| Speaker : | Kuang XU |
| Stanford University | |
| Date: | 29/08/2018 |
| Time: | 2:00 pm - 3:00 pm |
| Location: | LINCS Seminars room |
Abstract
Motivated by the increasing ubiquity of large-scale data
collection infrastructures, we investigate how to protect sensitive
information in dynamic resource allocation problems. The central question
is: how should one make resource allocation decisions such that these
decisions, if observed and analyzed by an adversary, do not inadvertently
reveal private information of the decision maker? In this talk we will
examine two well known decision problems, path planning and active learning,
and in each case provide tight information-theoretic upper and lower bounds
on the amount of additional resources required for a given level of privacy.
Bio: Kuang Xu was born in Suzhou, China. He received the B.S. degree in
Electrical Engineering (2009) from the University of Illinois at
Urbana-Champaign, Urbana, Illinois, USA, and the Ph.D. degree in Electrical
Engineering and Computer Science (2014) from the Massachusetts Institute of
Technology, Cambridge, Massachusetts, USA. He was a postdoctoral fellow at
the Microsoft Research-Inria Joint Center in Paris, France (2014-2015),
hosted by Laurent Massoulié. His research interests lie in the fields of
applied probability theory, optimization, and operations research, seeking
to understand fundamental properties and design principles of large-scale
stochastic systems, with applications in queueing networks, healthcare,
privacy and statistical learning theory. He has received several awards
including a First Place in INFORMS George E. Nicholson Student Paper
Competition, a Best Paper Award, as well as a Kenneth C. Sevcik Outstanding
Student Paper Award from ACM SIGMETRICS.