Johannes Gutenberg University Mainz
Institut für Informatik
Staudingerweg 9
55128 Mainz, Germany
E-Mail: siekiera@uni-mainz.de
Room: 03-623
Research Interests
Deep Learning in Population Genomics, Variational Autoencoders,Text Classification, Active Learning
Short Scientific CV
since 05/2019 Research associate at Johannes Gutenberg University in Mainz
04/2017 - 03/2019 Computer Science (M.Sc.) at Johannes Gutenberg University in Mainz
04/2014 - 08/2017 Computer Science (B.Sc.) at Johannes Gutenberg University in Mainz
Teaching
- Tutor in Complexity Theory (2017)
- Tutor in Programming Languages (2016, 2017)
- Tutor in Introduction to Programming (2016)
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. In: Pacific Symposium for Biocomputing (PSB).
Burkhardt, S., Siekiera, J., Kramer, S. (2018) Semi-Supervised Bayesian Active Learning for Text Classification. In: Bayesian Deep Learning Workshop at NeurIPS.