Lecture by Prof. Alessandro Laio at the Faculty of Chemistry UWr
On 7–9 May 2025, the Faculty of Chemistry at the University of Wrocław will host Prof. Alessandro Laio from the Theoretical and Scientific Data Science Group at the Scuola Internazionale Superiore di Studi Avanzati (SISSA) in Trieste, Italy.
As part of the visit, on 8 May (Thursday) at 10:15 a.m. in lecture hall 145 at the Faculty of Chemistry, ul. F. Joliot-Curie 14 in Wrocław, Prof. Laio will give an open lecture entitled ‘Identifying informative distance measures in high-dimensional feature spaces’.
The visit is part of the short-term visiting professor programme implemented under the IDUB initiative.
About lecture
Real-world data in biochemistry, material science and beyond typically contain a large number of features that are often heterogeneous in nature, relevance, and also units of measure. When assessing the similarity between data points, one can build various distance measures using subsets of these features. Finding a small set of features that still retains sufficient information about the dataset is important for the successful application of many statistical learning approaches.
We introduce a statistical test that can assess the relative information retained when using two different distance measures, and determine if they are equivalent, independent, or if one is more informative than the other. This test can be used to identify the most informative distance measure and, therefore, the most informative set of features, out of a pool of candidates. The approach can be used to identify the most appropriate set of collective variables in molecular systems and to infer causality in high-dimensional dynamic processes and time series.
Date of publication: 28.04.2025
Added by: M.K.
