Tristan A.A.
Tristan Ty Aumentado-Armstrong

I am currently a research scientist in artificial intelligence at the Samsung Artificial Intelligence Centre in Toronto and a post-doctoral fellow at York University.

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Research History

My initial research work was in structural molecular bioinformatics, including macromolecular imaging via transmission electron microscopy and protein surface interface site prediction.

I then spent time in the Computational Systems Neuroscience Lab of Prof. Maurice Chacron, simulating and analysing sensory pathway midbrain cells with invariant recognition properties involved in animal communication. I also interned at the McGill Centre for Integrative Neuroscience (MCIN) within the Montreal Neurological Institute (MNI), working with Prof. Tristan Glatard, on scientific computing tools for neuroinformatics.

Afterwards, I joined the Shape Analysis Group of Prof. Kaleem Siddiqi in the Centre for Intelligent Machines (CIM). I worked on medical computer vision and biophysical modelling of the heart, as well as geometric feature extraction from shapes.

I then entered graduate school, co-supervised by Prof. Sven Dickinson and Prof. Allan Jepson, at the University of Toronto, working on computer vision and artificial intelligence. My thesis encompasses various aspects of shape representation learning, including modelling deformations, weakening supervision, and improving implicit geometry fields.

Currently, I am a research scientist at the Samsung Artificial Intelligence Centre (SAIC) in Toronto, supervised by Dr. Alex Levinshtein. I am also a post-doctoral fellow in the Computational Vision and Imaging Lab (CVIL) at York University, where I work with Prof. Marcus Brubaker and Prof. Kosta Derpanis.

Research Interests

My research interests lie in artificial intelligence, particularly the subfields of computer vision and machine learning. I am especially interested in the representation learning of 3D shape, with an eye towards weak supervision, inference from images, and generative manipulation. This includes modelling various aspects of scenes and objects, such as disentangling attributes (e.g., shape, pose, and appearance) and investigating how geometry can be best represented for downstream tasks. Such approaches can be useful for helping an AI agent model the world, such as for intuitive physics simulations or planning in reinforcement learning, as well as for applications within computer vision and graphics, including creative ones.

I am also interested in applying AI techniques to biological modelling and medical informatics (e.g., molecular generation, medical image processing, evolutionary analysis).

Education Background

  • Doctor of Philosophy (PhD) in Computer Science, University of Toronto, 2024
    • Thesis: On Disentangled Analysis-by-Synthesis Shape Representations
  • Masters of Science (MSc) in Computer Science, University of Toronto, 2018
    • Thesis: Geometric Disentanglement for Generative Latent Shape Models
  • Bachelors of Science (BSc), McGill University, 2016
    • Major: Computer Science | Minor: Mathematics
  • Bachelors of Science (BSc), McGill University, 2014
    • Major: Anatomy and Cell Biology | Minor: Computer Science