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

Pablo Millán Arias, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Lila Kari

Please note: This master’s thesis presentation will take place in DC 3102.

Kamyar Ghajar, Master’s candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Mark Smucker, Charles Clarke

Please note: This master’s thesis presentation will take place online.

Luyun Lin, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jimmy Lin

Please note: This PhD seminar will take place in DC 3317 and online.

Ehsan Ganjidoost, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jeff Orchard

Please note: This seminar will take place in M3 4206 and online.

Rachel Yun Zhang, PhD student
CSAIL, Massachusetts Institute of Technology

A code, which is a set of strings called codewords, is locally testable if one can test whether a given word is close to a codeword by reading only a few bits. Locally testable codes have been studied since the 1990s as key ingredients in the construction of probabilistically checkable proofs.

Please note: This seminar will take place in MC 5501 and online.

Mika Göös, Assistant Professor
Theory Group, École polytechnique fédérale de Lausanne

We present a top-down lower-bound method for depth-4 Boolean circuits. In particular, we give a new proof of the well-known result that the parity function requires depth-4 circuits of size exponential in n^{1/3}. Our proof is an application of robust sunflowers and block unpredictability.

Joint with Artur Riazanov, Anastasia Sofronova, and Dmitry Sokolov.

Please note: This PhD seminar will take place in E5 4047.

Murray Dunne, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Sebastian Fischmeister

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

Han Zhao, Assistant Professor
Computer Science, University of Illinois Urbana-Champaign
Amazon Visiting Academic, Amazon AI and Search Science

To mitigate the bias exhibited by machine learning models, fairness criteria can be integrated into the training process to ensure fair treatment across all demographics, but it often comes at the expense of model performance. Understanding such tradeoffs, therefore, underlies the design of optimal and fair algorithms.

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

Marina Meila
Department of Statistics, University of Washington
Senior Fellow, University of Washington’s eScience Institute

Please note: This master’s thesis presentation will take place online.

Jack Spalding-Jamieson, Master’s candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Anna Lubiw, Therese Biedl