Time table of all LIRMM seminars

We investigate the short-term production planning of green hydrogen obtained by water electrolysis using electricity from a wind power source and a connection to the national electricity grid. Electricity consumption on the grid has to be declared a day before production and cannot be adjusted afterwards, while the future availability of the wind power source is uncertain. This production problem can be viewed as a two-stage stochastic lot-sizing model, and a consistent framework is introduced to solve it efficiently. First, the innovative use of a variational auto-encoder to estimate the conditional uncertainty of wind power and generate scenarios is studied. Next, a time-efficient Benders decomposition approach is proposed, in which the special features of our problem are exploited to speed up its solving. Finally, a new application of an adaptive partition-based approach and a stabilization method further improve the solution time of the decomposition scheme. A realistic simulation demonstrates the advantages of the framework presented.

(Joint work with Céline Gicquel and Victor Spitzer)

A $q$-colorful graph $(G, f)$ is a graph $G$ together with a vertex-coloring function $f: V(G) \to \{1, \ldots, q\}$. Such graphs naturally capture a wide range of algorithmic problems on graphs involving distinguished sets of terminals. The concept of graph minors extends in a natural way to colorful graphs. However, to understand the complexity landscape of related problems, especially in the presence of terminals, a structural theory of colorful minors is essential. In this talk, we present three structural theorems concerning the exclusion of specific colorful graphs as minors. These are:

1. the rainbow clique — a clique in which each vertex is assigned all colors,

2. the rainbow grid — a grid in which every vertex carries all colors, and

3. segregated grids — all grids with all colors appearing segregated and in sequence on the outer face.

Our results yield a complete answer to the question of when the Erdős–Pósa property holds for colorful minors, for any possible number of colors. Beyond this, they lead to new meta-algorithmic results for graph problems with terminal constraints—problems that fall outside the reach of existing algorithmic frameworks.

Joint work with Evangelos Protopapas and Sebastian Wiederrecht

Lorna M. Lopez, PhD, is Professor of Human Genetics in the Department of Biology and the Kathleen Lonsdale Human Health Institute at Maynooth University. Her research mission is to generate knowledge, connections and expertise on the role of genetics in neuropsychiatric and neurodevelopmental conditions. She leads the FamilyGenomics research group and is Principal Investigator on internationally funded projects, currently supported by the European Research Council (ERC) and the Wellcome Trust. She is also a co-investigator with FutureNeuro, the SFI Research Centre for Translational Brain Science, where her work explores computational mental health genomics and chronobiology. This also includes the use of large-scale biomedical resources such as UK Biobank, ABCD, the Australian Autism Biobank. A committed advocate for engaged and responsible genomic research, Lorna is currently visiting LIRMM, University of Montpellier, as a recipient of a France Excellence Research Residency 2025, awarded by the Ambassade de France en Irlande. Lorna will present her research on the role of genetics in neuropsychiatric and neurodevelopmental conditions, with a particular focus on the chronobiology of mental health.