Ni Dang

Ni Dang is a former Research Associate and PhD candidate at the Chair of Automatic Control Engineering (LSR) at Technical University of Munich (TUM), with a focus on advanced control strategies for autonomous vehicles. They earned a Master of Science in Circuits and Systems from Xidian University, China, and a Bachelor of Science in Electronic and Information Engineering from the same institution.

• PhD in Automatic Control Engineering (TUM, 2023)
• M.Sc. in Circuits and Systems (Xidian University, 2018)
• B.Sc. in Electronic and Information Engineering (Xidian University, 2015)

Their research spans Autonomous Vehicles, Model Predictive Control, and Optimization, with interdisciplinary work in Epidemiology (2020) and Network Science (2018). Key contributions include applying Inverse Reinforcement Learning and Imitation Learning to model driving styles, integrating deep learning with control systems, and developing distributed stochastic MPC for traffic models.

Their publications highlight trends in Autonomous Driving, Control Algorithms, and Network Analysis, balancing theoretical and applied approaches. A 2020 paper on a power-law model for pandemic spread demonstrates cross-domain adaptability of their methodologies.