Noha Radwan - Autonomous Intelligent Systems (original) (raw)
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Albert-Ludwigs-Universität Freiburg Technische Fakultät Autonome Intelligente Systeme Georges-Köhler-Allee 080 D-79110 Freiburg i. Br., Germany Office: 080-00-008 radwann@informatik.uni-freiburg.de Phone: +49 761 203-8011 Fax: +49 761 203-8007 |
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About me
I am a postdoctoral researcher in the Autonomous Intelligent Systems group of the Department of Computer Science at the University of Freiburg in Germany.
My research is focused on addressing the challenges associated with localization and state estimation in urban environments using techniques from computer vision and machine learning, with the overall goal of enabling scalable, lifelong behavior.
Education
- 09/2007 - 06/2012: Bachelor o.Sc. in Computer Science, German University in Cairo
- 10/2012 - 03/2015: Master o.Sc. in Computer Science, University of Freiburg
- 04/2015 - 06/2019: Dr. rer. nat. (Ph.D.) in Computer Science, Unversity of Freiburg
- 06/2019 - up to now: Working at the Autonomous Intelligent Systems group of Prof. Dr. Wolfram Burgard as Research Assistant.
Research Interests
- Robot Perception
- Visual Localization
- Robot Learning
Teaching
- Co-organizer, Robot Navigation Proseminar, University of Freiburg, WS 2018/19
- Co-organizer, Robot Navigation Seminar, University of Freiburg, WS 2016/17
- TA, Robot Mapping, University of Freiburg, WS 2015/16
Students Supervised
- Dynamic Object Invariant Space Recovery, Borna Bešić, 2018, Master's Project (Ongoing)
- Landmark-based Visual Localization using Deep Convolutional Neural Networks, Jay Patravali, 2017, Internship
- Multimodal Localization using Deep Convolutional Neural Networks, Hanna Stellmach, 2017, Master's Project
- Text Spotting using Attention Models, Claas Bollen, 2017, Master's Project
- Text Recognition in Urban Environments, Larissa Ho, 2016, Internship
Videos
- Multimodal Interaction-aware Motion Prediction for Autonomous Street Crossing
- VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry
- Deep Auxiliary Learning for Visual Localization and Odometry
- Topometric Localization with Deep Learning
- Why Did the Robot Cross the Road? - Learning from Multi-Modal Sensor Data for Autonomous Road Crossing
- Do you see the Bakery? Leveraging Geo-Referenced Texts for Global Localization in Public Maps