networks consisting of a large number of interconnected, simpler dynamical units. Over the last decade, the challenge of efficiently controlling large-scale networks has driven renewed interest in the control community. Several strategies for guaranteeing network controllability have been developed, such as the optimal selection of driver nodes and the implementation of minimal changes to the network topology.
In this talk, we explore a different strategy for achieving controllability, motivated by networks whose topology cannot be easily modified, such as traffic and water-flow networks. Inspired by lifted Markov chains, we enlarge the local state space of selected network nodes, introducing memory and directionality into their dynamics while leaving the network topology unchanged. We show that this strategy can reduce the worst-case control energy by a factor exponential in the network size.
We further discuss how our strategy can render otherwise uncontrollable networks structurally controllable (i.e., controllable for almost all choices of the network parameters). We characterize networks for which different types of dynamics modifications are required and show that our approach can require significantly fewer subsystem modifications than alternative local dynamics modification strategies.
[INMA] 2026-07-16 (10:00) : Path-conditioned training: a principled way to rescale ReLU neural networks
At Euler building (room A.002)
Speaker :
Titouan Vayer (COMPACT, Inria team Irisa, Rennes, France)
Abstract : Despite recent algorithmic advances, we still lack principled ways to leverage the well-documented rescaling symmetries in ReLU neural network parameters. While two properly rescaled weights implement the same function, the training dynamics can be dramatically different. To offer a fresh perspective on exploiting this phenomenon, we build on the recent path-lifting framework, which provides a compact factorization of ReLU networks. We introduce a geometrically motivated criterion to rescale neural network parameters which minimization leads to a conditioning strategy that aligns a kernel in the path-lifting space with a chosen reference. We derive an efficient algorithm to perform this alignment. In the context of random network initialization, we analyze how the architecture and the initialization scale jointly impact the output of the proposed method. Numerical experiments illustrate its potential to speed up training.
[INGI] 2026-07-02 (13:00) : 2 INGI seminars in one (J. Vanderhaeghen & A. Fierens)
At Shannon (Maxwell - a.105)
Speaker :
Amaury Fierens (ICTEAM)
,
and
Juliette Vanderhaeghen (ICTEAM)
Abstract : By Juliette Vanderhaeghen:
Towards Personalized Tumor Growth Modeling with Physics-Informed Neural Networks
Abstract:
Cancer remains a major global health challenge, highlighting the urgent need for more personalized treatment strategies. Mathematical models, in particular PDE-based approaches, provide a powerful framework to describe tumor dynamics. However, classical numerical methods can be computationally expensive and require careful calibration, while purely data-driven models often lack interpretability and demand large amounts of data.
Physics-Informed Neural Networks (PINNs) offer a promising alternative by embedding physical laws directly into the training process, enabling both efficient simulations and the inference of patient-specific parameters from limited data.
In this presentation, I present our current work on the use of PINNs to model tumor growth dynamics. First, I will demonstrate how PINNs can reconstruct tumor evolution and estimate key parameters from partial synthetic observations. Then I will present their application to multicellular tumor spheroids as a controlled experimental system. These spheroids generate spatially and temporally resolved data under varying treatment conditions, making them a valuable testbed for model validation.
By Amaury Fierens:
Self-Alignment and Reranking for Large-Scale Medical Coding in Open-Source Clinical NLP
Abstract:
Medical coding is a challenging large-scale linking problem: clinical mentions must be mapped to highly ambiguous terminologies containing millions of possible codes, such as UMLS. While large language models are increasingly used in clinical NLP, they are not yet a fully satisfactory solution for this task, especially when reliability, traceability, terminology coverage, and precise code selection are required.
The presentation is structured in three parts. First, I will introduce the complexity of medical coding and why it remains difficult for both automated systems and clinicians. Second, I qill discuss an effective technical approach based on self-alignment pretraining with contrastive learning, as in SapBERT, combined with candidate reranking through cross-encoders, rule-based signals, and hierarchical information. Finally, drawing on the open-source work carried out this year by two master’s students, I will discuss how effective NLP components can be integrated into practical tools that support medical annotation and coding workflows.
