My name is Stephen Chung. I am now studying as a PhD student at the University of Cambridge, supervised by David Krueger. Before coming to Cambridge, I graduated from the University of Massachusetts Amherst with a master's degree in 2021. My primary research interest includes reinforcement learning (RL), biologically-inspired machine learning, and deep learning.
During my master's years, I was supervised by Andrew Barto. Under his guidance, I studied methods to train a deep neural network without backpropagation efficiently. Despite being used in almost all deep learning methods, backpropagation is generally regarded as being biologically implausible. A more biologically plausible way of training a deep network is through treating each unit in the network as an RL agent that receives the same reward signal, but the learning speed of this method is very slow. Therefore, I investigated methods that can speed up learning while retaining the biological plausibility of this learning method. To this end, I proposed two novel algorithms: MAP Propagation (Chung, 2021) and Weight Maximization (Chung, 2022). I argue that both algorithms, built on RL methods, are more biologically plausible than backpropagation while maintaining a similar learning speed. As such, these algorithms may shed light on biological learning and substitute backpropagation in training deep learning models. You can read more about this research here!
As for my interest, I love reading Western and Chinese philosophy books, such as Zhuangzi and Nietzche. I enjoy thinking about the world and philosophical questions. I also like playing tennis and hiking!