[Ml-stat-talks] Colloquium Speaker Ameet Talwalkar Thurs March 13, 4:30pm

David Blei blei at CS.Princeton.EDU
Fri Mar 7 14:24:22 EST 2014

can't go on safari?  see the next best thing: "machine learning in the
wild."  next thursday.


---------- Forwarded message ----------
From: Nicole E. Wagenblast <nwagenbl at cs.princeton.edu>
Date: Fri, Mar 7, 2014 at 1:36 PM
Subject: [talks] Colloquium Speaker Ameet Talwalkar Thurs March 13, 4:30pm
To: "Talks (colloquium)" <talks at lists.cs.princeton.edu>

Machine Learning in the Wild

Ameet Talwalkar (University of California, Berkeley)
Thursday, March 13, 4:30pm
Computer Science 105

Modern datasets are rapidly growing in size and complexity, and this
wealth of data holds the promise for many transformational
applications. Machine learning is seemingly poised to deliver on this
promise, having proposed and rigorously evaluated a wide range of data
processing techniques over the past several decades. However, concerns
over scalability and usability present major roadblocks to the wider
adoption of these methods, and in this talk I will present work that
addresses these concerns. In terms of scalability, my work relies on a
careful application of divide-and-conquer methodology. In terms of
usability, I focus on developing tools to diagnose the applicability
of learning techniques and to autotune components of typical machine
learning pipelines. I will discuss applications in the context of
matrix factorization, estimator quality assessment and genomic variant

Ameet Talwalkar is a postdoctoral fellow in the Computer Science
Division at UC Berkeley. He obtained a Ph.D. in Computer Science from
the Courant Institute at New York University, and prior to that
graduated summa cum laude from Yale University. His work addresses
scalability and ease-of-use issues in the field of machine learning,
as well as applications related to large-scale genomic sequencing
analysis. He has won the Janet Fabri Prize for best doctoral
dissertation and the Henning Biermann Award for exceptional service at
NYU, received Yale's undergraduate prize in Computer Science, and is
an NSF OCI postdoctoral scholar.

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