Towards Personalized Tumor Growth Modeling with Physics-Informed Neural Networks
Abstract:
Cancer remains a major global health challenge, highlighting the urgent need for more personalized treatment strategies. Mathematical models, in particular PDE-based approaches, provide a powerful framework to describe tumor dynamics. However, classical numerical methods can be computationally expensive and require careful calibration, while purely data-driven models often lack interpretability and demand large amounts of data.
Physics-Informed Neural Networks (PINNs) offer a promising alternative by embedding physical laws directly into the training process, enabling both efficient simulations and the inference of patient-specific parameters from limited data.
In this presentation, I present our current work on the use of PINNs to model tumor growth dynamics. First, I will demonstrate how PINNs can reconstruct tumor evolution and estimate key parameters from partial synthetic observations. Then I will present their application to multicellular tumor spheroids as a controlled experimental system. These spheroids generate spatially and temporally resolved data under varying treatment conditions, making them a valuable testbed for model validation.
By Amaury Fierens:
Self-Alignment and Reranking for Large-Scale Medical Coding in Open-Source Clinical NLP
Abstract:
Medical coding is a challenging large-scale linking problem: clinical mentions must be mapped to highly ambiguous terminologies containing millions of possible codes, such as UMLS. While large language models are increasingly used in clinical NLP, they are not yet a fully satisfactory solution for this task, especially when reliability, traceability, terminology coverage, and precise code selection are required.
The presentation is structured in three parts. First, I will introduce the complexity of medical coding and why it remains difficult for both automated systems and clinicians. Second, I qill discuss an effective technical approach based on self-alignment pretraining with contrastive learning, as in SapBERT, combined with candidate reranking through cross-encoders, rule-based signals, and hierarchical information. Finally, drawing on the open-source work carried out this year by two master’s students, I will discuss how effective NLP components can be integrated into practical tools that support medical annotation and coding workflows.
[INMA] 2026-07-02 (10:00) : A two-level inexact smoothing framework for nonsmooth optimization
At Euler building (room A.002)
Speaker :
Masoud Ahookhosh (University of Antwerp)
Abstract : We introduce an inexact two-level optimization framework, ItsOPT, for computing first- and second-order critical points of nonsmooth and nonconvex optimization problems. The framework consists of two interconnected levels. At the upper level, a smoothing technique—such as the high-order Moreau envelope, high-order forward-backward envelope, or high-order tensor envelope—is employed to construct a smooth approximation of the original objective function while preserving its minimizers. First- or second-order optimization methods are then applied to minimize the resulting smooth surrogate. At the lower level, the associated high-order proximal subproblems (e.g., high-order proximal, forward-backward, or tensor subproblems) are solved inexactly using subgradient-based or Bregman proximal methods. The resulting approximate solutions provide inexact evaluations of the smoothing function and its derivative information, which are subsequently used by the upper-level optimization methods. The overall complexity of the proposed framework is given by the product of the computational complexities of the upper- and lower-level procedures. By combining accelerated first- or second-order methods at the upper level with lower-level algorithms whose complexity is negligible (e.g., logarithmic in the desired accuracy), the resulting methods may achieve overall iteration complexities that improve upon existing worst-case complexity bounds. Finally, we present several concrete algorithms within the proposed framework and report preliminary numerical results demonstrating their practical performance.
[ELEN] 2026-06-18 (11:00) : Interfacing molecules with silicon metamaterials for sensing applications
At Shannon
Speaker :
Michael J. Sailor (University of California)
Abstract : Combining the functions of biologics or reporter molecules within microelectronic systems is a growing discipline, with a wide range of applications in sensing, healthcare, robotics, and human factors engineering. There are many examples in which native or near-native proteins have been integrated into devices (e.g., glucose sensors, antigen detection kits, DNA sequencing devices, etc.), and some examples of engineered proteins that can function in non-native media or in more challenging environments. There are needs both for new molecules and for new materials that can enable hybrid optical sensor systems with enhanced performance in terms of sensitivity, selectivity, or long-term stability. Mesoporous silicon, generated by anodization of single-crystal silicon wafers, provides a means to integrate potential reporter molecules (indicator dyes, fluorescent proteins, enzymes, etc.) within nanoscale silicon metamaterials (such as photonic crystals) to achieve such enhanced performance. The effects of modulating mesopore dimensions, surface charge, and surface chemistry on performance characteristics such as enzyme turnover rates, ingress/egress of reactants/products, and enhanced optical responses will be discussed.
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