Technical University of Munich

• SS22: Practical course Learning For Self-Driving Cars and Intelligent Systems with Mariia Gladkova.
• WS21/22: Practical course Learning For Self-Driving Cars and Intelligent Systems with Mariia Gladkova.
• SS21: Practical course Learning For Self-Driving Cars and Intelligent Systems
• WS20/21: Practical course Learning For Self-Driving Cars and Intelligent Systems
• SS20: Practical course Learning For Self-Driving Cars and Intelligent Systems with Patrick Wenzel.
• WS19/20: Practical course Learning For Self-Driving Cars and Intelligent Systems with Yuesong Shen and Patrick Wenzel.
• SS19: Practical course Hands-on Deep Learning for Computer Vision and Biomedicine with Dr. Vladimir Golkov and Patrick Wenzel.

In case you are interested in doing an IDP, Guided Research, Bachelor/Master thesis, in the areas of Graphical neural networks, active learning, vehicle control feel free to send your application including resume and transcripts and a short description of the area of interest.

• Fusion of Multi-sensor data for robust vehicle control
• Pseudo-Lidar Vehicle navigation.
• Multi-agent vehicle control in unconstrained environments
• Identifying critical brain regions in autism detection.
• Discrete Optimization with graphical neural networks

• Offline Reinforcement Learning for vehicle control. [IDP, Samuel Weber]
• VentriloquistNet: Leveraging Speech Cues to Generate Naturalistic Talking Head Motions. [Master Thesis, Deepan Das]
• 3D Spatial Motion Estimation of Image Pixels. [Guided Research, Tong Yan Chan]
• Active Learning For Reducing The Labelling Effort in Semantic Segmentation. [Application Project, Mohab Ghanem]
• Self-supervised Novel View Image Synthesis for Improving Performance of Driving Algorithms.[Master Thesis, Yiu Ting Tang]
• Relative Pose Estimation using Novel View Synthesis for Relocalization.[Guided Research, Melis Öcal]
• Deep Active learning on Graphical networks for semantic segmentation.[Master Thesis, Viktor Drobnyi]
• Model Pruning for faster inference [Guided Research, Paul Ursulean]
• Self-Supervised Vehicle Control on Sparse 3D point clouds [Bachelor Thesis, Florian Müller]
• IMU based pose estimation using deep neural networks [IDP, Nicholas Gao]

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Journal Articles
2020
[]		GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization (L. von Stumberg, P. Wenzel, Q. Khan and D. Cremers), In IEEE Robotics and Automation Letters (RA-L), volume 5, 2020. ([arXiv][video][project page][supplementary]) [bibtex]
Conference and Workshop Papers
2022
[]		Ventriloquist-Net: Leveraging Speech Cues for Emotive Talking Head Generation (D Das, Q Khan and D Cremers), In IEEE International Conference on Image Processing (Accepted), 2022.  [bibtex]
[]		Biologically Inspired Neural Path Finding (L Hang, Q Khan, V Tresp and D Cremers), In Brain Informatics (Accepted), Springer, 2022. ([code]) [bibtex]
[]		Lateral Ego-Vehicle Control Without Supervision Using Point Clouds (F Müller, Q Khan and D Cremers), In Pattern Recognition and Artificial Intelligence, Springer International Publishing, 2022.  [bibtex] [pdf]
2021
[]		Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry (Q. Khan, P. Wenzel and D. Cremers), In International Conference on Artificial Intelligence and Statistics (AISTATS), 2021. ([arXiv]) [bibtex]
2020
[]		4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving (P. Wenzel, R. Wang, N. Yang, Q. Cheng, Q. Khan, L. von Stumberg, N. Zeller and D. Cremers), In Proceedings of the German Conference on Pattern Recognition (GCPR), 2020. ([project page][arXiv][video]) [bibtex] [pdf]
2019
[]		Towards Generalizing Sensorimotor Control Across Weather Conditions (Q. Khan, P. Wenzel, D. Cremers and L. Leal-Taixé), In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019. ([arXiv]) [bibtex] [pdf]
2018
[]		Modular Vehicle Control for Transferring Semantic Information Between Weather Conditions Using GANs (P. Wenzel, Q. Khan, D. Cremers and L. Leal-Taixé), In Conference on Robot Learning (CoRL), 2018. ([arXiv][videos][poster]) [bibtex] [pdf]
[]		q-Space Deep Learning for Alzheimer's Disease Diagnosis: Global Prediction and Weakly-Supervised Localization (V. Golkov, P. Swazinna, M. M. Schmitt, Q. A. Khan, C. M. W. Tax, M. Serahlazau, F. Pasa, F. Pfeiffer, G. J. Biessels, A. Leemans and D. Cremers), In International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting, 2018.  [bibtex] [pdf]
2017
[]		Establishment of an interdisciplinary workflow of machine learning-based Radiomics in sarcoma patients (J.C. Peeken, C. Knie, V. Golkov, K. Kessel, F. Pasa, Q. Khan, M. Seroglazov, J. Kukačka, T. Goldberg, L. Richter, J. Reeb, B. Rost, F. Pfeiffer, D. Cremers, F. Nüsslin and S.E. Combs), In 23. Jahrestagung der Deutschen Gesellschaft für Radioonkologie (DEGRO), 2017.  [bibtex]