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Monday, March 8, 2021 2:00 pm - 2:00 pm EST (GMT -05:00)

PhD Seminar • Computer Graphics — Constructing Smooth Surfaces with Continuous Curvatures for Triangular Scattered Data

Please note: This PhD seminar will be given online.

Xiang Fang, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Stephen Mann

A new set of schemes are designed to create smooth surfaces with continuous curvatures or higher order continuity for triangular scattered data sites, without complex computation.

Thursday, March 11, 2021 9:30 am - 9:30 am EST (GMT -05:00)

PhD Seminar • Natural Language Processing — UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus

Please note: This PhD seminar will be given online.

Georgios Michalopoulos, PhD candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Ian McKillop and Helen Chen

Thursday, March 11, 2021 12:00 pm - 12:00 pm EST (GMT -05:00)

Seminar • Machine Learning — Steps Towards Making Machine Learning More Natural

Please note: This seminar will be given online.

Mengye Ren, Department of Computer Science
University of Toronto

Over the past decades, we have seen machine learning making great strides in AI applications. Yet, most of its success relies on training models offline on a massive amount of data and evaluating them in a similar test environment. By contrast, humans can learn new concepts and skills with very few examples, and can easily generalize to novel tasks.

Wednesday, March 17, 2021 11:00 am - 11:00 am EDT (GMT -04:00)

PhD Seminar • Algorithms and Complexity — Geodesic Convexity in Statistics and Sample Complexity Bounds

Please note: This PhD seminar will be given online.

Akshay Ramachandran, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Lap Chi Lau

The matrix normal model, the family of Gaussian matrix-variate distributions whose covariance matrix is the Kronecker product of two lower dimensional factors, is frequently used to model matrix-variate data. The tensor normal model generalizes this family to Kronecker products of three or more factors.