Ing. Antonie Brožová

Ph.D. student

Antonie Brožová
Research interests: Variational Bayesian methods, image processing, deep learning

Biography

Publication list

EXPERIENCE

March 2021 – present: PhD student, Institute of Information Theory and Automation, CAS,

Department of Adaptive Systems

April, 2020 – February 2021: Research assistant, Institute of Information Theory and Automation,

CAS, Department of Adaptive Systems

TEACHING

Fall semester 2021, 2022, 2023: labs in Calculus 1, FNSPE CTU

EDUCATION

March, 2021 - present: CTU, Faculty of Nuclear Sciences and Physical Engineering - PhD programme:

Mathematical Engineering

2018 – Feb, 2021: CTU, Faculty of Nuclear Sciences and Physical Engineering– master programme:

Applied Mathematical Stochastic Methods

- Fall semester 2019: Kansas State University, USA – courses taken: Applied Data Mining/

Machine Learning and Predictive Analytics, Mathematics of Data and Networks, Bayesian

Inference, Experimental Design for Product Development and Quality Improvement

2015 – 2018: CTU, Faculty of Nuclear Sciences and Physical Engineering – bachelor programme:

Mathematical Engineering with focus on Applied Mathematical Stochastic Methods

LANGUAGES

English – FCE (B2, 2014), compulsory school course with an exam (C1 Academic English)

German – passive knowledge, compulsory school course (intermediate level)

SKILLS

Julia, MATLAB, R, C++

 

E-mail: antonie.brozova@gmail.com

Journal articles

  1. Brožová Antonie, Šmídl Václav, Tichý Ondřej, Evangeliou N.: Spatial-temporal source term estimation using deep neural network prior and its application to Chernobyl wildfires, Journal of Hazardous Materials 448 Download Download DOI: 10.1016/j.jhazmat.2025.137510 [2025]

Other publications

  1. Šmídl Václav, Brožová Antonie, Tichý Ondřej, Evangeliou N.: Estimation of Spatial-temporal Source Term of Chernobyl Wildfires using Deep Neural Network Prior, EGU General Assembly 2025 Download DOI: 10.5194/egusphere-egu25-11939 [2025]
  2. Brožová Antonie, Šmídl Václav, Tichý Ondřej: Bayesian methods in neural networks for inverse atmospheric modelling, Stochastic and Physical Monitoring Systems 2024 Download Download [2024]
  3. Brožová Antonie, Šmídl Václav, Tichý Ondřej, Evangeliou N.: Estimation of spatio-temporal source of microplastics using Bayesian Neural networks, EGU General Assembly 2024 Download DOI: 10.5194/egusphere-egu24-5960 [2024]
  4. Brožová Antonie, Šmídl Václav: Avoiding Undesirable Solutions of Deep Blind Image Deconvolution, Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024), p. 559-566, Eds: Radeva Petia, Furnari Antonino, Bouatouch Kadi, Sousa A. Augusto Download Download DOI: 10.5220/0012397600003660 [2024]