THESIS DEFENSE: Julia Schwarz
When
-
Where
GHC 4405
Description
Monte Carlo Methods for Managing Uncertain User Interfaces
Julia Schwarz
COMMITTEE
Jennifer Mankoff, CMU (Co-Chair)
Scott Hudson, (Co-Chair)
Niki Kittur, CMU
Andy Wilson, Microsoft Research
DOCUMENT
http://juliaschwarz.net/DissertationDraftSchwarzJulia.pdf
ABSTRACT
Current user interface toolkits provide effective techniques for
acting on user input. However, many input handling systems make the
assumption that all input events are certain, and are not built to
handle ambiguity such as multiple possible inputs from a recognizer.
This is unfortunately at odds with recent interaction trends towards
voice, gesture and touch, all of which come with a great deal of
uncertainty.
This dissertation presents a new user interface architecture that
treats user input as an uncertain process, approximates the
probability distribution over possible interfaces using Monte Carlo
sampling, and enables interface developers to easily build
probabilistic user interfaces without needing to think
probabilistically. This architecture is embodied in the JULIA toolkit:
a JavaScript User interface Library for tracking Interface
Alternatives. To demonstrate the versatility and power of this
architecture, the dissertation presents a collection of applications
and interaction techniques built using the JULIA toolkit. This
architecture provides the foundation for a new era of nondeterministic
user interfaces that leverage probabilistic models to better infer
user intent.