[Ml-stat-talks] Fwd: [talks] Colloquium Speaker: Shivaram Venkataraman, Monday, March 13- 12:30pm-reminder

Barbara Engelhardt bee at princeton.edu
Sun Mar 12 20:04:39 EDT 2017


Talk of interest.


Colloquium Speaker
Shivaram Venkataraman, University of California, Berkeley
Monday, March 13- 12:30pm
Computer Science 105
Host: Prof. Andrew Appel

Scalable Systems for Fast and Easy Machine Learning

Machine learning models trained on massive datasets power a number of
applications; from machine translation to detecting supernovae in
astrophysics. However the end of Moore’s law and the shift towards
distributed computing architectures presents many new challenges for
building and executing such applications in a scalable fashion.

In this talk I will present my research on systems that make it easier to
develop new machine learning applications and scale them while achieving
high performance. I will first present programming models that let users
easily build distributed machine learning applications. Next, I will show
how we can exploit the structure of machine learning workloads to build
low-overhead performance models that can help users understand scalability
and simplify large scale deployments. Finally, I will describe scheduling
techniques that can improve scalability and achieve high performance when
using distributed data processing frameworks.

Shivaram Venkataraman is a PhD Candidate at the University of California,
Berkeley and is advised by Mike Franklin and Ion Stoica. His research
interests are in designing systems and algorithms for large scale data
processing and machine-learning. He is a recipient of the Siebel
Scholarship and best-of-conference citations at VLDB and KDD. Before coming
to Berkeley, he completed his M.S at the University of Illinois,
Urbana-Champaign.

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