Biography

I am an Assistant Professor at the Zaven & Sonia Akian College of Science and Engineering, American University of Armenia. I am also the Head of the "Intelligent Systems and Advanced Control" Basic Research Laboratory at the National Polytechnic University of Armenia and a Leading Researcher at the Center for Scientific Innovation and Education.

I earned my B.Sc. in Automation and Control from the National Polytechnic University of Armenia in 2018, and my M.Sc. and Ph.D. in Electrical and Computer Engineering from Seoul National University in 2020 and 2023, respectively. I received the Distinguished ECE M.S. Dissertation Award and the Distinguished ECE Ph.D. Dissertation Award from Seoul National University.

My research focuses on control and optimization, motion planning, distributionally robust and risk-aware decision-making, and safe autonomous systems operating under uncertainty.

Research Interests

  • Safety in Autonomous Systems
  • Distributionally Robust Optimization and Control
  • Motion Planning and Control

Publications

  1. M. Jang, J. Lee, A. Hakobyan, N. Hovakimyan, and I. Yang, “Residual-aware distributionally robust EKF: Absorbing linearization mismatch via Wasserstein ambiguity,” arXiv preprint arXiv:2604.02749, 2026 (accepted for presentation at IEEE CDC 2026).
  2. A. Hakobyan, A. Nazarians, A. Gahlawat, N. Hovakimyan, and I. Kolmanovsky, “Distributionally robust planning with L1 adaptive control,” arXiv preprint arXiv:2603.28758, 2026 (accepted for presentation at IEEE CDC 2026).
  3. M. Jang, A. Hakobyan, and I. Yang, “Distributionally robust Kalman filter,” arXiv preprint arXiv:2512.06286, 2025.
  4. M. Jang, A. Hakobyan, and I. Yang, “On the steady-state distributionally robust Kalman filter,” in 2025 IEEE 64th Conference on Decision and Control (CDC), 2025, pp. 2385–2392.
  5. A. Cherukuri, A. Dixit, and A. Hakobyan, “Distributionally robust and risk-averse model predictive control for motion planning and control: Reformulations and computational issues,” in Nonlinear and Constrained Control: Applications, Synergies, Challenges and Opportunities, Springer, 2025, pp. 151–179.
  6. J. Minhyuk, A. Hakobyan, and I. Yang, “Wasserstein distributionally robust control and state estimation for partially observable linear systems,” arXiv preprint arXiv:2406.01723.
  7. A. Hakobyan and I. Yang, “Wasserstein distributionally robust control of partially observable linear stochastic systems,” IEEE Transactions on Automatic Control (TAC), 2024.
  8. J.M. Nadales, A. Hakobyan, D.M. de la Peña, D. Limon, and I. Yang, “Risk-aware Wasserstein distributionally robust control of vessels in natural waterways,” IEEE Transactions on Control Systems Technology (TCST), vol. 32, no. 4, pp. 1471-1478, 2024.
  9. A. Hakobyan and I. Yang, “Distributionally robust differential dynamic programming with Wasserstein distance,” IEEE Control Systems Letters (L-CSS), vol. 7, pp. 2329-2334, 2023 (presented at CDC 2023).
  10. A. Hakobyan and I. Yang, “Distributionally robust optimization with unscented transform for learning-based motion control in dynamic environments,” in IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 3225–3232.
  11. A. Hakobyan and I. Yang, “Distributionally robust risk map for learning-based motion planning and control: A semidefinite programming approach,” IEEE Transactions on Robotics (T-RO), vol. 39, no. 1, pp. 718-737, 2023.
  12. A. Hakobyan and I. Yang, “Wasserstein distributionally robust control of partially observable linear systems: Tractable approximation and performance guarantee,” in IEEE Conference on Decision and Control (CDC), 2022, pp. 4800–4807.
  13. J. Shin, A. Hakobyan, M. Park, Y. Kim, G. Kim, and I. Yang, “Infusing model predictive control into meta-reinforcement learning for mobile robots in dynamic environments,” IEEE Robotics and Automation Letters (RA-L), vol. 7, no. 4, pp. 10065-10072, 2022 (presented at IROS 2022).
  14. A. Hakobyan and I. Yang, “Toward improving the distributional robustness of risk-aware controllers in learning-enabled environments,” in IEEE Conference on Decision and Control (CDC), 2021, pp. 6024–6031.
  15. A. Hakobyan and I. Yang, “Wasserstein distributionally robust motion control for collision avoidance using conditional value-at-risk,” IEEE Transactions on Robotics (T-RO), vol. 38, no. 2, pp. 939–957, 2021.
  16. A. Hakobyan and I. Yang, “Learning-based distributionally robust motion control with Gaussian processes,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020, pp. 7667–7674.
  17. A. Hakobyan and I. Yang, “Wasserstein distributionally robust motion planning and control with safety constraints using conditional value-at-risk,” in IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 490–496.
  18. A. Hakobyan, G. C. Kim, and I. Yang, “Risk-aware motion planning and control using CVaR-constrained optimization,” IEEE Robotics and Automation Letters (RA-L), vol. 4, no. 4, pp. 3924–3931, 2019 (presented at IROS 2019).

Awards and Scholarships

  • Distinguished ECE Ph.D. Dissertation Award (2023)
  • SNU Global Scholarship (2022, Spring)
  • SNU Development Fund Scholarship (2021, Fall)
  • SNU Global Scholarship (2021, Spring)
  • Distinguished ECE M.S. Dissertation Award (2020)
  • Korean Government Scholarship Program, KGSP (2018-2020)