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Wednesday, October 19, 2022 1:00 pm - 2:00 pm EDT (GMT -04:00)

PhD Seminar • Algorithms and Complexity • Cheeger Inequalities for Vertex Expansion and Reweighted Eigenvalues

Please note: This PhD seminar will take place in DC 2310.

Kam Chuen (Alex) Tung, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Lap Chi Lau

The classical Cheeger’s inequality relates the edge conductance of a graph and the second smallest eigenvalue of the Laplacian matrix. Recently, Olesker-Taylor and Zanetti discovered a Cheeger-type inequality connecting the vertex expansion of a graph and the maximum reweighted second smallest eigenvalue of the Laplacian matrix.

Thursday, October 20, 2022 1:00 pm - 2:00 pm EDT (GMT -04:00)

PhD Seminar • Human-Computer Interaction • Independence & Technology: Experiences of Older Adults and Caregivers During the COVID-19 Pandemic

Please note: This PhD seminar will take place online.

Nabil Bin Hannan, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Edith Law

Thursday, October 20, 2022 4:00 pm - 5:00 pm EDT (GMT -04:00)

PhD Seminar • Machine Learning | Self-driving AI • A lecture on “Classifying travelers’ driving style using basic safety messages generated by connected vehicles: Application of unsupervised machine learning”

Please note: This PhD seminar will take place in DC 2568.

Connor Raymond Stewart, PhD candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Krzysztof Czarnecki, Paulo Alencar

Friday, October 28, 2022 1:00 pm - 2:00 pm EDT (GMT -04:00)

Seminar • Algorithms and Complexity • New Lower Bounds for Set Multilinear Formulas

Please note: This seminar will take place in DC 1304 and online.

Shubhangi Saraf, Associate Professor
Department of Mathematics, Department of Computer Science, University of Toronto

The recent exciting breakthrough by Limaye, Srinivasan, and Tavenas showing superpolynomial lower bounds for constant-depth algebraic circuits has underscored the importance of studying the complexity of set multilinear formulas.