My research interests lie in the intersection between deep learning and computational neuroscience. The following are some of my published works and articles. You can also find my profile on Google Scholar and Semantic Scholar.

Research

Bryan M. Li*, Filippo Corponi*, Gerard Anmella, Ariadna Mas, Miriam Sanabra, Isabella Pacchiarotti, Marc Valentí, Anna Giménez-Palomo, Marina Garriga, Isabel Agasi, Anna Bastidas, Tabatha Fernández-Plaza, Néstor Arbelo, Myriam Cavero, Clemente García-Rizo, Miquel Bioque, Norma Verdolini, Santiago Madero, Andrea Murru, Iria Grande, Silvia Amoretti, Victoria Ruiz, Giovanna Fico, Michele De Prisco, Vincenzo Oliva, Eduard Vieta, Diego Hidalgo-Mazzei. Can machine learning with data from wearable devices distinguish disease severity levels and generalise across patients? A pilot study in Mania and Depression. 2022.
Bryan M. Li, Leonardo V. Castorina, Maria del C. Valdés-Hernández, Una Clancy, Stewart J. Wiseman, Eleni Sakka, Amos J. Storkey, Daniela Jaime Garcia, Yajun Cheng, Fergus Doubal, Michael T. Thrippleton, Michael Stringer, Joanna M. Wardlaw. Deep Attention Super-Resolution of Brain Magnetic Resonance Images Acquired Under Clinical Protocols. Frontiers in Computational Neuroscience. 2022.
Bryan M. Li, Theoklitos Amvrosiadis, Nathalie Rochefort, Arno Onken. Neuronal Learning Analysis using Cycle-Consistent Adversarial Networks. 2021.
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.
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 (JOSS). 2019.
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 (ICLR). 2018.

Thesis

Neuronal Learning Analysis using Cycle-Consistent Adversarial Networks. Biomedical AI CDT MSc by Research (awarded with Distinction). University of Edinburgh. 2021.
Synthesising Realistic Calcium Imaging Data of Neuronal Populations using Deep Generative Models. MSc by Research (awarded with Distinction). University of Edinburgh. 2020.

Talk

Identifying digital biomarkers of illness activity and treatment response in bipolar disorder. Oral presentation at UKRI AI CDTs in Healthcare Conference 2022.

Blog post

Accelerated TensorFlow model training on Intel Mac GPUs. Towards Data Science. 2021.
Multi-GPUs and custom training loops in TensorFlow 2. Towards Data Science. 2021.
A Transformer Chatbot Tutorial with TensorFlow 2.0. TensorFlow Blog. 2019.

Teaching

Tutor & Marker. INFR11130 Machine Learning and Pattern Recognition. University of Edinburgh. 2021 Autumn.
Teaching Assistant. INFR11077 MSc Dissertation (Informatics). University of Edinburgh. 2020 Sping.
Teaching Assistant. CSC258H1 Computer Organization. University of Toronto. 2018 Winter.
Teaching Assistant. CSC120H1 Computer Science for the Sciences. University of Toronto. 2017 Fall.

Work in news

AI startup Cohere launches a nonprofit research lab. TechCrunch. 2022.
Introducing: Cohere For AI. Co:here. 2022.
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.