Prof. Dr. Magda Gregorová

Projects

MSc. thesis

• Muhammad Zakriya Shah Sarawr: Step Detection (2023, THWS) (github repo)
• Marius Benkert: Cross Topology Modeling and Optimization of Electrical Drives Using Machine Learning (2023, THWS, 2nd examiner) (journal paper)
• Ana Muñoz Gutiérrez: Transfer Learning in Crop Remote Sensing (2023, THWS) (github repo)
• Alexander Lorz: Entwicklung eines neuronalen Netze zur gezielten Veränderung von Bildern (2022, THWS)
• Marc Desaules: Learned compression with VAEs applied to one dimensional signal (2021, University of Geneva)
• Frantzeska Lavda: A Study of Recurrent neural networks (RNNs) in univariate and multivariate time series (2016, University of Geneva, pdf)

Publications

• DBLP
• Google Scholar

Vita

Magda Gregorova comes from Prague, Czech Republic, where she obtained her Master‘s degree in Statistics (2001) from the University of Economics. She started her career as an applied statistician in the Czech National Bank, where she headed a technical unit on financial statistics and collaborated closely with the ECB and the IMF. After several years in banking she has decided to follow an international career and joined Eurocontrol, the European Organization for the Safety of Air Navigation based in Brussels, Belgium, as a statistical analyst and forecaster. She then moved to Geneva, Switzerland, where she obtained in 2018 a PhD in machine learning from the Computer Science Department of the University of Geneva. She continued as a post-doc in the Data Mining and Machine Learning group of the University of Applied Sciences of Western Switzerland. 

In 2021 Magda has moved to Germany, where she obtained the research professorship for “Representation and Learning in Artificial Intelligence” at the Faculty of Computer Science and Business Information Systems of the Technical University of Applied Sciences Würzburg-Schweinfurt (THWS). She is a founding member of the THWS research Center for Artificial Intelligence (CAIRO) which she has led from its beginnings in 2022 till mid 2024. Her teaching activities are mainly within the international masters on AI in the areas of deep learning and generative modelling. In her research she focuses on deep unsupervised learning methods for modelling complex high-dimensional distributions and data representations for downstream tasks (google scholar). In addition to her own research she regularly contributes to the machine learning community through reviewing service (ICML, ICLR, NeurIPS, etc.) and by active participation in outreach and educational events such as IK.

Academic service

Boards

• FIW, THWS - member of the faculty board
• Faculty of Informatics and Statistics, University of Economics and Business, Prague, Czech Republic - member of the scientific board

Reviewing

• ECML-PKDD (2022)
• ICLR (2022, 2023)
• ICML (2021, 2022, 2023)
• MCPR (2021, 2022)
• NeurIPS (2021, 2022)
• TMLR (since 2022)
• FNR Core Program, Luxembourg (2021, 2022, 2023)