Stephen Tu

I am a graduate student in EECS at UC Berkeley advised by Ben Recht. My research interests lie somewhere in the intersection of machine learning and optimization. In a previous life, I worked on multicore databases and encrypted query processing.

The easiest way to reach me is e-mail: stephent at berkeley dot edu


Breaking Locality Accelerates Block Gauss-Seidel. [arXiv]
Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens,
Michael I. Jordan, and Benjamin Recht.

Large Scale Kernel Learning using Block Coordinate Descent. [arXiv]
Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, and Benjamin Recht.


Cyclades: Conflict-free Asynchronous Machine Learning. [arXiv]
Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Christopher Ré, and Benjamin Recht.
NIPS 2016.

Low-rank Solutions of Linear Matrix Equations via Procrustes Flow. [PDF] [Slides]
Stephen Tu, Ross Boczar, Max Simchowitz, Mahdi Soltanolkotabi, and Benjamin Recht.
ICML 2016.

Machine Learning Classification over Encrypted Data. [PDF]
Raphael Bost, Raluca Ada Popa, Stephen Tu, and Shafi Goldwasser.
NDSS 2015.

Fast Databases with Fast Durability and Recovery through Multicore Parallelism. [PDF]
Wenting Zheng, Stephen Tu, Eddie Kohler, and Barbara Liskov.
OSDI 2014.

Anti-Caching: A New Approach to Swapping in Main Memory OLTP Database Systems. [PDF]
Justin DeBrabant, Andrew Pavlo, Stephen Tu, Michael Stonebraker, and Stan Zdonik.
VLDB 2014.

Speedy Transactions in Multicore In-Memory Databases. [PDF] [Slides] [Code]
Stephen Tu, Wenting Zheng, Eddie Kohler, Barbara Liskov, and Samuel Madden.
SOSP 2013.

Processing Analytical Queries over Encrypted Data. [PDF] [Slides] [Code]
Stephen Tu, M. Frans Kaashoek, Samuel Madden, and Nickolai Zeldovich.
VLDB 2013.

The HipHop Compiler for PHP.
Haiping Zhao, Iain Proctor, Minghui Yang, Xin Qi, Mark Williams, Guilherme Ottoni, Charlie Gao, Andrew Paroski, Scott MacVicar, Jason Evans, and Stephen Tu.
OOPSLA 2012.

The Case for PIQL: A Performance Insightful Query Language. [PDF]
Michael Armbrust, Nick Lanham, Stephen Tu, Armando Fox, Michael Franklin, and David Patterson.
SoCC 2010.

PIQL: A Performance Insightful Query Language For Interactive Applications. [PDF]
Michael Armbrust, Stephen Tu, Armando Fox, Michael Franklin, David Patterson, Nick Lanham, Beth Trushkowsky, and Jesse Trutna.
SIGMOD 2010, Demonstration.


Learning mixture models. [PDF]

Practical first order methods for large scale semidefinite programming. [PDF]

Geometric random walks for sampling from convex bodies. [PDF]

data-microscopes: Bayesian non-parametric inference made simple in Python. [Slides]

The Dirichlet-Multinomial and Dirichlet-Categorical models for Bayesian inference. [PDF]

Derivation of EM updates for discrete Hidden Markov Models. [PDF]

Introductory notes on differential privacy. [PDF]

Techniques for query processing on encrypted databases. [PDF]

Implementing concurrent data structures on modern multicore machines. [Slides] [Examples]