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Education

Ph.D in Computer Science, Institute of Machine Learning, ETH Zurich, 2023
M.Sc. in Information Technology and Electrical Engineering, ETH Zurich, 2014
B.Sc. in Information Technology and Electrical Engineering, ETH Zurich, 2011

Work experience

2023 - : PostDoctoral Researcher, Institute of Machine Learning, ETH Zurich
- Development of solutions for multiomics and multimodal healthcare applications (Python, PyTorch)
- Evaluation, Design, and Training of multimodal foundation models for the Swiss AI initiative
- Leading projects and guiding junior PhD students
- ETH Zurich: I lead research projects in Prof. Dr. Julia Vogt’s lab at ETH Zurich (topics: multimodal learning, generative models)
2023: Postdoctoral Researche, UC Irvine
- I visited Prof. Dr. Stephan Mandt and his group in the fall of 2023 for a research stay.
- We worked on developing novel multimodal generative models
2018-2023 Graduate Researcher, Institute of Machine Learning, ETH Zurich
- (Co-)Authored 20 scientific publications at top-tier ML conferences (NeurIPS, ICML, ICLR) and in medical journals
- Design of multimodal generative models and representation learning methods
- Development of pipelines for machine learning applications in the healthcare domain
2015–2018 R&D Software Engineer, Logitech
- Developed and improved computer vision algorithms in the Consumer Video Unit
- Developed machine learning methods from scratch, collected the data and tested the developed methods on real-world data
- Brought various machine learning projects from the prototype to production (Python, Tensorflow, C++)
2014–2015 Project Collaborator, upicto
- Worked on computer vision algorithm for estimating the visibility distance using the camera network of MeteoSwiss
- Transformed the algorithm and the pipeline from a research project to a production algorithm (Python, R, OpenGL)
- Logitech acquired upicto in February 2015

Publications

Generation of differentially private heterogeneous electronic health records

K Chin-Cheong, TM Sutter, JE Vogt. (2020). "Generation of Differentially Private Heterogeneous Electronic Health Records." arxiv preprint.

Self-supervised disentanglement of modality-specific and shared factors improves multimodal generative model

I Daunhawer, TM Sutter, R Marcinkevics, JE Vogt. (2020). "Self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models." GCPR 2020.

Multimodal Generative Learning Utilizing the Jensen-Shannon Divergence

TM Sutter, I Daunhawer, JE Vogt. (2020). "Multimodal Generative Learning Utilizing the Jensen-Shannon Divergence." Neurips 2020.

A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions

TM Sutter, JA Roth, K Chin-Cheong, BL Hug, JE Vogt. (2020). "A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions." Journal of the American Medical Informatics Association, Volume 28, Issue 4, April 2021.

Machine Learning Algorithms Evaluate Immune Response to Novel Mycobacterium tuberculosis Antigens for Diagnosis of Tuberculosis

NR Meier, TM Sutter, M Jacobsen, THM Ottenhoff, JE Vogt, N Ritz. (2021) "Machine Learning Algorithms Evaluate Immune Response to Novel Mycobacterium tuberculosis Antigens for Diagnosis of Tuberculosis." Front. Cell. Infect. Microbiol.. 10(2020).

Generalized Multimodal ELBO

TM Sutter, I Daunhawer, JE Vogt. (2021). "Generalized multimodal ELBO." ICLR 2021.

Multimodal Generative Learning on the Mimic-CXR database

HJ Klug, TM Sutter, JE Vogt. (2021). "Multimodal Generative Learning on the Mimic-CXR database." MIDL 2021 Short Paper.

Multimodal Relational VAE

TM Sutter, JE Vogt. (2021). "Multimodal Relational VAE." Neurips 2021 Workshop on Bayesian Deep Learning.

On the Limitations of Multimodal VAEs

I Daunhawer, TM Suttter, K Chin-Cheong, E Palumbo, JE Vogt. (2022). "On the limitations of multimodal VAEs." ICLR 2022.

M(otion)-mode Based Prediction of Cardiac Function on Echocardiograms

TM Sutter, S Balzer, E Ozkan, JE Vogt. (2022) "M(otion)-mode Based Prediction of Cardiac Function on Echocardiograms." Medical Imaging meets NeurIPS Workshop 2022.

Learning Group Importance using the Differentiable Hypergeometric Distribution

TM Sutter, L Manduchi, A Ryser, JE Vogt. (2023). "Learning Group Importance using the Differentiable Hypergeometric Distribution" ICLR 2023.

M(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms

E Ozkan, TM Sutter, Y Hu, S Balzer, JE Vogt. (2023) "M(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms." GCPR 2023.

Differentiable Random Partition Models

TM Sutter, A Ryser, J Liebeskind, JE Vogt. (2023) "Differentiable Random Partition Models." NeurIPS 2023.

Anomaly Detection by Context Contrasting

A Ryser, TM Sutter, A Marx, JE Vogt (2024) "Anomaly Detection by Context Contrasting." arxiv.

Unity by Diversity: Improved Representation Learning in Multimodal VAEs

TM Sutter, Y Meng, A Agostini, D Chopard, N Fortin, JE Vogt, B Shahbaba, S Mandt (2024) "Unity by Diversity: Improved Representation Learning in Multimodal VAEs." Neurips 2024.

Weakly-Supervised Multimodal Learning on MIMIC-CXR

A Agostini, D Chopard, Y Meng, N Fortin, B Shahbaba, S Mandt, TM Sutter and JE Vogt (2024) "Weakly-Supervised Multimodal Learning on MIMIC-CXR." ML4H.

Thomas M. Sutter

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