• Click on the titles of individual talks for abstract, slides and archived video.

All talks will take place in Sibley Auditorium, Bechtel Engineering Center, UC Berkeley. The reception on Tuesday afternoon will be held at Calvin Lab.

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Coffee and Check-In
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Opening Remarks
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MapReduce Algorithmics
Sergei Vassilvitskii, Google
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Break
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Large Scale Response Prediction to Serve Ads on LinkedIn
Deepak Agarwal (LinkedIn)
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Three Goals in Parallel Graph Computations: High Performance, High Productivity, and Reduced Communication
Aydin Buluç, Lawrence Berkeley National Laboratory
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Scalable Graph-Parallel Inference Algorithms and Systems
Joseph Gonzalez, UC Berkeley
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Lunch Break
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Cost Models for Locality and Parallelism
Harsha Vardhan Simhadri, Carnegie Mellon University
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Extreme Multiclass
John Langford, Microsoft Research New York
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Break
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Parallel Active Learning
Alekh Agarwal, Microsoft Research New York
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MLbase: A Distributed Machine Learning System
Tim Kraska, Brown University
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Sampling Algorithms to Count Frequent Patterns in Graphs
Ali Pinar, Sandia National Laboratories
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Coffee and Check-In
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Big Data on Smallish Machines
Guy Blelloch (Carnegie Mellon University)
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Break
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Communication-Optimal Parallel Algorithm for Strassen’s Matrix Multiplication
Grey Ballard, Sandia National Laboratories
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Implementing Randomized Matrix Algorithms in Parallel and Distributed Environments
Michael Mahoney (International Computer Science Institute and UC Berkeley)
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Tall-and-skinny QR Factorizations in MapReduce
David Gleich (Purdue University)
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Lunch Break
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From Sparse Matrices to Graphs and Back Again
John Gilbert (UC Santa Barbara)
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On the O(1/k) Convergence of the Asynchronous Distributed Alternating Direction Method of Multipliers
Asu Ozdaglar, Massachusetts Institute of Technology, Ermin Wei, Massachusetts Institute of Technology
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Break
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Distributed Learning, Communication Complexity, and Privacy
Nina Balcan, Georgia Institute of Technology
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Reception at Calvin Lab
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Coffee and Check-In
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Parallel Coordinate Descent Methods for Big Data Optimization
Peter Richtarik (University of Edinburgh)
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Break
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An Algebraic View of the New Randomized Kaczmarz Linear Solver
Sivan Toledo, Tel Aviv University
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Randomized Asynchronous Linear Solvers
Haim Avron, IBM T.J. Watson Research Center
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Parallelization by Approximation
Kanat Tangwongsan, IBM
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Lunch Break
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Highly Scalable Branch-and-Bound for Maximum Monomial Agreement
Cynthia Phillips, Sandia National Laboratories
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Provably Efficient Algorithms for Training Deep Networks
Shai Shalev-Shwartz, Hebrew University of Jerusalem
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Break
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Peeling Algorithms
Michael Mitzenmacher, Harvard University
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Coffee and Check-In
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Parallelism in Linear and Mixed Integer Programming
Ed Rothberg, Gurobi Optimization
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Break
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Using Data-Oblivious Algorithms for Private Cloud Storage Access
Michael Goodrich, UC Irvine
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Communication Avoiding Algorithms for Linear Algebra and Beyond
James Demmel (UC Berkeley)
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Consensus-Based Distributed Online Prediction and Optimization
Michael Rabbat, McGill University
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Lunch Break
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Large-Scale Generalized Matching
Rainer Gemulla, Max Planck Institute, Saarbrücken
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Stochastic Optimization for Big Data Machine Learning Problems
Tong Zhang, Rutgers University
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Break
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First Order Methods for Distributed Network Optimization
Angelia Nedich, University of Illinois, Urbana-Champaign