Technical University of Munich

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I am a PhD student at the Technical University of Munich at the Chair for Computer Vision and Artificial Intelligence headed by Prof. Daniel Cremers. I received my Master's degree from TUM and my Bachelor's degree from Technical University Graz and studied as well as conducted research at University of Oxford, University of California Berkeley and University of Agder.

My interests cover a large spectrum of Machine Learning and Deep Learning topics. Projects of mine include uncertainty aware and robust models for in domain, domain shift and out of domain (OOD) scenarios; Natural Language Processing (NLP) and Large Language Models (LLMs); Computer Vision (CV); Time Series Data Analysis with supervised and self-supervised learning algorithms; Recurrent Neural Networks (RNNs) and Transformer architectures; attribution maps; designing learning algorithms for generalization; etc.

I am looking for motivated as well as talented students. If you are interested please contact me directly via email and highlight your relevant academic experience and programming skills. Additionally, please include a CV and a recent transcript.

• Beyond In-Domain Scenarios: Robust Density-Aware Calibration, ICML 2023.

• Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration, ECCV 2022.

• Post-hoc Uncertainty Calibration for Domain Drift Scenarios, CVPR 2021, Oral Presentation.

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2023
Conference and Workshop Papers
[]		Beyond In-Domain Scenarios: Robust Density-Aware Calibration (C Tomani, F Waseda, Y Shen and D Cremers), In Proceedings of the 40th International Conference on Machine Learning (ICML), 2023.  [bibtex] [arXiv:2302.05118]
2022
Preprints
[]		Challenger: Training with Attribution Maps (C Tomani and D Cremers), In arXiv preprint, 2022.  [bibtex] [arXiv:2205.15094] [pdf]
Conference and Workshop Papers
[]		What Makes Graph Neural Networks Miscalibrated? (HHH Hsu, Y Shen, C Tomani and D Cremers), In NeurIPS, 2022. ([code]) [bibtex] [arXiv:2210.06391]
[]		Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration (C Tomani, D Cremers and F Buettner), In European Conference on Computer Vision (ECCV), 2022.  [bibtex] [arXiv:2102.12182]
2021
Conference and Workshop Papers
[]		Post-hoc Uncertainty Calibration for Domain Drift Scenarios (C Tomani, S Gruber, ME Erdem, D Cremers and F Buettner), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.  [bibtex] [arXiv:2012.10988]Oral Presentation
[]		Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration (C Tomani and F Buettner), In InThirty-FifthAAAIConferenceonArtificialIntelligence(AAAI-2021), 2021.  [bibtex] [arXiv:2012.10923]