About Me


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!

Research Interest

  • Reinforcement learning (RL): Multi-agent RL, model-based RL, and hierarchical RL;
  • Biologically-inspired machine learning: Spiking neural networks, and biologically-inspired learning rules;
  • Deep learning: Alternatives to backpropagation, theories of deep neural networks, neural ODEs;
  • AI alignment: Reward modelling.


Research Experience

PhD Student (supervised by David Krueger)

Sep 2022 - Present
Computational and Biological Learning Lab at The University of Cambridge
The United Kingdom
  • Investigated methods to train an RL agents to learn how to plan.

Researcher (supervised by Andrew Barto)

Jan 2020 - Jun 2022
University of Massachusetts Amherst
  • Investigated methods to train a deep artificial neural network (ANN) without backpropagation based on RL methods;
  • Proposed two novel algorithms called MAP Propagation (Chung, 2021) and Weight Maximization (Chung, 2022) that can train a deep ANN to solve standard RL tasks efficiently without any backpropagation;
  • Proved the theoretical properties of both algorithms and conducted various experiments.

Researcher (supervised by Hava Siegelmann)

Aug 2020 - Aug 2021
BINDS Lab at The University of Massachusetts Amherst
  • Prepared part of the proposal for a grant from DARPA on applying AI in intelligent hardware simulation;
  • Proposed new theorems and constructed proof on the Turing completeness of recurrent neural networks.


M.S. in Computer Science

Aug 2019 - Jun 2021
University of Massachusetts Amherst
  • Artificial Intelligence: Machine Learning, Reinforcement Learning, Computer Vision, Natural Language Processing
  • Computational Neuroscience: Neurodynamic, Spiking Neural Network
  • Miscellaneous: Quantum Information Systems, Advanced Algorithms

B.S. in Quantitative Finance

Sep 2012-Jun 2016
The University of Hong Kong
Hong Kong
  • Mathematics & Statistics: Real Analysis, Linear Algebra, Multivariable Calculus, Probability Modeling, Statistical Inference, Stochastic Calculus, Markov Chain
  • Computer Science: Computer Programming, Algorithms, Spreadsheet Finance Modeling
  • Finance: Corporate Finance, Investment, Derivatives, Financial Statement Analysis, Mathematical Finance

Exchange Student

London School of Economics and Political Science
United Kingdom