Summary
I am a Ph.D. student at Imperial College London, where I am a member of Systems and Algorithms Laboratory (SysAL).
My research interests mainly include Trustworthy Autonomous System Design through Formal Guarantees on Interpretable reinforcement learning and optimal control theories, and its application on real aerial robots. Particularly, I am interested in the theories and algorithms that empower machine to see and think more intelligent. In addition to scientific research, I love classic music and sports!
If you are interested in my research, please move to see my personal website.
Visit Yanran's Google Scholar page.
Education
2021.05 - NOW: Ph.D candidate, in Dyson School for aerial robotics, Imperial College London.
2017.09 - 2020.03: M.S. in information and control, Shanghai Jiao Tong University.
2013.09 - 2017.06: B.S. in Automation, Southest University.
Publications
Journals
Xiao G, Wang Y, He F, 2019, Research on safety modeling and analysis in information fusion system, Aerospace Systems, Vol:2, ISSN:2523-3947, Pages:51-60
Wang Y, Xiao G, Dai Z, 2017, Integrated display and simulation for automatic dependent surveillance–broadcast and traffic collision avoidance system data fusion, Sensors, Vol:17, ISSN:1424-8220, Pages:1-23
Conference
Wang Y, Qian Q, O'Keeffe J, et al., 2023, QuaDUE-CCM: Interpretable Distributional Reinforcement Learning using Uncertain Contraction Metrics for Precise Quadrotor Trajectory Tracking, Pages:2306-2316
Wang Y, O'Keeffe J, Qian Q, et al., 2022, KinoJGM: A framework for efficient and accurate quadrotor trajectory generation and tracking in dynamic environments, Pages:11036-11043, ISSN:1050-4729
Wang Y, O'Keeffe J, Qian Q, et al., 2022, Interpretable Stochastic Model Predictive Control using Distributional Reinforced Estimation for Quadrotor Tracking Systems, Pages:3335-3342, ISSN:0743-1546