Linda Mhalla

EPFL SB MATH
MA A1 417 (Bâtiment MA)
Station 8
1015 Lausanne

EPFL SB MATH
MA A1 417 (Bâtiment MA)
Station 8
1015 Lausanne

EPFL SB MATH
MA A1 417 (Bâtiment MA)
Station 8
1015 Lausanne

EPFL SB MATH
MA A1 417 (Bâtiment MA)
Station 8
1015 Lausanne

Expertise

Statistical theory and methods, with applications to environmental methods.
Statistical consulting.

Mission

My academic work aims to advance statistical science by developing and applying innovative approaches to data analysis, with expertise in risk and extreme value analysis, and by fostering collaborations both within and outside EPFL.

Selected publications

Causality and extremes

Linda Mhalla, Valérie Chavez-Demoulin
Published in Handbook on Statistics of Extremes in 2026

A Review of Applications of Extreme Value Theory to Environmental Risk Assessment

Linda Mhalla
Published in Environmental Modelling with Contemporary Statistics: Learning, Directionality, and Space-Time Dynamics in 2026

Causal Discovery in Multivariate Extremes: A Study of Swiss Hydrological Catchments

Linda Mhalla, Valérie Chavez-Demoulin, Philippe Naveau
Published in Environmetrics in 2025

Causal mechanism of extreme river discharges in the upper Danube basin network

Linda Mhalla
Published in Journal of the Royal Statistical Society: Series C (Applied Statistics) in 2020

Extremal connectedness and systemic risk of hedge funds

Linda Mhalla
Published in Journal of Applied Econometrics in 2020

Teaching & PhD

Courses

Applied statistics

MATH-516

The course will provide an overview of everyday challenges in applied statistics through case studies. Students will learn how to use core statistical methods and their extensions, and will use computational and problem-solving tools to provide reproducible solutions for the problems presented.

Bioinformatic Analysis of RNA-sequencing (Spring)

BIO-693(b)

This course will take place at EPFL in June 2026, from 8th to 12th, in room AAC 137. It introduces the workflows and techniques that are used for the analysis of bulk and single-cell RNA-seq data. It empowers students to understand and analyze their own data.

Probability and statistics

MATH-131

The course introduces the basic notions of probability and statistical inference, with an emphasis on the main concepts and the most commonly used methods.

Risk and environmental sustainability

MATH-383

This course gives an introduction to the assessment of risk with a particular focus on modelling of rare events, which can have huge environmental impacts.

Statistical computation and visualisation

MATH-517

The course will provide the opportunity to tackle real world problems requiring advanced computational skills and visualisation techniques to complement statistical thinking. Students will practice proposing efficient solutions, and effectively communicating the results with stakeholders.

Statistical consulting and collaborations

MATH-663

Analyzing data for a collaborator or client is very different from working on your own research project ; not only do you need competences in statistics, you must also ensure good communication (both ways) in a multi-disciplinary environment, coordination of the work, and the management of everyone.