29-30/10/2026 - 9:30 - Room D251
 

Margaux Zaffran 

(Laboratoire de Mathématiques d’Orsay) 

The course will be held at ISBA, UCLouvain (54 Grand Rue, Louvain-la-Neuve) on:

Thursday October, 29th, 2026, 9h30-12h30 and 13h30-15h (Room D251);
Friday October, 30th, 2026, 9h30-12h30 and 13h30-15h (Room D251).

To better organize the event, may we kindly ask you to register (before October 19th at 17h; the sooner the better, so that we can reserve a larger room in case needed and the catering) using the following link:
Workshop EDT on Conformal Prediction – Remplir le formulaire

For those travelling by car, if you need parking please let us know at least one week in advance (it takes at least a couple of days to get a QR code from the central administration). For those in need of a certificate of attendance, please let us know as well in advance.

More details and information regarding the event will follow.
Nevertheless, you are all warmly invited to already check out some slides the speaker prepared for a similar event at SFdS (on which our short course is based) here:
https://github.com/mzaffran/ECAS_SFdS_ConformalPrediction

Kind regards,
EDT in Statistics, Biostatistics and Actuarial Sciences

More details coming soon

27/11/2026 - 11:00 - ISBA C115 
 

Ivan Kojadinovic  

(Université de Pau et des Pays de l'Adour) 

Will give a presentation on : 

An algorithmic procedure for solving the generalized minimum information checkerboard copula problem

Abstract: 

The minimum information copula principle (see Meeuwissen and Bedford, 1997) is a maximum entropy-like approach for finding the least informative copula that satisfies a certain number of expectation constraints specified either from expert knowledge or the available limited data. In this presentation, we first propose a generalization of this principle allowing the inclusion of additional constraints fixing certain higher-order margins of the copula. We next prove that the associated optimization problem has a unique solution under a natural condition. As the latter problem is intractable in general, following the existing literature, we consider its version with all the probability measures involved in its formulation replaced by checkerboard approximations. This amounts to attempting to solve a
so-called discrete I-projection linear problem. We then use the seminal results of Csiszar (1975) to derive an IPFP-like procedure for solving the latter and provide theoretical guarantees for its convergence. We next report numerical experiments in dimensions up to four with substantially finer discretizations than those encountered in the literature. We finally discuss the computational cost of the procedure and hint at memory saving implementations that should allow to tackle higher dimensional problems under certain conditions.

More details coming soon

11/12/2026 - 14:30 - ISBA C115 
 

Alessandra Luati  

(Imperial College London) 

More details coming soon

13/11/2026 - 14:30 - ISBA C.115
 

Kellie Archer 

(The Ohio State University) 

Will give a presentation on : 

High-Dimensional Mixture Cure Models for Time-to-Event Data 

Abstract: 

Medical breakthroughs in recent decades have led to cures for many diseases including cancer.  For example, various groups have shown that advances in therapy for leukemia and myelodysplastic syndrome have increased overall survival rates and some groups have identified factors related to long-term survival, defined as survival exceeding three years, which included that patients treated on newer treatment regimens were more likely to be long-term survivors. In fact, some argue that these improved outcomes indicate that some AML patients can be considered “potentially cured.” The mixture cure model (MCM) is a time-to-event model that is used when a cured fraction exists. MCMs assume the population consists of two subgroups, those cured will not experience the event of interest and those susceptible to the event of interest. Therefore, “cured” can be considered synonymous with attaining long-term relapse-free survival. Thus, there are two regression components in MCMs which permit identification of features associated with cure and/or latency of susceptible patients. Many researchers have sought to identify a prognostic model for a time-to-event outcome. When the covariate space is high-dimensional, as in the case with gene expression data, typically penalized Cox proportional hazards (PH) models are fit. However, when a dataset includes a cured fraction, MCMs are more appropriate than Cox PH models. In these scenarios, Cox models often yield inaccurate hazard and survival estimates due to violations of the proportional hazards assumption. Despite their utility, robust methods for fitting MCMs to high-dimensional data remain scarce. In this talk, I will present our recent work extending MCMs to effectively handle high-dimensional covariate spaces, offering a more accurate prognostic tool for modern high-dimensional datasets.

26/02/2027 - 14:30 - ISBA C115 
 

Debarghya Ghoshdastidar

(Technical University of Munich) 

More details coming soon

05/03/2027 - 14:30 - ISBA C115 
 

Emilie Devijver

(Université Grenoble Alpes) 

More details coming soon

06/11/2026 - 14:30 - ISBA C.115 + Teams
 

Sigrid Knops & Matthias Mertens

(CSR,Climate director & Data Engineer, Korian Belgium)

Will give a presentation on : 

Data-smart Nursing Homes: Using Data for Well-being and Sustainability

Summary:

How can data help nursing homes use energy and water more efficiently while maintaining residents’ and employee’s well-being?

At Korian, a large network of elderly care homes, smart meters and indoor sensors provide opportunities to explore this question. Their value, however, depends on the reliability of the measurements and our ability to interpret them in an operational context.

In this talk, we share our experience of collecting and using this data.  During the academical year 2025-2026, we launched some student projects on electricity/gas/water consumption and solar panel production/injection. Students have investigated missing measurements, anomalous readings and consumption patterns across a sample of Korian’s elderly care homes. A complementary case study combined indoor temperatures, weather data and gas consumption to examine thermal behavior and identified opportunities for further investigation and optimization.

The projects illustrated both the possibilities and the limitations of data science in practice. Filling a gap does not necessarily resolve an underlying sensor problem, and a statistical pattern needs operational context before it can lead to a decision.

The seminar opens a discussion on the next steps. How can we improve the existing ideas and obtain a practical approach which leads to lower resource use and lower emissions while still preserving a comfortable living environment?
 

Participation via Teams is also possible via the link https://teams.microsoft.com/meet/3554464068012969?p=fm7poiGgyUWzXWb34b