News:
(7/01/2024) In this Spring'24 term, I welcome all interested students for the "Reinforcement Learning" course , which will be offered for the first time in the history of our university. In the course, the mathematical derivations of the relevant algorithms and the logic/motivation behind them will be explained in full detail from a control engineering perspective.
(20/04/2023) I am looking for motivated and determined master/PhD students (graduated from good universities with a good GPA) to work in the following fields: system identification, control, reinforcement learning, machine learning and optimization. For contact: ercan.atam@bogazici.edu.tr
(15/02/2022) I have started to write a book on "reinforcement learning" targeting at the following aspects: (i) including all the fundamentals, (ii) easy-to-follow, (iii) including mathematical derivations, (iv) access to Matlab/Python code for the examples in the book. The draft chapters will be available here for your valuable feedback.
I am a Professor at the Institute for Data Science and Artificial Intelligence at Boğaziçi University.
My research focuses on developing and applying cutting-edge methodologies in control, optimization, and artificial intelligence to address a wide range of challenging problems with the potential for significant societal impact.
My current research interests include the following:
Control:
RL for music.
RL, MPC, LPV, or Nonlinear Control-based advanced control of unmanned aerial vehicles (UAVs).
Ensemble control systems
Optimal co-design (simultaneous system optimization and control) for large-scale and complex dynamical systems (e.g. optimal thermal zoning of large-scale office buildings)
Structural control for seismic protection of buildings
Optimization:
Stochastic programming
Robust optimization
Approximation methods for large-scale MILPs
Artificial intelligence:
Machine learning, deep learnning
Ensemble learning
Physics-informed neural networks (PINNs)
AI applications in social sciences
AI applications in art
AI for scientometrics