MACHINE LEARNING GROUP

TECHNISCHE UNIVERSITÄT KAISERSLAUTERN

Jun.-Prof. Dr. Sophie Burkhardt

Junior professor

Bio

Since 2020 Sophie Burkhardt is a junior professor at TU Kaiserslautern at the department of computer science. Previously she was a research group leader at Johannes Gutenberg University Mainz (07/2020-10/2020) and before that a PostDoc in the research group of Stefan Kramer. She obtained a PhD from Uni Mainz in 2018 under the supervision of Stefan Kramer and a Magister in Philosophy and Computer Science in 2013 with a thesis on mental representation.

Research interests

Sophie Burkhardt is interested in probabilistic machine learning methods, especially with applications on text data such as topic models, but also generative models and deep generative models in general. More recently, she is also interested in how to insert knowledge into neural models and models that bridge the fields of graphical models and neural networks. Her PhD was also concerned with multi-label classification, nonparametric Bayesian models and online models.

Appointments and scientific matters
burkhardt@cs.uni-kl.de

Curriculum Vitae

Education

2018
Doctoral Degree in Computer Science, Johannes Gutenberg University Mainz, Germany
2013
Magister in Philosophy and Computer Science, Johannes Gutenberg University Mainz, Germany

Professional Experience

since 2020
Junior Professor, TU Kaiserslautern, Kaiserslautern, Germany
2017-2020
Research Associate, Johannes Gutenberg University Mainz, Germany

Activities and honors

2019
Best Dissertation Award, Uni Mainz
2013-2017
PhD Scholarship, PRIME Research
2010-2011
Scholarship German Academic Exchange Service (DAAD)

Key publications

  • 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. Pacific Symposium for Biocomputing (PSB).
  • Burkhardt, S., Kramer, S. (2019) A Survey of Multi-Label Topic Models. SIGKDD Explorations.
  • Burkhardt, S., Kramer, S. (2019) Decoupling Sparsity and Smoothness in the Dirichlet Variational Autoencoder Topic Model. Journal of Machine Learning Research 20.131, pp. 1-27.
  • Burkhardt, S. and Kramer, S. (2018) Multi-label Classification Using Stacked Hierarchical Dirichlet Processes with Reduced Sampling Complexity. Knowledge and Information Systems, pp. 1-23.
  • Burkhardt, S. and Kramer, S. (2018) Online Multi-Label Dependency Topic Models for Text Classification. Machine Learning 107.5, pp. 859-886.
  • 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.