[Ml-stat-talks] Fwd: [talks] Colloquium Speaker Joseph Gonzalez Thurs Feb 26, 12:30pm

Barbara Engelhardt bee at CS.Princeton.EDU
Wed Feb 25 11:52:53 EST 2015

ML talk on Thursday from Joey Gonzalez, co-founder of GraphLab (now Dato).

---------- Forwarded message ----------
From: Nicole E. Wagenblast <nwagenbl at cs.princeton.edu>
Date: Wed, Feb 25, 2015 at 10:00 AM
Subject: [talks] Colloquium Speaker Joseph Gonzalez Thurs Feb 26, 12:30pm
To: "Talks (colloquium)" <talks at lists.cs.princeton.edu>

Colloquium Speaker
Joseph Gonzalez, University of California
Thursday, February 26, 12:30pm
Computer Science 105

Learning Systems: Systems and Abstractions for Large-Scale Machine Learning

The challenges of advanced analytics and big data cannot be address by
developing new machine learning algorithms or new computing systems in
isolation.  Some of the recent advances in machine learning have come from
new systems that can apply complex models to big data problems.  Likewise,
some of the recent advances in systems have exploited fundamental
properties in machine learning to reach new points in the system design
space.  By considering the design of scalable learning systems from both
perspectives, we can address bigger problems, expose new opportunities in
algorithm and system design, and define the new fundamental abstractions
that will accelerate research in these complementary fields.

In this talk, I will present my research in learning systems spanning the
design of efficient inference algorithms, the development of graph
processing systems, and the unification of graphs and unstructured data.  I
will describe how the study of graphical model inference and power-law
graph structure shaped the common abstractions in contemporary graph
processing systems, and how new insights in system design enabled
order-of-magnitude performance gains over general purpose data-processing
systems.  I will then discuss how lessons learned in the context of
specialized graph-processing systems can be lifted to more general
data-processing systems enabling users to view data as graph and tables
interchangeably while preserving the performance gains of specialized
systems.  Finally, I will present a new direction for the design of
learning systems that looks beyond traditional analytics and model fitting
to the entire machine learning life-cycle spanning model training, serving,
and management.

Joseph Gonzalez is a postdoc in the UC Berkeley AMPLab and cofounder of
GraphLab. Joseph received his PhD from the Machine Learning Department at
Carnegie Mellon University where he worked with Carlos Guestrin on parallel
algorithms and abstractions for scalable probabilistic machine learning.
Joseph is a recipient of the AT&T Labs Graduate Fellowship and the NSF
Graduate Research Fellowship.

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