About me

I am an Assistant Professor at the Department of Applied Mathematics, part of the Faculty of Information Technology at the Czech Technical University in Prague.

I did my Ph.D. at Faculty of Information Technology at the Czech Technical University in Prague. My supervisor was Marcel Jiřina and co-supervisor Daniel Vašata. The thesis called "Advanced Methods of Asymmetric Heterogeneous Transfer Learning" was defended on 3. 3. 2021.

I am a member of the Machine Learning & Computational Group based at the Czech Technical University in Prague.

Teaching

Data Visualizations

2022 - present
Winter semester, FIT CTU

Data Preprocessing

2020 - present
Winter semester, FIT CTU

Knowledge Engineering Seminar

2019 - present
Winter/Summer semester, FIT CTU

Enterprise Data Warehouses

2018 - 2021
Summer semester, FIT CTU

Expert systems

2016 - 2017
Winter semester, FIT CTU

Data Mining

2016
Winter semester, FIT CTU

Supervision of theses in the areas of data preprocessing, transfer and metric learning, educational data mining, data warehousing and business intelligence. If you are interested in any of these topics and would like a more specific assignment or if you have your own idea for a thesis, please do not hesitate to contact me.

Projects

  • Member of "The use of Vehicle-to-Grid technology to provide energy flexibility" research team, Technology Agency of the Czech Republic (TAČR) grant No. TS01020030, 2024-2027, link

  • Member of "Flow-based Encrypted Traffic Analysis" research team, Ministry of the Interior grant No. VJ02010024, 2022-2025, link

  • Member of “Analysis of thematicclusters from the field of current cultural and social categories and their application to literary works of Czech 19th and 20th century" research team, Technology Agency of the Czech Republic (TAČR) grant No. TL05000288, 2021-2023, link

  • Member of "Creation of advanced risk analysis and a software tool for the purpose of identification of risk subjects on their entrance to the VAT system" research team, Technology Agency of the Czech Republic (TAČR) grant No. TL03000276, 2020-2023, link

  • Member of “Modern Algorithms and Techniques of Knowledge Engineering” research team, grant No. SGS20/213/OHK3/3T/18, 2020-2022

  • Member of “Fusion-Based Knowledge Discovery in Human Activity Data” research team, Czech Science Foundation (GAČR) grant No. GA18-18080S, 2018-2020, link

  • Member of “Mining information from unstructured data” research team, grant No. SGS17/210/OHK3/3T/18, 2016-2019

  • Member of “CTU Data Warehouse” research team, IP 2016-2021

Publications

  • Miltner, M.; Zíka, J.; Bryksa, A.; Štogl, O.; Starý, O.; Friedjungová, M.; Vašata, D. Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas. NeurIPS 2024 Workshop on Tackling Climate Change with Machine Learning, Poster.

  • Miltner, M.; Bryksa, A.; Štogl, O.; Starý, O.; Friedjungová, M.; Vašata, D. Towards deeper understanding of public EV charging load patterns, Case study covering data from Prague, Czechia. EESYMP 2024.

  • Klesnilová, K.; Klouda, K.; Friedjungová, M.; Plecháč, P. Automatic Poetic Metre Detection for Czech Verse. Studia Metrica et Poetica. University of Tartu Press, 2024.Link.

  • Brabec, J.; Friedjungová, M.; Vašata, D.; Englund, E.; Bengzon, J.; Knutsonn, L.; Szczepankiewicz, F.; van Westen, D.; Sundgren, P. C.; Nilsson, M. Meningioma microstructure assessed by diffusion MRI: an investigation of the source of mean diffusivity and fractional anisotropy by quantitative histology. NeuroImage: Clinical. Elsevier, 2023.Link.

  • Brabec, J.; Friedjungová, M.; Vašata, D.; Englund, E.; Knutsonn, L.; Szczepankiewicz, F.; Sundgren, P. C.; Nilsson, M. Mean diffusivity and fractional anisotropy at the mesoscopic level in meningioma tumors: Relation with cell density and image anisotropy. Abstract, ESNR 2022.

