About the Authors
Daniel Sheldon
Assistant Professor
University of Massachusetts Amherst
sheldon[ta]cs[td]umass[td]edu
people.cs.umass.edu/~sheldon
Assistant Professor
University of Massachusetts Amherst
sheldon[ta]cs[td]umass[td]edu
people.cs.umass.edu/~sheldon
Daniel Sheldon is a Five Colleges Assistant Professor in
computer science at the University of
Massachusetts and
Mount Holyoke
College.
Prior to his appointment in 2012, he was a Postdoctoral Fellow in the
School of EECS at Oregon State
University, where he held a National Science Foundation (NSF)
Fellowship in Bioinformatics. He received his Ph.D. from
Cornell University in 2009 under the
supervision of John Hopcroft;
his thesis explored issues of
manipulation in web-based reputation systems and developed new
inference methods for probabilistic graphical models, inspired by the
problem of modeling bird migration.
His research interests include: algorithms for computational ecology
and environmental science; machine learning; probabilistic modeling
and inference; and optimization.
Neal E. Young is a Professor in computer science
at the University of California, Riverside.
In 1991 he completed a Ph.D. in Computer Science
at Princeton University under Professor Robert Tarjan.
From 1991 to 1994
he did postdocs at the University of Maryland, Princeton,
Cornell, and AT\&T Bell Laboratories.
From 1995 to 1999
he was an assistant professor in the Computer Science
department at Dartmouth College.
From 1999 to 2003
he worked at Akamai Technologies
(the world's largest Internet content-delivery network).
He has been at the University of California since 2004.
His research to date is on fast approximation algorithms for combinatorial
optimization problems,
including online problems, NP-hard problems, and linear programs.
He is primarily interested in general techniques
for the design and mathematical analysis of such algorithms.