Our research focus is on prediction and control of complex systems, whose dynamics are hard to describe, predict, or control. In particular, we are interested in control theory, game theory, and reinforcement learning, on the theory side, and their applications to aerospace, automotive, and robotics fields, on the engineering side. 

PROSPECTIVE STUDENTS: Please have a look at our research projects/publications and if you are interested, please send a short email to Yildiray Yildiz (yyildiz@bilkent.edu.tr) describing your research interests together with your CV. Undergraduates are also welcome. If you are a good programmer, please indicate that in your email (this is NOT a requirement). 

OPEN POSTDOCTORAL POSITIONS: We seek candidates with strong programming skills together with an interest in reinforcement learning and game theory. We are particularly interested in candidates who can apply his/her computer skills on solving problems in prediction of the behavior of complex dynamical systems. Please contact Yildiray Yildiz (yyildiz@bilkent.edu.tr) if you are interested.


Our paper, titled "Cyber-Physical Human Systems," prepared in collaboration with MIT, is accepted for publication at Encyclopedia of Systems and Control. 13 July 2020

Systems Lab member Shahab Tohidi's collaborative work with the University of Michigan, about control allocation for uncertain systems, is accepted for publication at Automatica. 22 June 2020

Systems Lab member Emre Eraslan's collaborative work with MIT is accepted for publication at IEEE Control Systems magazine. The title of the paper is "Shared Control Between Pilots and Autopilots: Illustration of a Cyber-Physical Human System". 14 April 2020

Systems Lab member Shahab Tohidi's research results on stability properties of closed loop control systems in the presence of operator dynamics, when there are redundant and uncertain actuators, will be presented at 2020 IFAC World Congress in Berlin, this July. 

Our paper on human reaction modeling in air and ground traffic, using reinforcement learning and game theory, became one of the most downloaded articles from Annual Reviews in Control in the last 90 days. To access the paper, click here. To see a list of most-downloaded papers, click here.  
25 February 2020