Dr. Sophie Burkhardt

UPDATE: Starting from November 2020, I am a junior professor at TU Kaiserslautern. There will be a new website soon. My email adress is the same except mainz now has to be replaced by kl

I am still hiring PhD students. Please contact me with a cover letter and CV if you are interested.

Contact: Dr. Sophie Burkhardt, burkhardt (at) informatik.uni-mainz.de

My PhD thesis was on multi-label topic models and my current research interests are variational autoencoders, probabilistic topic models, Bayesian nonparametrics, text classification, multi-label classification, online learning, active learning, and drift detection.

My main focus now is my new project on Semantic Disentanglement to disentangle style and topic in text data. More details will follow on my new website.

Short Scientific CV

07/2020 - Head of junior research group "Semantic Disentanglement"
01/2020 ISCB and DAAD travel award for PSB conference
05/2019 Dissertation award of the department for best PhD thesis
since 11/2017 Research associate, University of Mainz
09/2017 DAAD travel stipend for ECML/PKDD
03/2014-09/2018 Ph.D. student, University of Mainz
10/2013 – 10/2017 PRIME Research scholarship
07/2012 – 01/2013 Scholarship from University of Mainz for writing the final thesis
08/2010 – 06/2011 DAAD scholarship for year abroad at University of Sussex
04/2008 – 04/2013 Study of Philosophy and Computer Science, University of Mainz



  • AISTATS 2020, ICML 2020, ECML/PKDD 2020
  • IEEE International Conference on Data Mining  (ICDM 2014, 2016, 2017, 2019)
  • International Conference on Data Sciences and Advanced Analytics (DSAA 2014)
  • European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2015, 2017)
  • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2014, 2015, 2016, 2019)
  • Machine Learning Journal (2016)


  • Probabilistic Graphical Models and Deep Learning (2019)
  • Computational Logic (2019)
  • Software Engineering (2018)
  • Machine Learning Lab Course (2018)
  • Data Mining Lab Course(2018-2019)
  • Computational Logic (2018)
  • Software Engineering, Data Mining (2017)
  • Machine Learning Lab Course(2017-2019)
  • Machine Learning Seminar (2014 - 2019)
  • Data Mining Seminar (2013 - 2019)
  • Tutor in Programming Languages (2013, 2015)


Burkhardt, S., Siekiera, J., Glodde, J., Andrade-Navarro, M., Kramer, S. (2020) Towards identifying drug side effects from social media using active learning and crowd sourcing. In: Pacific Symposium for Biocomputing (PSB), accepted.

Burkhardt, S., Kramer, S. (2019) A Survey of Multi-Label Topic Models. In: SIGKDD Explorations.

Burkhardt, S., Kramer, S. (2019) Decoupling Sparsity and Smoothness in the Dirichlet
Variational Autoencoder Topic Model. In: Journal of Machine Learning Research 20.131, pp. 1-27. (pdf)

Code: https://github.com/sophieburkhardt/dirichlet-vae-topic-models

Burkhardt, S., Wagner, N. and Kramer, S. (2019) Extracting Rules with Adaptable Complexity from Neural Networks using K-Term DNF Optimization. In: DeCoDeML Workshop at ECML/PKDD.

Wagner, N., Burkhardt, S. and Kramer, S. (2019) A Deep Convolutional DNF Learner. In: DeCoDeML Workshop at ECML/PKDD.

Burkhardt, S., Siekiera, J. and Kramer, S. (2018) Semi-Supervised Bayesian Active Learning for Text Classification. In: Bayesian Deep Learning Workshop at NeurIPS.

Burkhardt, S. and Kramer, S. (2018) Multi-label Classification Using Stacked Hierarchical Dirichlet Processes with Reduced Sampling Complexity. In: Knowledge and Information Systems, pp. 1-23.

Burkhardt, S. and Kramer, S. (2018) Online Multi-Label Dependency Topic Models for Text Classification. In: Machine Learning 107.5, pp. 859-886.

Code available at https://github.com/sophieburkhardt/Multi-Label-Topic-Modeling

Ahmadi, Z., Burkhardt, S., Kramer, S. (2017) Online Topic Modeling: Keeping Track of News Topics for Social Good. In: Proceedings of the 2nd Workshop on Data Science for Social Good (SoGood 2017) at ECML-PKDD.

Burkhardt, S. and Kramer, S. (2017) Online Sparse Collapsed Hybrid Variational-Gibbs Algorithm for Hierarchical Dirichlet Process Topic Models, ECML-PKDD. Skopje, Macedonia, pp. 189-204.

supplement for ECML 2017 paper

Code available at https://github.com/kramerlab/HybridHDP

Burkhardt, S. and Kramer, S. (2017) Multi-label Classification Using Stacked Hierarchical Dirichlet Processes with Reduced Sampling Complexity In: Proceedings of the 8th IEEE International Conference on Big Knowledge, Ed.: Xindong Wu, Tamer Ozsu, Jim Hendler and Ruqian Lu. Hefei, China, pp. 1-8. (best student paper)

Burkhardt, S. and Kramer, S. (2015) On the Spectrum between Binary Relevance and Classifier Chains in Multi-Label Classification, ACM SAC. Salamanca, Spain.


Room: 03-621

Johannes Gutenberg – Universität Mainz
Institut für Informatik
Staudingerweg 9
55128 Mainz, Germany

E-Mail: soburkha [at] uni [dash] mainz [dot] de

(For teaching related inquiries please write to our group account datamining [at] uni-mainz.de)
Office Phone: +49-6131-39-21059