Linda Mhalla

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linda.mhalla@epfl.ch +41 21 693 74 43 https://lindamhalla.netlify.app

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

+41 21 693 74 43
Office:  MA A1 417
EPFL > SB > MATH > MATH-GE

Web site:  Web site:  https://math.epfl.ch/

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

+41 21 693 74 43
Office:  MA A1 417
EPFL > SB > SB-SMA > SMA-ENS

Web site:  Web site:  https://sma.epfl.ch/

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

+41 21 693 74 43
Office:  CM 1 618
EPFL > VPA-AVP-DLE > AVP-DLE-EDOC > EDMA-ENS

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

+41 21 693 74 43
Office:  CM 1 618
EPFL > VPA-AVP-DLE > AVP-DLE-EDOC > EDMS-ENS

Web site:  Web site:  https://go.epfl.ch/edms

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Administrative data

Teaching & PhD

Teaching

Mathematics

Courses

Bioinformatic Analysis of RNA-sequencing (Fall)

Bioinformatic Analysis of RNA-sequencing (Spring)

(Coursebook not yet approved by the section)

Probability and statistics

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

Motivation: environmental risks and catastrophes. Elements of statistical modelling. Elements of risk analysis. Probability models for rare events: Poisson process and its properties; extremal distributions and extremal paradigm. Statistical application of rare event models in environmental settings: analysis of block maxima, threshold exceedances, Poisson models.  R

Applied statistics

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.

Statistical computation and visualisation

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

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.