日時：2024年7月23日(火) 10:00 ~　90分

場所：R545およびWeb併用　（※ 対象は特に限定しない）

講演者：Marco Scutari先生（Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Switzerland）

題目： Causal Modelling in Space and Time

概要：
The assumption that data are independent and identically distributed samples from a single underlying population is pervasive in statistical and machine learning modelling. However, most real-world data do not satisfy this assumption. Regression models have been extended to deal with structured data collected over time, space, and different populations. But what about causal network models, which often use regression for their local distributions? In this talk, I will discuss how to learn well-specified models with causal discovery in environmental sciences, epidemiology and other challenging domains that produce data with complex structures. I will focus on scalable and interpretable techniques, modelling the interplay between weather patterns, pollution, mental conditions and dermatologic problems as an example.

BIO:
Marco Scutari is a Senior Researcher at Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), one of Switzerland’s national AI research centres. Since completing his PhD in Statistics in 2011, he has held positions in Statistics Genetics (UCL), Statistics (Oxford), and Machine Learning (IDSIA) in the UK and Switzerland. His work focuses on the theory of Bayesian networks and their application to biological, environmental and clinical sciences, statistical computing, and software engineering for machine learning applications.

お問い合わせは以下のアドレスにメールでご連絡ください。
dser-center@biwako.shiga-u.ac.jp