Nemanja Rakicevic obtained his BSc degree in Mechatronics at the Faculty of Technical Sciences, University of Novi Sad in 2011. He completed the double-degree EMARO (European Masters on Advanced Robotics) program and was awarded the MSc degree in 2013.
During 2013-2014, he worked as a research engineer at RIS group, LAAS-CNRS Toulouse on rover locomotion diagnostics using sequential machine learning models.
From 2015 to 2016, he was as a research assistant at iBug, Dept. of Computing, ICL where he worked on applying deep learning methods for human emotion recognition based on facial expressions.
He started his PhD in 2016 in the Robot Intelligence Lab, Dyson School of Design Engineering, ICL.
His research interests lie at the intersection of artificial intelligence and robotics, more specifically representation learning, policy search and deep reinforcement learning for continuous control.
Rakicevic N, Kormushev P, 2019, Active learning via informed search in movement parameter space for efficient robot task learning and transfer, Autonomous Robots, Vol:43, ISSN:0929-5593, Pages:1917-1935
Saputra RP, Rakicevic N, Kormushev P, 2020, Sim-to-real learning for casualty detection from ground projected point cloud data, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), IEEE
et al., 2017, Multi-modal neural conditional ordinal random fields for agreement level estimation, 23rd International Conference on Pattern Recognition (ICPR), IEEE, Pages:2228-2233, ISSN:1051-4651
et al., 2015, Neural Conditional Ordinal Random Fields for Agreement Level Estimation, 6th AAAC Affective Computing and Intelligent Interaction International Conference (ACII), IEEE, Pages:885-890, ISSN:2156-8103