Yu Wu (Ledell)

Theory of Computation Group,
Department of Computer Science,
University of Toronto

Contact

SF 4302C, 10 King's College Rd.
Toronto, Ontario M5S 3G4, Canada
Email: wuyu AT cs DOT toronto DOT edu

Hi! I am a graduate student at the University of Toronto. I'm fortunate to be advised by Toniann Pitassi. Previously, I obtained a M.Sc. from the University of Toronto, and a B.Sc. from School of Mathematical Sciences, Peking University, P.R.China.

(I am currently on leave at Facebook, Inc.)

Research Interest

I am interested in the theory of computation in general. More specifically, my research is focused on approximation algorithms and the hardness of approximation. My other interests include communication complexity, privacy and machine learning.

Publication and Manuscript

Richard Zemel, Toniann Pitassi, Yu Wu, Kevin Swersky and Cynthia Dwork, Learning Fair Representations , ICML-2013: The 30th International Conference on Machine Learning (2013).
Per Austrin, Toniann Pitassi, and Yu Wu, Inapproximability of Treewidth, One-Shot Pebbling, and Related Layout Problems, In International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX), 2012, pp. 13-24, Accepted for publication in Theory of Computing. [ arXiv ], [ Slides ]

Talks and Presentations

Oct. 2012. Learning Fair Representations, ITCS, Tsinghua University.
Oct. 2011. Inapproximability of Treewidth, One-Shot Pebbling, and Related Layout Problems, Theory Seminar, University of Toronto.
May 2011. Inapproximability of Treewidth and Related Graph Layout Problems, ITCS, Tsinghua University.

Theory Student Seminar Presentations

Jan. 2012. Subexponential Algorithms for Unique Games and Small Set Expansion (presented the work of [ABS10] )
Dec. 2011. Cheeger's Inequality: Proof and Applications.
Nov. 2010. Approximation Algorithm for Balanced Separator and Its Applications (presented the work of [LT99] and [ARV04] )

Courses Taken

Introduction to Computational Complexity. Instructor: Stephen Cook
Methodologies to deal with Intractability. Instructor: Avner Magen
System Modeling and Analysis. Instructor: Peter Marbach
Introduction to Machine Learning. Instructor: Richard Zemel
Project: Evaluating Probabilistic Matrix Factorization on Netflix Dataset. [ Pdf ]
Topics in Applied Discrete Mathematics: Lattices in Computer Science. Instructor: Vinod Vaikuntanathan

Work Experience

I worked at Google, Inc. in the summer of 2011 as a software engineering intern on Gmail's anti-spam team. I applied machine learning techniques to the problem of identifying phishing emails that were misclassified as regular spam.