Former Member of the Signal Processing Group at the Institute of Telecommunications, TU Darmstadt. (This page is no longer maintained. Last update: 12/2018)
Office: S3|06 250
work +49 6151 16-21348
fax +49 6151 16-21342
Office Hours: Mondays, 15:30-18:30
Adrian Šošić received his B.Sc. and M.Sc. degrees in Electrical Engineering and Information Technology from Technische Universität Darmstadt in October 2010 and May 2013, respectively. During his studies, he spent time at University College Cork (UCC), Ireland. In his master thesis “Markov Assumptions for Non-negative Matrix Factorization” he investigated how fundamental concepts of linear dynamical systems and non-negative representations can be combined in order to learn parts-based models for sequential data. The representations developed in his thesis can be applied, for instance, in sequence classification, e.g. human action recognition from video data.
In September 2013, Adrian joined the Signal Processing Group at TU Darmstadt and commenced working on his Ph.D.
Adrian's research interests center around topics from modern machine learning, statistical signal processing, image processing, decision-making, reinforcement learning, and game theory. He is especially interested in the methodology of Bayesian inference and his goal is to develop robust inference methods that allow to deal with uncertainty in a principled manner.
Currently he is working on inference methods in (large-scale) multi-agent settings as they appear in many biological systems, e.g. in animal swarms.
Completed Student Projects
|Rong Zhi||Deep Reinforcement Learning under Uncertainty for Autonomous Driving||Master Thesis||ongoing|
|Mahmood Omaira||Deep Neural Networks for Radar-Based Human Gait Recognition||Master Thesis||12/2017|
Burak Celik||Bayesian Nonparametric Clustering||ATISSP Seminar||07/2017|
Benjamin Graf||Learning a Communication Scheme in Homogeneous Multi-Agent Systems||Master Thesis||05/2017|
Sanket Pratap Shinde|
|Partially Observable Markov Decision Processes for Continuous States and Observations for Robotics||Master Thesis||04/2017|
Mengyao Zhang||A Change Detection Approach in Map-Building for Autonomous Vehicles||Master Thesis||02/2017|
Guided Deep Reinforcement|
Learning for Robot Swarms
Frederik Bous & Edin Ragibović||Localisation with the Particle Filter||ATISSP Seminar||07/2016|
Mahmoud El-Hindi||Reinforcement Learning||Proseminar||05/2016|
Romain Gemble||Investigation and Implementation of Algorithms for Music Source Separation||Bachelor Thesis||02/2016|
Sandro Kecanovic||Bayesian Non-negative Matrix Factorization||Bachelor Thesis||12/2015|
Sandro Kecanovic||Non-negative Matrix Factorization techniques||Project Seminar||08/2015|
Benjamin Graf, |
Rosa Maria Carpio López
& Daniel Scheuermann
|Reinforcement Learning for Black Jack||ATISSP Seminar||07/2015|
Zhengqi Qian||Reinforcement Learning in Swarm Systems||Master Thesis||07/2015|
Mateesh Bhave||Learning Class Uncertainties using Neural Networks||Master Thesis||
Sandro Kecanovic||Machine Learning: An Overview||Proseminar||12/2014|
Jun Liu & Mengyao Zhang||Reinforcement Learning||ATISSP Seminar||07/2014|