In 2025, the two guest lecturers are Po-Ling Loh and John C. Duchi.
The two invited lectures will be given by Mathilde Mougeot and Stéphane Robin.
Po-Ling Loh
University of CambridgeRobust statistics, Old and New
Po-Ling Loh is a Professor in the Statistical Laboratory within the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge, where she also serves as a Fellow of St. John's College. She earned a PhD in Statistics in 2014 from the University of California, Berkeley. Following her doctoral studies, she was an Assistant Professor in the Department of Statistics at the Wharton School of the University of Pennsylvania from 2014 to 2016. She then joined the University of Wisconsin–Madison, where she held joint appointments in the Departments of Electrical and Computer Engineering and Statistics, getting an Associate Professor position in 2020. She began her appointment in the University of Cambridge in 2021. Her research interests include high-dimensional statistics, robustness, optimization, network inference, neural networks and differential privacy.
Throughout her career, Po-Lin Loh has received numerous prestigious awards in recognition of her contributions to statistics and machine learning. She is a recipient of a Philip Leverhulme Prize, NSF CAREER Award, ARO Young Investigator Program Award, IMS Tweedie New Researcher Award, Bernoulli Society New Researcher Award, and Hertz Fellowship. She also received a best paper award at the Neural Information Processing Systems (NeurIPS) Conference in 2012 for her work on structure estimation in discrete graphical models, which has applications in social networks and epidemiology.
John C. Duchi
Stanford UniversityStatistical Perspectives on Privacy
John C. Duchi is an associate professor at Stanford University, holding appointments in the Departments of Statistics and Electrical Engineering, with a courtesy appointment in Computer Science. He earned his PhD in Electrical Engineering and Computer Sciences from the University of California, Berkeley, in 2014. His research interests encompass statistical learning, optimization, information theory, and computation. He focuses on developing statistical learning procedures that optimally balance resources such as computation, communication, and privacy, while maintaining statistical efficiency. Additionally, he aims to create efficient large-scale optimization methods and develop tools to assess and ensure the validity of machine-learned systems.
Throughout his career, John Duchi has received numerous awards and honors, including best paper awards at the Neural Information Processing Systems conference, the International Conference on Machine Learning, and the International Conference on Learning Theory. He has also been recognized with the Society for Industrial and Applied Mathematics (SIAM) Early Career Prize in Optimization, an Office of Naval Research (ONR) Young Investigator Award, an NSF CAREER award, a Sloan Fellowship in Mathematics, and the Okawa Foundation Award.
Stéphane Robin
Sorbonne UniversitéSome latent variable models in ecology
Stéphane Robin is a professor at Sorbonne Université, in the Laboratoire de Probabilités, Statistique et Modélisation (LPSM). Researcher in statistics, specialized in latent variable models, network models and change-point detection. Interested in their applications to life sciences, formerly genomics and genetics, and now mostly ecology.
Mathilde Mougeot
Université Paris-SaclayInterplay between data, physics and simulation models with a link to industrial use-cases
Mathilde Mougeot is a researcher and professor at the ‘École nationale supérieure d'informatique pour l'industrie et l'entreprise’ ENSIIE (National School of Computer Science for Industry and Business), and the Industrial data analytics and machine learning chairholder at the Centre Borelli (Université Paris-Saclay, CNRS, ENS Paris-Saclay, Université de Paris, SSA). Her unusual background in industry and academia gives her a dual set of skills which she uses for her research and teaching in data science.