The following are some of my published works and articles, * denote equal contribution. You can also find my profile on Google Scholar and Semantic Scholar.

Research

Filippo Corponi, Bryan M. Li, Gerard Anmella, Clàudia Valenzuela-Pascual, Ariadna Mas, Isabella Pacchiarotti, Marc Valentí, Iria Grande, Antonio Benabarre, Marina Garriga, Eduard Vieta, Allan H Young, Stephen M. Lawrie, Heather C. Whalley, Diego Hidalgo-Mazzei, Antonio Vergari. Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning. 2023.
Gerard Anmella, Ariadna Mas, Miriam Sanabra, Clàudia Valenzuela-Pascual, Marc Valentí, Isabella Pacchiarotti, Antoni Benabarre, Iria Grande, Michele De Prisco, Vincenzo Oliva, Giovanna Fico, Anna Giménez-Palomo, Anna Bastidas, Isabel Agasi, Allan H. Young, Marina Garriga, Filippo Corponi, Bryan M. Li, Peter de Looff, Eduard Vieta, Diego Hidalgo-Mazzei. Electrodermal activity in bipolar disorder: Differences between mood episodes and clinical remission using a wearable device in a real-world clinical setting. Journal of Affective Disorders. 2023.
Filippo Corponi*, Bryan M. Li*, Gerard Anmella, Ariadna Mas, Miriam Sanabra, Eduard Vieta, INTREPIBD Group, Stephen M. Lawrie, Heather C. Whalley, Diego Hidalgo-Mazzei, Antonio Vergari. Automated mood disorder symptoms monitoring from multivariate time-series sensory data: Getting the full picture beyond a single number. 2023.
Gerard Anmella*, Filippo Corponi*, Bryan M. Li*, Ariadna Mas, Miriam Sanabra, Isabella Pacchiarotti, Marc Valentí, Iria Grande, Antoni Benabarre, Anna Giménez-Palomo, Marina Garriga, Isabel Agasi, Anna Bastidas, Myriam Cavero, Tabatha Fernández-Plaza, Néstor Arbelo, Miquel Bioque, Clemente García-Rizo, Norma Verdolini, Santiago Madero, Andrea Murru, Silvia Amoretti, Anabel Martínez-Aran, Victoria Ruiz, Giovanna Fico, Michele De Prisco, Vincenzo Oliva, Aleix Solanes, Joaquim Radua, Ludovic Samalin, Allan H. Young, Eduard Vieta, Antonio Vergari, Diego Hidalgo-Mazzei. Exploring digital biomarkers of illness activity in mood episodes: hypotheses generating and model development study. Journal of Medical Internet Research (JMIR) mHealth and uHealth. 2023.
Bryan M. Li, Isabel M. Cornacchia, Nathalie L. Rochefort, Arno Onken. V1T: large-scale mouse V1 response prediction using a Vision Transformer. Transactions on Machine Learning Research (TMLR). 2023.
Bryan M. Li*, Filippo Corponi*, Gerard Anmella, Ariadna Mas, Miriam Sanabra, Diego Hidalgo-Mazzei, Antonio Vergari. Inferring mood disorder symptoms from multivariate time-series sensory data. NeurIPS Workshop on Learning from Time Series for Health. 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

Dynamic V1 response prediction with a video Transformer. Short talk at NeurIPS Sensorium Competition workshop. 2023.
Identifying digital biomarkers of illness activity and treatment response in bipolar disorder. Oral presentation at UKRI AI CDTs in Healthcare Conference. 2022.

Blog post

A batch too large: Finding the batch size that fits on GPUs. Towards Data Science. 2022.
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 & demonstrator. INFD11005 Introductory Applied Machine Learning. University of Edinburgh. 2023 Winter.
Tutor & marker. INFR11132 Machine Learning Practical. University of Edinburgh. 2023 Winter.
Tutor & demonstrator. INFR11211 Applied Machine Learning. University of Edinburgh. 2022 Autumn.
Tutor & marker. INFR11130 Machine Learning and Pattern Recognition. University of Edinburgh. 2022 Autumn.
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.

Misc

Discover the Edinburgh Students Joining the Turing Student Enrichment Scheme. Bayes Centre, University of Edinburgh. 2023.
C4AI Special - Grad School Applications. Cohere For AI. 2022.
Introducing: Cohere For AI. Cohere For AI. 2022.
Cracking the code: This group of U of T computer science researchers are decoding ciphers with AI. University of Toronto News. 2017.