M.Sc.
Robin
Dietrich
Technische Universität München
Dienstort
Informatik 6 - Lehrstuhl für Robotik, Künstliche Intelligenz und Echtzeitsysteme (Prof. Knoll)
Friedrich-Ludwig-Bauer-Str. 3(5932)/II
85748 Garching b. München
Curriculum Vitae
Robin received his bachelor's degree in Computer Science from the Hochschule Darmstadt in March 2015 and his master's degree in Computer Science from the University of Stuttgart in August 2017. Beyond that, Robin stayed at UMass Lowell (USA) for one semester during his bachelor's and studied robotics for one year at Oregon State University (USA) during his master's.
In October 2019 Robin joined the Chair of Robotics, Artificial Intelligence and Real-time Systems at the Informatics department as a research assistant. He is working on the neural basis of localization and mapping in mammlian brains in order to use recent findings from neuroscience to build new algorithms for mobile robot navigation. His work is specifically focused on the identification and analysis of temporal dynamics in the hippocampus and its potential application in a neuromorphic SLAM.
Thesis and Project Offers
If you are interested in a thesis or semester project position in the overlapping field of robotics, artificial intelligence and computational neuroscience then have a look at the current offers or contact me to discuss your personal preferences. When contacting me via email, please add "[thesis]" at the beginning of the subject of your email. Emails without this included will neither be read nor answered.
Thesis or Projects
| Type | Field / Keywords | Title | Date |
Open | | | | |
| | | | |
Ongoing | Master Thesis | Spiking Neural Networks, Egde Offloading, Autonomous Driving | Edge Offloading Strategies for Autonomous Driving Systems Using Spiking Neural Networks | 10/2023 |
| | | | |
Completed | Bachelor Thesis | Spatial Cells, Spiking Neural Networks, Mobile Robot Navigation | Exploring Spatial Cell Dynamics in Spiking Neural Networks Trained on a Navigation Task | 08/2023 |
| Master Thesis | Spiking Neural Networks, Uncertainty Quantification, Drones | Uncertainty Quantification in Spiking Neural Networks for Drone Landings | 05/2022 |
| Master Thesis | Edge Computing, Object Detection, AMRs | Analysis of Edge Computing Offloading Strategies for Autonomous Mobile Robots | 05/2023 |
| Master Thesis | Evolutionary Optimization, Spiking Neural Networks, Radar Data Clustering | Evolutionary Optimization for Spiking Neural Networks - Clustering of FMCW Radar data | 11/2022 |
| Master Thesis | Reward Based Learning, Neuromorphic Engineering, AI, Radar Data Processing | Biologically Inspired Person Following Using FMCW Radar Data | 11/2022 |
| Bachelor Thesis | Neuromorphic Engineering, AI, Radar Data Processing, Object Tracking | Biologically Inspired Neuromorphic Object Tracking Using FMCW Radar Data | 09/2022 |
| Master Thesis | Spiking Neural Networks, RBF Neurons, Radar Data Processing | Biologically Inspired Spiking Clustering for Autonomous Driving | 05/2022 |
| Master Thesis | Spiking Neural Networks, Attractor Networks, Radar Data Processing | A Bio-Inspired Spiking Model for Object Tracking in FMCW-Radar Data | 02/2022 |
| Master Thesis | Multi-Robot Navigation, Safety, Path Planning | A comparative study on the ability of a mobile robot to safely navigate through diverse dynamic environments | 02/2022 |
| Guided Research | Multi-Robot Exploration, SLAM | Co-Explore a novel multi-robot exploration metric and framework | 12/2021 |
| Semester Thesis | Deep Learning, Object Detection, Mobile Robot | Object Detection for an Autonomous Trash Picking Robot | 05/2021 |
Teaching
SS 2023 | |
Lecture | Digital Signal Processing |
Seminar | Bio-inspired Data Processing and Learning With Neural Networks |
WS 2022/23 | |
Lecture | Real-Time Systems |
SS 2022 | |
Lecture | Digital Signal Processing |
Seminar | Bio-inspired Data Processing and Learning With Neural Networks |
WS 2021/22 | |
Lecture | Real-Time Systems |
SS 2021 | |
Lecture | Digital Signal Processing |
Seminar | Bio-inspired Data Processing and Learning With Neural Networks |
WS 2020/21 | |
Lecture | Real-Time Systems |
Practical Course | Intelligent Mobile Robots With ROS |
SS 2020 | |
Lecture | Digital Signal Processing |
Seminar | Bio-inspired Data Processing and Learning With Neural Networks |
WS 2019/20 | |
Lecture | Real-Time Systems |