This profile is built from public research funding records (CIHR, NSERC and SSHRC). We have not imported them from a McGill University directory, so their publications, courses and email address may be missing. Find their university profile.
Research
Latest funding
- $81,000
Principled approaches to deep learning: generalization under distribution shift and predictive uncertainty
NSERC · 2022 · Principal investigator
- $198,000
Numerical Methods for Nonlinear Partial Differential Equations, with applications to Optimal Transportation, and Geometric Data Reduction
NSERC · 2016 · Principal investigator
- $25,000
High Dimensional Data Reduction using approximate Convex Hulls
NSERC · 2015 · Principal investigator
Principled approaches to deep learning: generalization under distribution shift and predictive uncertainty
Principal investigators: Oberman, Adam
Keywords: machine learning; computer vision; statistical learning theory; uncertainty estimation; generative modeling; loss design
Numerical Methods for Nonlinear Partial Differential Equations, with applications to Optimal Transportation, and Geometric Data Reduction
Principal investigators: Oberman, Adam
High Dimensional Data Reduction using approximate Convex Hulls
Principal investigators: Oberman, Adam
Numerical methods for fully nonlinear and degenerate elliptic partial differential equations
Principal investigators: Oberman, Adam
Numerical methods for geometric partial differential equations: applications to freeform deformations in animation and nonrigid medical image registration
Principal investigators: Oberman, Adam
From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.
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