Emma Brunskill
Wednesday 18th March 2015
Time: 4pm
Basement Seminar Room
Alexandra House, 17 Queen Square, London, WC1N 3AR
Faster Learning for Better Decisions
A fundamental goal of artificial intelligence is to create agents that
learn to make good decisions as they interact with a stochastic
environment. Some of the most exciting and valuable potential
applications
involve systems that interact directly with humans, such as
intelligent tutoring systems or medical interfaces. In these cases,
sample efficiency is highly important, as each decision, good or bad,
is impacting a real person. I will describe our research on tackling
this challenge, including transfer learning across sequential decision
making tasks, as well as its relevance to improving educational tools.
bio:
Emma Brunskill is an assistant professor in the computer science
department at Carnegie Mellon University. She is also affiliated with
the machine learning department at CMU. She works on reinforcement
learning, focusing on applications that involve artificial
agents interacting with people, such as intelligent tutoring systems.
She is a Rhodes Scholar, Microsoft Faculty Fellow and NSF CAREER award
recipient, and her work has received best paper nominations in
Education Data Mining (2012, 2013) and CHI (2014).