I am an M.Phil graduated from the Intelligent Driving Lab (iDLab), Tsinghua University. I worked with Prof. Shengbo Li and Prof. Bo Cheng. Currently, my research covers neural network, reinforcement learning, and autonomous driving. I have been committed to building more intelligent and safer AI for the decision-making and control of automated vehicles, and to developing the next generation of diagram for neural network training.
Tsinghua University, 2021-2024
M.Phil in Vehicle Engineering
Minor in Big Data (certificate program)
Delft University of Technology, 2017-2021
Joint Education Program
Beijing Jiaotong University, 2017-2021
B.Eng in Traffic and Transportation
B.Eng in Computer Science and Technology (dual degree)
We propose a smooth policy network (LipsNeXt) and a smooth distributed soft actor-critic (DSAC-S) algorithm to achieve coordinated optimization of control precision and action smoothness.
We propose a class of neural networks with low-pass filtering ability, named Smonet, to alleviate the non-smooth issue by learning a low-frequency representation within hidden layers.
We propose Ising learning algorithm, which is the first technique to train multi-layer feedforward neural networks on Ising machine (a dedicated quantum computer).
we propose a neural ODE network variant, called adaptive liquid-time constant (ALTC), to smooth out control actions. A mapping function is incorporated to estimate the speed of system dynamics.
We propose LipsNet, a smooth and robust neural network with adaptive Lipschitz constant, to deal with the action fluctuation problem in RL (reinforcement learning).