I am interested in computer vision and machine learning with a focus on 3D scene understanding, parsing and reconstruction. During my Ph.D. I have developed probabilistic models for 3D traffic scene understanding from movable platforms.
I am interested in computer vision, machine learning, robotics and human-autonomy interaction. I aim to enable intelligent systems (e.g., autonomous vehicles) that can automatically understand and interactively learn from their environment. Towards this goal, I work to address real-world challenges in robust perception and action of safety-critical autonomous systems.
My main interests include computer vision and machine learning. I focus on depth estimation and 3D reconstruction, with a mix between applied and more theoretical research.
Perception is a fundamental part of intelligence since perception is necessary to acquire knowledge and knowledge is necessary to understand perception. Therefore computer vision is one of the most important aspects in the realization of intelligent systems. My interest of research lies in computer vision and the combination with machine learning which, to my mind, will enable the realization of intelligent systems. Currently, I am working on optical flow and how to incorporate high-level information to alleviate this ill-posed problem.
I am a PhD student under the joint supervision of Andreas Geiger and Sebastian Nowozin from Microsoft Research. Currently, I am working on generative and probabilistic models in machine learning and computer vision.
My research focuses on understanding the visual information given in the world to generate realistic data synthetically. In particular, I am interested in generative modelling in the field of computer vision and machine learning.
Since November I am a PhD candidate under the supervision of Prof. Andreas Geiger. Currently I am working on deep generative models in collaboration with the ML team of the ETAS GmbH, a subsidiary of Bosch.
One of the most challenging tasks of Computer Vision is to endow computers with the ability to discover the underlying relationships between the objects in a scene. The large amount of available labeled data as well as the fast progress in deep learning has significantly advanced many Computer Vision tasks, such as object segmentation. optical flow estimation, action recognition etc. However, a truly intelligent system would ideally be able to infer high-level semantics underlying human actions such as motivation, intent and emotion. However, all human actions involve some uncertainty. To this end, I would like to either develop or to further enhance existing methodologies that incorporate such uncertainties. For now, I have worked on the 3D reconstruction task, by developing a model able to incorporate uncertainties in the image formation process..
My research interest lies in the intersection of Computer Vision, Computer Graphics and Machine Learning. Currently I am working on reflectance and material estimation from RGB video input.