News from MLA2S and Make it (net)work!

• Claudius Krause (MBI Vienna (ÖAW))
• Nicki Holighaus (Acoustics Research Institute, Austrian Academy of Sciences)
• Claus Trost (Erich Schmid Institute of Materials Science of theAustrian Academy of Sciences)
• Kati Heinrich (IGF | ÖAW)
• Jan Odstrčilík (IMAFO)

Including a short round table news update.

Learning to See the Unseen: Data-Driven Regularization for Inverse Problems

• Cong Shi (RICAM)

This talk focuses on the analysis of regularization methods for solving nonlinear inverse problems – problems that aim to uncover the inner mechanisms of an observed system based on indirect or incomplete measurements. Specifically, we place strong emphasis on techniques that incorporate supervised or unsupervised data derived from prior experiments. This approach enables the integration of data-driven insights into the solution of inverse problems governed by physical models. The talk will bridge classical functional analysis with modern machine learning, with applications across different medical imaging modalities where identifying hidden parameters is essential.

Discussion

Introduction to VID

• Bernhard Rengs (VID)

Machine Learning for Economic Demography: Bayesian Approach to Analysing Ageing and Productivity

• Isabel Gerstner (VID)

This talk gives an insight into the application of Machine Learning methods in demographic research. As many societies are ageing rapidly, the question of economic competitiveness and labour market resilience is gaining significant relevance. Using Austrian register data in the LEDA (Linked Employer- Employee Data Analysis), we study how workers´ age composition is related to firm-level productivity. This data captures almost all firms operating and all individuals residing in Austria, therefore covering firms and individuals that may otherwise be underrepresented in surveys. One major drawback inherent to this otherwise outstandingly rich data source, is the lack of capital stock. While capital, together with labour, is one of the most important input factors in production, it is mostly estimated with error in empirical economic research. We acknowledge this reality by modelling this uncertainty using Bayesian methods and incorporating prior beliefs based on theoretical and empirical knowledge. By estimating the relationship "productivity - capital" more realistically, we are in turn able to analyse the relationship "productivity - ageing" more accurately.

Discussion

Networking with refreshments / further discussions