Jesse Sun Email & Phone Number

ML platforms, data engineering

Waterloo, Ontario, Canada

• Research in computational combinatorial optimization with applications in cooperative game theory and uncertainty quantification.
• Developed an efficient Python package to empirically study and test bounds on the minimum composition norms of Choquet capacities on product spaces by solving randomized linear programs.
• Supervised by David Saunders & Mario Ghossoub.

Recommendation systems & feature store

Waterloo, Ontario, Canada

• Researched normalizing flows and generative quantile models for probabilistic time-series forecasting and anomaly detection - conducted experiments in PyTorch on synthetic distributions, image, and time-series datasets.
• Published first-author research on a new family of deep quantile networks bridging optimal transport theory at ICLR 2022's Deep Generative Models for Highly Structured Data workshop.
• Advised by Yaoliang Yu.

Toronto, Ontario, Canada

• Developed a self-supervised learning method on 3D point clouds using tools from persistent homology theory.
• Trained state-of-the-art 3D point cloud neural networks on HPC GPU clusters leveraging Slurm scheduler.
• Advised by Bo Wang.

Toronto, Ontario, Canada

• Built out data pipelines in the HPC clusters for transforming magnetic resonance images (MRI) from DICOM format into pre-processed PyTorch tensors.
• Developed and trained a novel U-Net architecture on the processed images for automatic scar quantification on GPU clusters.
• Advised by Bo Wang.

Toronto, Ontario, Canada

• Led research project in deep image segmentation models for automatic ventricular segmentation from MR images.
• Advised by Bo Wang.