[Ml-stat-talks] Fwd: [talks] Colloquium Speaker Ryan Adams Thurs Nov 13, 4:30pm

Barbara Engelhardt bee at CS.Princeton.EDU
Thu Nov 13 07:28:43 EST 2014


ML talk today by Ryan Adams. Apologies for the late notice.

---------- Forwarded message ----------
From: Nicole E. Wagenblast <nwagenbl at cs.princeton.edu>
Date: Wed, Nov 12, 2014 at 10:00 AM
Subject: [talks] Colloquium Speaker Ryan Adams Thurs Nov 13, 4:30pm
To: "Talks (colloquium)" <talks at lists.cs.princeton.edu>


Better Science Through Better Bayesian Computation
Ryan Adams, Harvard University
Thursday, November 13, 4:30pm
Computer Science 105

As we grapple with the hype of "big data" in computer science, it
is important to remember that the data are not the central objects:
we collect data to answer questions and inform decisions in
science, engineering, policy, and beyond.  In this talk, I will discuss my
work in developing tools for large-scale data analysis, and the
scientific collaborations in neuroscience, chemistry, and astronomy that
motivate me and keep this work grounded.  I will focus on two lines of
research that I believe capture an important dichotomy in my work and in
modern probabilistic modeling more generally: identifying the
"best" hypothesis versus incorporating hypothesis uncertainty.  In the
first case, I will discuss my recent work in Bayesian optimization,
which has become the state-of-the-art technique for automatically
tuning machine learning algorithms, finding use across academia and
industry. In the second case, I will discuss scalable Markov chain Monte
Carlo and the new technique of Firefly Monte Carlo, which is the
first provably correct MCMC algorithm that can take advantage of subsets
of data.

Ryan Adams is an Assistant Professor of Computer Science at Harvard
University, in the School of Engineering and Applied Sciences. He leads the
Harvard Intelligent Probabilistic Systems group, whose research focuses on
machine learning and computational statistics, with applied collaborations
across the sciences.  Ryan received his undergraduate training in EECS at
MIT and completed his Ph.D. in Physics at Cambridge University as a Gates
Cambridge Scholar under David MacKay.  He was a CIFAR Junior Research
Fellow at the University of Toronto before joining the faculty at Harvard.
His Ph.D. thesis received Honorable Mention for the Leonard J. Savage Award
for Bayesian Theory and Methods from the International Society for Bayesian
Analysis.  Ryan has won paper awards at ICML, AISTATS, and UAI, and
received the DARPA Young Faculty Award.

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