Research

Overview

I am a PhD student at the Division of Systems and Control within the Department of Information Technology and the Centre for Interdisciplinary Mathematics in Uppsala, supervised by Fredrik Lindsten, Dave Zachariah, and Erik Sjöblom. The main focus of my PhD studies is uncertainty-aware deep learning. Currently, I am particularly interested in analyzing and evaluating calibration of probabilistic models.

For my Master’s thesis in Mathematics at TU Munich, I explored a delay differential equation system of quorum sensing in Pseudomonas aeruginosa. For the numerical analysis of the investigated system I used and contributed to DelayDiffEq.jl, a delay differential equation solver in the Julia programming language.

Moreover, during my Master’s studies I worked on a research project at the Institute of Computational Biology, Munich, and investigated post-translational histone modifications using hidden Markov models.

During my medical studies at LMU Munich I was part of a research group at the Center of Neuropathology, Munich, and performed sequencing and imaging analysis of 5-hmC in brain tumours.

Talks

• Widmann, D. (2022). μ-seminar: π. Thursday Tea Time, Division of Systems and Control, Uppsala University. slides

• Widmann, D. (2022). Calibration of probabilistic predictive models. Machine Learning Journal Club of the Gatsby Unit at UCL. source slides

• Widmann, D. (2022). Scientific Computing with Julia. Polygon Math Club at the American University in Bulgaria. source slides/notebook

• Widmann, D. (2021). Calibration analysis of probabilistic models in Julia. JuliaCon 2021. source info video slides/notebook

• Widmann, D. (2021). Probabilistic Modelling with Turing.jl. Julia User Group Munich. source slides/notebook

• Widmann, D. (2019). Solving Delay Differential Equations with Julia. JuliaCon 2019, Baltimore, MD, USA. source video slides

• Widmann, D. (2018). Introduction to Machine Learning. Day of the Programmer 2018, Jönköping, Sweden. abstract

Posters

• Widmann, D. (2021). EllipticalSliceSampling.jl: MCMC with Gaussian priors. JuliaCon 2021. source info video slides/notebook

• Widmann, D., Lindsten, F., & Zachariah, D. (2019). Calibration measures in multi-class classification. Swedish Symposium on Deep Learning (SSDL), Norrköping, Sweden.

• Vaicenavicius, J., Widmann, D., Andersson, C., Lindsten, F., Schön, T. B. & Roll, J. (2018). Evaluation of model calibration in classification. Machine Learning Summer School (MLSS) 2018, Madrid, Spain.

• Widmann, D., & Kuttler, C. (2018). Quorum sensing of Pseudomonas putida in continuous cultures. 11th European Conference on Mathematical and Theoretical Biology (ECMTB), Lisbon, Portugal.

• Vaicenavicius, J., Widmann, D., Andersson, C., Lindsten, F., Schön, T. B. & Roll, J. (2018). Calibrated predictive uncertainty in classification with neural networks. Reglermöte 2018, Stockholm, Sweden. abstract

Publications

Conferences

• Widmann, D., Lindsten, F., & Zachariah, D. (2021). Calibration tests beyond classification. International Conference on Learning Representations (ICLR) 2021. full-text webpage code video slides poster

• Widmann, D., Lindsten, F., & Zachariah, D. (2019). Calibration tests in multi-class classification: A unifying framework. Conference on Neural Information Processing Systems (NeurIPS) 2019. arXiv code code slides poster

• Vaicenavicius, J., Widmann, D., Andersson, C., Lindsten, F., Roll, J. & Schön, T. B. (2019). Evaluating model calibration in classification. Proceedings of Machine Learning Research, in PMLR 89:3459-3467. full-text arXiv code

Journals

• Kraus, T. F. J., Globisch, D., Wagner, M., Eigenbrod, S., Widmann, D., Münzel, M., Müller, M., Pfaffeneder, T., Hackner, B., Feiden, W., Schüller, U., Carell, T., Kretzschmar, H. A. (2012). Low values of 5-hydroxymethylcytosine (5hmc), the “sixth base,” are associated with anaplasia in human brain tumors. International Journal of Cancer, 131(7), 1577–1590. full-text