  • Lank, M.; Friedjungová, M. Road Quality Classification. In Image Analysis and Processing – ICIAP 2022. Springer International Publishing, Cham, 2021.Link.

  • Brabec, J.; Friedjungová, M.; Vašata, D.; Englund, E.; Knutsonn, L.; Szczepankiewicz, F.; Sundgren, P. C.; Nilsson, M. Explaining variation in DTI parameters with meningioma microscopy: A comparison between a neural network and an image-feature-based approach. Abstract, ISMRM 2022.

  • Vašata, D.; Halama, T.; Friedjungová, M. Image Inpainting Using Wasserstein Generative Adversarial Imputation Network. In Artificial Neural Networks and Machine Learning – ICANN 2021. Springer International Publishing, Cham, 2021.Link.

  • Kovalenko, A.; Kordík, P.; Friedjungová, M. Dynamic Neural Diversification: Path to Computationally Sustainable Neural Networks. In Artificial Neural Networks and Machine Learning – ICANN 2021. Springer International Publishing, Cham, 2021.Link.

  • Friedjungová, M.; Vašata, D.; Chobola, T.; Jiřina, M. Unsupervised Latent Space Translation Network. In ESANN 2020 - Proceedings, 2020. Link.

  • Friedjungová, M.; Vašata, D.; Balatsko, M.; Jiřina, M. Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network. In Computational Science - ICCS 2020. Springer International Publishing, Cham, 2020. Link.

  • Friedjungová M., Vašata D., Jiřina M. Missing Features Reconstruction and Its Impact on Classification Accuracy. In Computational Science - ICCS 2019. Springer International Publishing, Cham, 2019. Link.

  • Kubernátová, P.; Friedjungová, M.; van Duijn, M. Constructing a Data Visualization Recommender System. In Data Management Technologies and Applications.Springer International Publishing, Cham, 2019. Link.

  • Kubernátová, P.; Friedjungová, M.; van Duijn, M. Knowledge at First Glance: A Model for a Data Visualization Recommender System Suited for Non-expert Users. In Proceedings of the 7th International Conference on Data Science, Technology and Applications. SciTePress, 2018. Link.

  • Friedjungová, M.; Jiřina, M. An Overview of Transfer Learning Focused on Asymmetric Heterogeneous Approaches. In Data Management Technologies and Applications. Springer International Publishing, Cham, 2018. Link.

  • Friedjungová, M.; Jiřina, M. Asymmetric Heterogeneous Transfer Learning: A Survey. In Proceedings of the 6th International Conference on Data Science, Technology and Applications. SciTePress, 2017. Link.

  • DBLP source.

Theses Supervised - not updated

List of supervised students and their topics (in Czech or English, depends on language of the thesis).

  • Martin Lank: Road Quality Classification. Bachelor thesis, 2021. Link

  • Iveta Šárfyová: Data Augmentation Using Generative Adversarial Networks. Bachelor thesis, 2020. Link

  • Tomáš Halama: Image Inpainting Using Generative Adversarial Networks. Bachelor thesis, 2020. Link

  • Jan Nováček: Analýza chování studentů v systému MARAST. Master's thesis, 2018. Link

  • Radomír Žemlička: Spolupráce studentů různé úrovně znalostí. Bachelor thesis, 2018. Link

  • Ondřej Pleticha: Webová aplikace pro monitoring datového skladu ČVUT. Bachelor thesis, 2018. Link

  • Richard Werner: Framework pro tvorbu diagnostického znalostního systému. Bachelor thesis, 2018. Link

  • Jakub Krejčí: Návrh datových vrstev pro datový sklad ČVUT. Master's thesis, 2017. Link

  • Ondřej Nový: Doporučovací systém pro výběr volitelných předmětů. Master's thesis, 2017. Link

Work Experience

Outside of the academic field I work as a data architect and data analyst for the university data warehouse. I also provide consulting in the area of reporting and business intelligence solutions. See my LinkedIn profile for more information.