Make it (net)work! - MLA2S Networking Seminar #10

Tuesday 9 Jun 2026, 13:00 → 16:55 Europe/Vienna

10th Network Seminar of the Thematic Platform MLA²S. 
Seminarrooms 2 and 3 (formerly 1 and 2, also known as 3A.1 and 3A.2), PSK 3rd floor

 

 

To register: click "register" below here. 

13:00 → 13:15 News from MLA2S and Make it (net)work!

Including a short round table news update.

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

13:15 → 13:45 Learning to See the Unseen: Data-Driven Regularization for Inverse Problems

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.

Speaker: Cong Shi (RICAM)

13:45 → 14:00 Discussion

14:00 → 14:15 Break to walk a few steps and get some fresh air in.

14:15 → 14:25 Introduction to VID

Speaker: Bernhard Rengs (VID)

14:25 → 14:50 Machine Learning for Economic Demography: Bayesian Approach to Analysing Ageing and Productivity

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.

Speaker: Isabel Gerstner (VID)

14:50 → 15:00 Discussion

15:00 → 16:00 Networking with refreshments / further discussions