Please find my dissertation (Doktorarbeit) here: https://mediatum.ub.tum.de/1444880

I finished my PhD in computer science at TUM 2018. My focus was research in computer vision and machine learning (colloquially known as "artificial intelligence") advised by Prof. Daniel Cremers.

I hold a Master's degree in physics from the University of California, Santa Cruz (USA) and a Bachelor's degree in physics from the LMU Munich where I was working at the cavity quantum optics group headed by Prof. T.W. Haensch.

When I'm not in the lab you might find me playing squash or volleyball or you might encounter one of my TV productions.

I will be on leave from May 2017 – September 2017 for an internship at Google.

More about my research at Google can be found here and here.

• Optical flow estimation (coop with Freiburg; ICCV paper 2015)
• Scene flow estimation (coop with Freiburg; CVPR paper 2016)
• Facial expression recognition (student project)
• Mathematical handwriting recognition (Bachelor thesis)
• Video frame prediction (Master's thesis)
• Construction zone recognition for self-driving cars (Master's thesis)
• Semi-supervised training "learning by association" (Google internship 2016; CVPR paper 2017)
• Domain adaptation with neural networks

• Practical Course: Machine Learning for Applications in Computer Vision [10 ECTS]
• Master-Praktikum/Lab Course Deep learning for the real world [10 ECTS]

• Practical Course: Machine Learning for Applications in Computer Vision [10 ECTS]

• Machine Learning for Robotics and Computer Vision [4 ECTS]

• Seminar: Current Trends in Deep Learning [4 ECTS]

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Conference and Workshop Papers
2018
[]		Associative Deep Clustering - Training a Classification Network with no Labels (P. Haeusser, J. Plapp, V. Golkov, E. Aljalbout and D. Cremers), In Proc. of the German Conference on Pattern Recognition (GCPR), 2018.  [bibtex] [pdf]
2017
[]		Associative Domain Adaptation (P. Haeusser, T. Frerix, A. Mordvintsev and D. Cremers), In IEEE International Conference on Computer Vision (ICCV), 2017. ([code] [PDF from CVF]) [bibtex] [pdf]
[]		Better Text Understanding Through Image-To-Text Transfer (K. Kurach, S. Gelly, M. Jastrzebski, P. Haeusser, O. Teytaud, D. Vincent and O. Bousquet), In arxiv:1705.08386, 2017.  [bibtex] [pdf]
[]		Learning by Association - A versatile semi-supervised training method for neural networks (P. Haeusser, A. Mordvintsev and D. Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ([code] [PDF from CVF]) [bibtex] [pdf]
2015
[]		FlowNet: Learning Optical Flow with Convolutional Networks (A. Dosovitskiy, P. Fischer, E. Ilg, P. Haeusser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers and T. Brox), In IEEE International Conference on Computer Vision (ICCV), 2015. ([video],[code]) [bibtex] [doi] [pdf]

• Challenges in Dynamic Imaging Data, June 9th 2015, Cambridge University slides
• Deep Learning and Convolutional Networks, July 9th 2015, University of Augsburg
• Kuenstliche Intelligenz und Digitalisierung, April 8th, 2017, Akademie fuer politische Bildung, Tutzing

Feel free to connect via

• Github (request to see repos in development)
• Linkedin
• Xing
• Facebook