Focus area: Foundations of ML: Computer Vision

Experienced Research Scientist with a demonstrated history of working on computer vision and image processing domains at well-known research institutes. Skilled in Computer Vision, Image Processing, Video Processing, Signal Processing, Feature Extraction, Deep Learning, Machine Learning, Biometrics, C++, Pattern Recognition, Python Programming, MATLAB and LaTeX. Strong research professional with a Doctor of Philosophy (Ph.D.) focused in facial biometrics (2D and 3D face recognition, face spoofing/anti-spoofing, disguise variations) at Multimedia Communications Department from Telecom ParisTech. Current research on multi-view action recognition using deep convolutional descriptors. Numerous well cited journals and conference papers in top journals and conferences.

Vittorio Ferrari is a Principal Research Scientist at Meta, working on realistic avatars for next-gen communication on wearable devices. In 2023-2025 he was the Director of Science at Synthesia, where he led R&D groups developing cutting-edge generative AI technology. Previously he built and led multiple research groups on computer vision and machine learning at Google (Principal Scientist), the University of Edinburgh (Full Professor), and ETH Zurich (Assistant Professor). He has co-authored over 160 scientific papers and won the best paper award at the European Conference in Computer Vision in 2012 for his work on large-scale segmentation. He received the prestigious ERC Starting Grant, also in 2012. He led the creation of Open Images, one of the most widely adopted computer vision datasets worldwide. While at Google his groups contributed technology to several major products (with launches e.g. on the Pixel phone, Google Photos, Google Lens). He was a Program Chair for ECCV 2018 and a General Chair for ECCV 2020. He is an Associate Editor of IEEE Pattern Analysis and Machine Intelligence, and formerly of the International Journal of Computer Vision. His recent research interests are in Generative Video, 3D Deep Learning, and Vision+Language models.

Hi! The ‘3D Graphics & Vision’ group at TU Darmstadt works at the intersection of computer graphics, computer vision and machine learning. Specifically, we are interested in marker-less motion capturing of facial performances, human bodies as well as general non-rigid objects. Besides capturing and reconstructing reality, we work on AI-based synthesis techniques that allow for photorealistic image and video synthesis.

Simone Schaub-Meyer is an assistant professor and leads the research group Image and Video Analysis at the Technical University of Darmstadt. She is an ELLIS member and a member of the Hessian Center for Artificial Intelligence (hessian.AI). The focus of her research is on developing efficient, robust, and understandable methods and algorithms for image and video analysis. She received the renowned Emmy Noether Programme (ENP) grant from the German Research Foundation (DFG), supporting her research on Interpretable Neural Networks for Dense Image and Video Analysis. She obtained her doctoral degree from ETH Zurich, advised by Prof. Dr. Markus Gross and in collaboration with Disney Research Zurich. Her doctoral thesis was awarded the ETH Medal.

I am an ELLIS PhD student in the Medical Image Computing department at the German Cancer Research Center (DKFZ). Before starting my PhD, I worked as a Junior Data Scientist at AstraZeneca in Gothenburg (Sweden) and as a Research Assistant at Uppsala University.

My current research focuses on improving model generalizability across clinical settings, with a particular emphasis on brain imaging. I am developing a stroke identification algorithm designed to perform robustly in multi-centric environments. I am also collaborating with the University of Amsterdam on analyzing temporal brain image data to extract patterns that enhance model generalization.

I have received several travel grants for international research stays during my PhD, as well as excellence scholarships during my Bachelor’s and Master’s studies.

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.

Christian Theobalt is The Scientific Director of the Visual Computing and Artificial Intelligence Department at the Max-Planck-Institute for Informatics, Saarbrücken, Germany. He is also a Professor of Computer Science at Saarland University, Germany. Christian is also the Founding Director of the Saarbrücken Research Center for Visual Computing, Interaction and Artificial Intelligence (VIA), a strategic research partnership between Google and MPI for Informatics. From 2007 until 2009 he was a Visiting Assistant Professor in the Department of Computer Science at Stanford University. He received his MSc degree in Artificial Intelligence from the University of Edinburgh, his Diplom (MS) degree in Computer Science from Saarland University, and his PhD (Dr.-Ing.) from the Max-Planck-Institute for Informatics. In his research he looks at algorithmic problems that lie at the intersection of Computer Graphics, Computer Vision and Machine Learning, such as: static and dynamic 3D scene reconstruction, neural rendering and neural scene representations, marker-less motion and performance capture, virtual humans, virtual and augmented reality, computer animation, intrinsic video and inverse rendering, computational videography, machine learning for graphics and vision, visual generative AI, new sensors for 3D acquisition, as well as image- and physically-based rendering. He is also interested in using reconstruction techniques for human computer interaction. For his work, he received several awards, including the Otto Hahn Medal of the Max-Planck Society in 2007, the EUROGRAPHICS Young Researcher Award in 2009, the German Pattern Recognition Award 2012, the Karl Heinz Beckurts Award in 2017, and the EUROGRAPHICS Outstanding Technical Contributions Award in 2020. He is a Fellow of EUROGRAPHICS and of ELLIS (European Lab for Learning and Intelligent Systems). He received two ERC grants, an ERC Starting Grant in 2013 and an ERC Consolidator Grant in 2017.

Prof Schnabel’s (*1969) field of research comprises medical image computing and machine learning. Her research focuses on intelligent imaging solutions and computer aided evaluation, including complex motion modelling, image reconstruction, image quality control, image segmentation and classification, applied to multi-modal, quantitative and dynamic imaging.

Since 2021 Julia Schnabel is Professor for Computational Imaging and AI in Medicine at TUM (TUM Liesel Beckmann Distinguished Professorship), jointly with Helmholtz Center Munich (Helmholtz Distinguished Professorship). She studied at TU Berlin (1993) and did a PhD at University College London (1998), followed by Postdocs at UMC Utrecht, King’s College London, and UCL. In 2007 she became first Associate Professor and in 2014 Full Professor of Engineering Science at University of Oxford, and from 2015 Chair in Computational Imaging at King’s College.

Professor at the DKFZ German Cancer Research Center.

I’m heading the subgroup on “Explainable Machine Learning”. Explainable learning shall open-up the blackbox of successful machine learning algorithms, in particular neural networks, to provide insight rather than mere numbers. To this end, we are designing powerful new algorithms on the basis of invertible neural networks and apply them to medicine, image analysis, and the natural and life sciences.

In addition, I’m interested in generic software bringing state-of-the-art algorithms to the end user and maintain the VIGRA image analysis library.