Yu Wu (Ledell)
Theory of Computation Group,
Department of Computer Science,
University of Toronto
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.)
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.

Richard Zemel,
Toniann Pitassi,
Yu Wu,
Kevin Swersky
and Cynthia Dwork,
Learning Fair Representations ,
ICML2013: The 30th International Conference on Machine Learning (2013).

Per Austrin,
Toniann Pitassi,
and Yu Wu,
Inapproximability of Treewidth, OneShot Pebbling, and Related
Layout Problems,
In International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX), 2012, pp. 1324, Accepted for publication in Theory of Computing.
[ arXiv ],
[ Slides ]

Oct. 2012. Learning Fair Representations,
ITCS, Tsinghua University.

Oct. 2011. Inapproximability of Treewidth, OneShot Pebbling, and Related
Layout Problems,
Theory Seminar, University of Toronto.

May 2011. Inapproximability of Treewidth and Related Graph Layout Problems,
ITCS, Tsinghua University.

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] )

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