Dongyan Lin is a third-year PhD at Integrated Program in Neuroscience at McGill University and Mila.
What is your previous degree in? Where did you earn it from?
I graduated from the University of Toronto with a BSc in 2019 where I majored in Physiology and double-minored in Mathematics and Psychology.
Describe your research and the implications of your project in three sentences or less.
My research is focused on the interplay between neuroscience and artificial intelligence (AI). Specifically, I am interested in understanding how memory is stored and used for learning in biological and artificial intelligence systems as well as developing novel machine learning tools to analyze neural data. This will advance neuroscience and AI in tandem; AI can provide useful models and tools to help us decipher the brain while understanding the principles of biological intelligence will bring us closer to human-level AI.
What inspired you to pursue your current degree?
I have always been fascinated by human intelligence—how we learn, how we make decisions, what makes us who we are—which propelled me toward neuroscience research during my undergraduate studies. I believed that only through recreation can we truly push the limits of our understanding of intelligence. This inspired me to embark on the journey of “building the brain.” My research allows me to explore and combine a wide range of disciplines, from computer science to neurobiology, cognitive science to philosophy. Currently, I am working on developing AI models for the hippocampus and the visual cortex that not only exhibit brain-like activity but also mimic the behaviours of animals during cognitive tasks.
What are the biggest challenges in your field right now? How are you working to overcome them?
Despite the rapid advancement of AI technology, we’re still challenged by the lack of representation of women, BIPOC, and LGBTQ2A+ individuals in the field of AI research. These disparities contribute to the unconscious biases in our research output, which can have a detrimental effect on broader society. For example, many AI algorithms used for facial recognition were trained on predominantly male and Caucasian data, causing these algorithms to be incapable of detecting female or dark-skinned faces. To tackle this, I have been involved in multiple initiatives that aim to provide equitable opportunities to underrepresented groups, such as the AI4Good summer school where I had the fortune to supervise aspiring female scientists through the development of AI prototypes that can benefit society.
What are your favourite things to do outside the lab?
During the pandemic, acrylic painting and baking became my creative outlets as well as a new way to relax. Before that, I loved to travel around the world solo or with friends and family.
What have you accomplished this year that you are most proud of?
I am very proud that I tried many new things for the first time, such as playing ukulele in front of an audience, dying my hair blue and running for student council at my institute.
