My research interests lie in the intersection between deep learning and computational neuroscience. The following are some of my published works and articles. Note: * denotes equal contributions.

Thesis

Bryan M. Li. Synthesising Realistic Calcium Imaging Data of Neuronal Populations using Deep Generative Models. Master of Science by Research dissertation, awarded with Distinction. 2020.

Conference paper

Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Lukasz Kaiser. CipherGAN: Unsupervised Cipher Cracking Using Neural Networks. International Conference on Learning Representations. 2018.

Workshop paper

Aidan N. Gomez, Sicong Huang, Ivan Zhang, Bryan M. Li, Muhammad Osama, Lukasz Kaiser. Unsupervised Cipher Cracking Using Discrete GANs. Neural Information Processing Systems DISCML. 2017.

Journal

Bryan M. Li, Alexander Cowen-Rivers, Piotr Kozakowski, David Tao, Siddhartha Rao Kamalakara, Nitarshan Rajkumar, Hariharan Sezhiyan, Sicong Huang, Aidan N. Gomez. RL: A generic reinforcement learning codebase in TensorFlow. Journal of Open Source Software. 2019.

Preprint

Bryan M. Li, Theoklitos Amvrosiadis, Nathalie Rochefort, Arno Onken. CalciumGAN: A Generative Adversarial Network Model for Synthesising Realistic Calcium Imaging Data of neuronal populations. 2020.

Jonathon Wong*, Anthony Kwan*, Bryan M. Li*, Vinod Muthusamy, Hans-Arno Jacobsen. Improving Predictions in Cloud Data center Workloads using Neural Networks and Online Learning. 2020.

Blog post

Multi-GPUs and custom training loops in TensorFlow 2. Towards Data Science. 2021.

A Transformer Chatbot Tutorial with TensorFlow 2.0. TensorFlow Blog. 2019.

Work in news

Powerful AI Algorithm That Cracks Encrypted Messages Could Help Facebook and Google Translate Human Language. Newsweek. 2018.

Cracking the code: This group of U of T computer science researchers are decoding ciphers with AI. University of Toronto News. 2017.