Assistant Professor of Computer Science
- 2018, Doctoral Degree, Robotics, Carnegie-Mellon University
- 2014, Master's Degree, Aeronautics and Astronautics, Massachusetts Institute of Technology
- 2009, Master's Degree, Precision Engineering, University of Tokyo
- 2006, Bachelor's Degree, Electrical and Computer Engineering, National Technical University of Athens
Stefanos Nikolaidis is an Assistant Professor of Computer Science at the University of Southern California. His research draws upon expertise on artificial intelligence, human-robot interaction and stochastic optimization and leads to end-to-end solutions that enable deployed robotic systems to act robustly when interacting with people in practical, real-world applications. Previously, Stefanos completed his PhD at Carnegie Mellon's Robotics Institute and received his MS from MIT. He has also a MEng from the University of Tokyo and a BS from the National Technical University of Athens. Stefanos has worked as a research associate at the University of Washington, as a research specialist at MIT and as a researcher at Square Enix in Tokyo. He has received a best Cognitive Robotics paper nomination from the IEEE/RSJ International Conference on Intelligent Robots and Systems, a Best Enabling Technologies Paper Award from the IEEE/ACM International Conference on Human-Robot Interaction in 2015, a best paper nomination from the same conference in 2018 and was a best paper award finalist in the International Symposium on Robotics 2013.
Stefanos Nikolaidis directs the Interactive and Collaborative Autonomous Robotic Systems (ICAROS) lab, which focuses on enabling robots to interact robustly with users in unconstrained and dynamic environments. The ICAROS lab draws upon insights from artificial intelligence, human-robot interaction, procedural content generation and quality diversity optimization to make fundamental advances in both developing interactive robots that assist users in complex, real-world tasks, as well as in generating complex, diverse and realistic scenarios that effectively test the developed systems to enhance their robustness.
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