Violeta Teodora Trifunov, M.Sc.
Violeta Teodora Trifunov
Address: Computer Vision Group
Department of Mathematics and Computer Science
Friedrich Schiller University of Jena
Ernst-Abbe-Platz 2
07743 Jena
Germany
Phone: +49 (0) 3641 9 46414
E-mail: violetateodora (dot) trifunov (at) uni-jena (dot) de
Room: 1212
Links:
Curriculum Vitae
since Sep 2017 PhD student
Computer Vision Group, Friedrich Schiller University Jena
Climate Informatics Group, German Aerospace Center (DLR), Institute for Data Science, Jena
Research topic: “Deep graphical models and domain knowledge integration”
2015 – 2017 M.Sc. in Mathematics
Rheinische-Friedrich-Wilhelms University Bonn
Master Thesis: “Endomorphism Algebras of Generators-Cogenerators Associated with the Cartan Matrix”
2012 – 2015 B.Sc. in Mathematics
University of Novi Sad
Research Interests
  • Deep Learning
  • Causal Graphical Models
  • Knowledge Integration
  • Causality
  • Anomaly Detection
  • Climate Informatics
Projects
Deep graphical models and domain knowledge integration
Climate data has been vastly accumulated over the past several years, making climate science one of the most data-rich domains. Despite the abundance of data to process, data science has not had a lot of impact on climate research so far, partly due to the fact that ample expert knowledge is rarely exploited. The main goal of this project is bridging the gap between deep learning and causal graphical models while using domain knowledge which could prove to be of significant importance for facilitating an understanding of the Earth system. We aim to develop a sequential version of the Causal Effect Variational Auto-Encoder (CEVAE) and apply it to time series of ecological or climate variables having suitable underlying causal graph structure. When this is accomplished, we intend to apply our method to time series anomaly detection, as well as to variables having more general causal structures.
Publications
2022
Sequential Causal Effect Variational Autoencoder: Time Series Causal Link Estimation under Hidden Confounding
Violeta Teodora Trifunov and Maha Shadaydeh and Joachim Denzler.
arXiv preprint arXiv:2209.11497. 2022.
[bibtex] [web] [abstract]
Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions
Violeta Teodora Trifunov and Maha Shadaydeh and Joachim Denzler.
NeurIPS Workshop on A Causal View on Dynamical Systems (NeurIPS-WS). 2022.
[bibtex] [pdf] [web] [abstract]
2021
Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning
Violeta Teodora Trifunov and Maha Shadaydeh and Björn Barz and Joachim Denzler.
IEEE International Conference on Machine Learning and Applications (ICMLA). Pages 166-172. 2021.
[bibtex] [pdf] [web] [abstract]
A Data-Driven Approach to Partitioning Net Ecosystem Exchange Using a Deep State Space Model
Violeta Teodora Trifunov and Maha Shadaydeh and Jakob Runge and Markus Reichstein and Joachim Denzler.
IEEE Access. 9 : pp. 107873-107883. 2021.
[bibtex] [abstract]
2019
Causal Link Estimation under Hidden Confounding in Ecological Time Series
Violeta Teodora Trifunov and Maha Shadaydeh and Jakob Runge and Veronika Eyring and Markus Reichstein and Joachim Denzler.
International Workshop on Climate Informatics (CI). 2019.
[bibtex] [pdf] [abstract]
Nonlinear Causal Link Estimation under Hidden Confounding with an Application to Time-Series Anomaly Detection
Violeta Teodora Trifunov and Maha Shadaydeh and Jakob Runge and Veronika Eyring and Markus Reichstein and Joachim Denzler.
German Conference on Pattern Recognition (GCPR). Pages 261-273. 2019.
[bibtex] [pdf] [abstract]
2018
Domain knowledge integration for causality analysis of carbon-cycle variables
Violeta Teodora Trifunov and Maha Shadaydeh and Jakob Runge and Veronika Eyring and Markus Reichstein and Joachim Denzler.
American Geophysical Union Fall Meeting (AGU): Abstract + Poster Presentation. 2018.
[bibtex] [web] [abstract]