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Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich

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Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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News

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

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members:tomani [2019/07/18 15:23]
Quirin Lohr created
members:tomani [2024/04/18 19:28] (current)
Christian Tomani
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 <member/> <member/>
 +
 +==== Brief Bio ====
 +Find me on [[https://www.linkedin.com/in/christiantomani/ |Linkedin]] and [[https://scholar.google.com/citations?hl=de&user=gfRxpTUAAAAJ |Google Scholar]].
 +
 +I am a PhD student at the Technical University of Munich at the Chair of [[:members:cremers|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. I worked at Google, Meta and Siemens as a research intern.
 +
 +
 +<fs large>**Research Internships in Industry:**</fs> 
 +
 +**Meta** (New York)\\
 +//Paper:// Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations (C Tomani, Kamalika C, Ivan E, Daniel C, Mark I), In arXiv preprint, 2024.
 +
 +**Google** (San Francisco Bay Area)\\
 +//Paper:// Quality Control at Your Fingertips: Quality-Aware Translation Models (C Tomani, D Vilar and M Freitag and C Cherry and S Naskar and M Finkelstein and D Cremers), In arXiv preprint, 2023.
 +
 +**Siemens** (Munich)\\
 +//Paper:// Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration (C Tomani and F Buettner), AAAI, 2021.
 +
 +
 +<fs large>**Research Visits in Academia:**</fs> 
 +
 +**University of Oxford**\\
 +Machine Learning Group - Department of Engineering Science
 +
 +**University of California Berkeley**\\
 +Artificial Intelligence Research Lab (BAIR) - Redwood Center for Theoretical Neuroscience
 +
 +
 +
 +
 +==== My Work ====
 +I am interested in developing reliable, robust, and reasoning-based large language models (LLMs) and multimodal models. Moreover, I am fascinated by enhancing the reasoning ability, safety and uncertainty awareness of generative models through pre-training and post-training (via fine-tuning and alignment to human preferences) to develop grounded world models and improve factuality and trustworthiness.
 +
 +My work covers a large spectrum of Machine Learning and Deep Learning topics. Projects of mine include reliable, reasoning-based, safe and uncertainty aware models for in domain, domain shift and out of domain (OOD) scenarios; Natural Language Processing (NLP) and Large Language Models (LLMs); investigating reasoning capabilities and developing reliable 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. 
 +
 +
 +==== Publications ====
 +  * **Beyond In-Domain Scenarios: Robust Density-Aware Calibration**, //ICML// 2023.
 +<html><center><iframe width="640" height="360" src="https://www.youtube.com/embed/_foOYyjYing" frameborder="0" allowfullscreen></iframe>
 +</center></html>
 +<html><br /></html>
 +
 +
 +  * **Parameterized Temperature Scaling for Boosting the Expressive Power in Post-Hoc Uncertainty Calibration**, //ECCV// 2022.
 +<html><center><iframe width="640" height="360" src="https://www.youtube.com/embed/i-TVNPkB_lw" frameborder="0" allowfullscreen></iframe>
 +</center></html>
 +<html><br /></html>
 +
 +
 +  * **Post-hoc Uncertainty Calibration for Domain Drift Scenarios**, //CVPR// 2021, Oral Presentation.
 +<html><center><iframe width="640" height="360" src="https://www.youtube.com/embed/IpKfdIYA8Sg" frameborder="0" allowfullscreen></iframe>
 +</center></html>
 +<html><br /></html>
 +
 +
 +<bibtex>
 +      <author>C. Tomani</author>
 +      <bytype>-1</bytype>
 +</bibtex>
 +

Rechte Seite

Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:

News

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

More