Location: Munich
Collaborated on cross-site research in autonomous driving systems.
I obtained a double degree in Mathematics and Physics from the University of Barcelona in 2021, followed by a Master’s in Applied Mathematics from the Technical University of Munich.
During my Master’s thesis, in collaboration with the Helmholtz Center Munich and funded by an ELIZA scholarship, I studied the prediction of drug-induced effects in single-cell biology. The methods of choice were state-of-the-art integrations of Optimal Transport and Deep Learning Methods.
Since graduation, I have been working as an R&D engineer at Canon Production Printing. As part of my work, I develop numerical simulations and data-based approaches to design and optimize print processes.
Thomas Wimmer is a doctoral researcher and PhD fellow of the Max Planck ETH Center for Learning Systems (CLS) and the ELLIS program, advised by Jan Eric Lenssen, Bernt Schiele, Christian Theobalt (MPI for Informatics), and Siyu Tang (ETH Zurich). He holds Master’s degrees from the Technical University of Munich and the Institut Polytechnique de Paris, both completed with highest honors. As a Master’s student, he was also an ELIZA fellow.
His research focuses on 3D computer vision at the intersection of computer graphics and geometry processing, with particular interest in (dynamic) scene understanding, reconstruction, and generation. His work has been published at top-tier venues such as CVPR, ACL, and 3DV. He has served as a reviewer for several conferences and journals, and conducted research stays at institutions including MPI, ETH Zurich, and École Polytechnique, collaborating with researchers such as Daniel Cremers, Maks Ovsjanikov, and Federico Tombari.
I am currently finishing my Master’s degree in Bioinformatics at LMU and the Technical University of Munich. I earned my Bachelor’s degree in Bioinformatics in Munich in 2023. During my studies, I have worked in several research assistant positions, including at Prof. Fabian Theis’s lab at the Institute for Computational Biology and Prof. Matthias Mann’s lab at the Max Planck Institute for Biochemistry.
Within the ELIZA program, my focus is on the application of machine learning to medicine and biology. I primarily work in software development and have contributed to Python frameworks such as scPortrait.
I am an ELIZA and ELLIS Ph.D. student at the Technical University of Munich and TU Darmstadt, with co-supervision from the University of Oxford. My research focuses on unsupervised scene understanding in {2, 3, 4}D and representation learning. I am supervised by Daniel Cremers (CVG), Stefan Roth (VisInf), and Christian Rupprecht (VGG).
Prior to starting my Ph.D., I was a research intern at NEC Laboratories America (Princeton), where I worked with Biplob Debnath on controlling standardized image and video codecs for deep vision models using self-supervised learning.
During my studies, I worked at the Self-Organizing Systems Lab with Tim Prangemeier on 2D and 3D segmentation for biomedical applications, as well as generative approaches for live-cell in silico experiments. I also collaborated with the Artificial Intelligent Systems in Medicine Lab (led by Christoph Hoog Antink), focusing on ECG analysis using deep learning.
Xiaoxiang Zhu is a Full Professor of Data Science in Earth Observation at the Technical University of Munich (TUM), where she also serves in the board of directors of the Munich Data Science Institute. She also serves as a Visiting AI Professor at the European Space Agency’s Φ-lab. Previously, she held leadership roles including Director of the International Future Lab AI4EO and Founding Head of the EO Data Science Department at the German Aerospace Center (DLR). Her research focuses on signal processing, machine learning, and data fusion for Earth observation, particularly in urban and climate applications. Prof. Zhu has led numerous high-impact projects, including the three ERC grants. Since 2022 her work has been recognized by inclusion in the list of “Highly Cited Scientists” by Clarivate in the field “Geosciences”. According to ScholarGPS, she ranked #2 worldwide in the list of highly ranked scholars in remote sensing during the prior 5 years. She serves on several scientific advisory boards, such as those of the Potsdam Institute for Climate Impact Research and the German Research Center for Geosciences, and editorial roles including IEEE Transactions on Geoscience and Remote Sensing and IEEE Signal Processing Magazine. Her honors include IEEE Fellow, Academia Europaea Fellow, and multiple best paper awards.
Prof. Wachinger conducts research on novel AI algorithms for the analysis of medical images and their translation into clinical practice. He develops multimodal models for disease prediction and uses big data to train complex neural networks. Currently, he is focusing on the following challenges: (i) transparency of AI, (ii) integration of heterogeneous data, and (iii) generalization, bias, and fairness.
Prof. Wachinger studied computer science at TUM and ENST Paris. He holds an Honours Degree in Technology Management from CDTM. In 2011, he received his PhD in medical image analysis from TUM. As a post-doc, he was at the Massachusetts Institute of Technology in Cambridge and Harvard Medical School in Boston, USA. Subsequently, he took over an interims-professorship at the Ludwig-Maximilians-University of Munich. In 2021, he was appointed to the professorship for AI in radiology at TUM.