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.
After my PhD, I was a post-doctoral fellow in the
Computational Vision and Imaging Lab (CVIL) at York University,
working with
Prof. Marcus Brubaker
and
Prof. Kosta Derpanis
on 3D scene inpainting and generative models of multiview image sets.
Currently, I am a senior AI research scientist at the
Samsung Artificial Intelligence Centre (SAIC) in Toronto,
supervised by
Dr. Alex Levinshtein.
I have worked on various aspects of computer vision, including neural 3D scene editing,
but more recently have focused on generative modelling and super-resolution of images.
Research Interests
My research interests lie in
artificial intelligence,
particularly computer vision.
My previous work largely focused on representation learning of 3D shape
(i.e., how geometry can best be modelled for downstream tasks),
with an eye towards disentanglement, weak supervision, inference from images, and generative manipulation.
Such approaches can be useful for intelligent agents,
such as
for intuitive physics simulations or planning in reinforcement learning,
but also for creative applications
(e.g., 3D scene editing).
In general, I am interested in conditional generative modelling
for solving inverse problems in computer vision.
My more recent focus has been on image super-resolution,
particularly the amelioration of generative hallucinations,
while preserving efficiency and image quality.
I am also interested in applying AI techniques
to biological modelling and medical informatics
(e.g., molecular generation, medical/biological 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
