Lea Multerer
From epidemiology to machine learning: models to interpret complexity
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Lea Multerer is a researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI), in the scientific area of machine learning. With a background in mathematics and a PhD in epidemiology, she develops robust methods for the analysis of complex data, with applications in the health and finance sectors.
Where do you come from, what did you study, and what are you currently working on?
I grew up in the Canton of Bern and studied Mathematics in Basel. Towards the end of my Master thesis, I came across a PhD position in trial design for Malaria studies at the Swiss Tropical and Public Health Institute in Basel. I was not looking to leave Mathematics, but the topic fascinated me so much that I took this applied path, finishing with a PhD in Epidemiology during the COVID-19 pandemic. This opened the door to work at the Federal Office of Public Health for the surveillance of COVID-19 in Switzerland, a very intense phase of my career.
I then joined the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI), where I focus on machine learning methods and their application to problems in health and finance.
What topics does your research focus on, and what excites you most about your work?
I would describe my research as very interdisciplinary, which is also what excites me most about it. A key experience for me was working with COVID-19 surveillance data, where I saw how challenging it is to obtain and curate meaningful data in this domain. In recent years, I have focused on developing machine learning methods that can still perform well when data is limited and noisy, for example through approaches like physics-informed machine learning. At the same time, I’m very interested in using these methods to answer causal questions from the data.
Is there a particular experience, personal or professional, that has shaped your journey?
Thinking back, I think what most shaped my journey was studying mathematical proofs. It taught me to work very carefully and pay attention to details, at the same time it requires a lot of creativity. Also, it gave me a high frustration tolerance, which still helps me today in my research.
What is the added value of working at SUPSI and within the Department of Innovative Technologies?
I really enjoy doing research in a team. At SUPSI, I have the privilege to work with extremely talented colleagues from different backgrounds. We often work on applied projects but have the freedom to pursue methodologically innovative work.