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.

Volker Tresp is a professor at Ludwig Maximilian University of Munich (LMU). He received his Diploma degree in physics from the University of Göttingen in 1984 and M.Sc., M.Phil. and Ph.D. degrees from Yale University in 1986 and 1989, respectively. During his Ph.D., he worked in Yale’s Image Processing and Analysis Group (IPAG). In 1990, he joined Siemens where he has been heading various research teams in machine learning. In 1997, he became Siemens Inventor of the Year for his innovations in neural networks research  and in 2018 became the first Siemens Distinguished Research Scientist. He revolutionized steel processing by pioneering a novel Bayesian neural network approach that cleverly integrated real-world data with simulated data from a prior solution.***  In 1994 he was a visiting scientist at the Massachusetts Institute of Technology in the Center for Biological and Computational Learning, working with the teams of Tomaso Poggio and Michael I. Jordan. He was co-editor of Advances in Neural Information Processing Systems 13.  In 2011, he was appointed professor in informatics at the LMU, where he teaches a course on machine learning and where he is leading a second research team. He is known for his work on Bayesian machine learning, in particular the Bayesian Committee Machine and his work on hierarchical learning with Gaussian processes. The IHRM, the SRM,  SUNS, and RESCAL are milestones in representation learning for multi-relational graphs. His team has been doing pioneering work on machine learning with knowledge graphs, temporal knowledge graphs, and scene graph analysis.  The work on the Tensor Brain reflects his interest in mathematical models for cognition and neuroscience. In 2020, he became a Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS). As co-director (with Kristian Kersting and Paolo Frasconi), he leads the ELLIS program “Semantic, Symbolic and Interpretable Machine Learning”.

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.

Matthias Hein is Bosch endowed Professor of Machine Learning and  the coordinator of the international master program in machine learning at the University of Tübingen. He is member of the Excellence Cluster “Machine Learning: New Perspectives for Science” and the Tübingen AI Center. His main research interests are to make machine learning systems robust, safe and explainable and to provide theoretical foundations for machine learning, in particular deep learning. He serves regularly as area chair for ICML, NeurIPS or AISTATS and has been action editor for Journal of Machine Learning Research (JMLR) from 2013 to 2018. He is an ELLIS Fellow and has been awarded the German Pattern recognition award, an ERC Starting grant and several best paper awards (NeurIPS, COLT, ALT).

Abhinav Valada is a Full Professor (W3) at the University of Freiburg, where he directs the Robot Learning Lab. He is a member of the Department of Computer Science, the BrainLinks-BrainTools center, and a founding faculty of the ELLIS unit Freiburg. Abhinav is a DFG Emmy Noether AI Fellow, Scholar of the ELLIS Society, IEEE Senior Member, and Chair of the IEEE Robotics and Automation Society Technical Committee on Robot Learning.

He received his PhD (summa cum laude) working with Prof. Wolfram Burgard at the University of Freiburg in 2019, his MS in Robotics from Carnegie Mellon University in 2013, and his BTech. in Electronics and Instrumentation Engineering from VIT University in 2010. After his PhD, he worked as a Postdoctoral researcher and subsequently an Assistant Professor (W1) from 2020 to 2023. He co-founded and served as the Director of Operations of Platypus LLC from 2013 to 2015, a company developing autonomous robotic boats in Pittsburgh, and has previously worked at the National Robotics Engineering Center and the Field Robotics Center of Carnegie Mellon University from 2011 to 2014.

Abhinav’s research lies at the intersection of robotics, machine learning, and computer vision with a focus on tackling fundamental robot perception, state estimation, and planning problems to enable robots to operate reliably in complex and diverse domains. The overall goal of his research is to develop scalable lifelong robot learning systems that continuously learn multiple tasks from what they perceive and experience by interacting with the real world. For his research, he received the IEEE RAS Early Career Award in Robotics and Automation, IROS Toshio Fukuda Young Professional Award, NVIDIA Research Award, AutoSens Most Novel Research Award, among others. Many aspects of his research have been prominently featured in wider media such as the Discovery Channel, NBC News, Business Times, and The Economic Times.

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.