Lecturers

Projects

I'm a member of CAIRO, Center for Artifificial Intelligence. 

Projects:

• ML-EMPerform: Machine learning surrogate modeling of electrical motor performance (2025-26). This project aims to develop deep learning (DL) methods for predicting the performance of EM bypassing the FEM-based simulation methods. The surrogate DL methods shall be significantly faster than the FEM-based simulations allowing for notable speed-ups in the EM design process.
• PhysioInk - data analysis and active planning of an experimental study (2025-27).The overall goal of the project is the development of "quality" bio-ink that enables the printing of physiological tissues. The task of THWS is to support researchers and developers in the life science field by using modern methods of artificial intelligence and experimental design, employing a central database and data analysis system provided for the partners. Innovative solutions provided by THWS through data analysis should offer optimal, accelerated, and targeted methods, as well as variation possibilities for the development and production of the bio-ink.

Closed projects:

• KI Transfer+ (2023-2025) (KIRZ)

Publications

DBLP

Google Scholar

2025

2024

2023

2022

2021

2020

2019

2018

2017

2015

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
• Executive Committee Interdisciplinary College GI-KI

Conference organizing committee

• KI2025 is the 48th German Conference on Artificial Intelligence - tutorial/workshop chair

Program committee / editorial board

Area Chair

• NeurIPS 2024, 2025

Action Editor

• TMLR since 2024
• TMLR Expert Reviewer Recognition - 2025

Reviewer

• ECML-PKDD 2022
• ICLR 2022, 2023, 2025
  
• ICML 2021, 2022, 2023, 2024
• MCPR 2021, 2022
• NeurIPS 2021, 2022
• TMLR since 2022
• MDIS 2024
• FNR Core Program, Luxembourg 2021, 2022, 2023

Teaching

I teach primarily in Masters Artificial Intelligence

Courses:

• Artificial Neural Networks and Cognitive Models (SoSe 2022-24), E-learning
• Scientific Seminar (WiSe 2022- ), E-learning
• Deep Unsupervised Learning (WiSe 2024), GitHub

Course responsible:

• Project Module (2022- ), E-learning 

Master thesis

• Esther Ademola: Weakly Supervised Semantic Segmentation of Multispectral Satellite Images for Land Cover Mapping (2024, THWS) (GiHub)
• Muhammad Zakriya Shah Sarawr: Step Detection (2023, THWS) (GitHub)
• Marius Benkert: Cross Topology Modeling and Optimization of Electrical Drives Using Machine Learning (2023, THWS, 2nd supervisor) (journal paper)
• Ana Muñoz Gutiérrez: Transfer Learning in Crop Remote Sensing (2023, THWS) (GitHub)
• 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, 2nd supervisor)
• Frantzeska Lavda: A Study of Recurrent neural networks (RNNs) in univariate and multivariate time series (2016, University of Geneva, , 2nd supervisor, pdf)

Supervision of student projects:

• App to Help Children with Special Needs to Improve Their Eye Movements and Focus (2023)
• Learning to Unpermute (2023)
• An Investigative Study Exploring Machine Learning Approaches for Optimising Deep Brain Stimulation Programming (2024)
• Dog Activity Detection and Recognition (2024)
• Deep Fakes for Good: A Multi-model Synthesis Pipeline for generating Learning Videos from Text (2024)
• Predictive Analysis of Cloud Motion with Convolutional and Recurrent Neural Networks (2024)
• Smartdarts: Automated Dart Scoring With Computer Vision on Portable Camera (2024)
• Detecting Bacteria in Gastric Biopsies (2025)
• Eye Tracking for Children with Special Needs (2025)
• Deepfrakes for Good II (2025)

• Probabilistic Deep Learning Group