ABI-ONE ghc program v14.indd - Grace Hopper Celebration of ...

9:30-10:30am
PhD Forum 1 –
Bioinformatics
Applications
Location: Regency Ballroom VII
Mentor: Deb Agarwal (Lawrence Berkeley
National Lab)
A Flexible, Scalable Framework for
Integrating Heterogeneous Sequence
Data in Comparative Genomics
Presenter: Allison A. Regier (University of
Notre Dame)
The assembly step is critical for using
genome sequence data, but it may
introduce errors and/or hide ambiguities.
Previously, the underlying sequence was
discarded because of its high computational
demands. I am developing a framework to
efficiently access underlying sequence data
during downstream analysis. The framework
leverages new developments in distributed
computing that make common data access
patterns efficient. We will use this information
to improve comparative genome
analysis.
Multivariate Time Series Analysis of
Clinical and Physiological Data
Presenter: Patricia Ordóñez Rozo
(University of Maryland, Baltimore County)
We aim to create a multivariate
temporal representation of electronic
medical data which automates the personalization
of baselines and thresholds based
on a patient’s history. Visualizations based
on the representation emphasize the rate
of change in variables and assist providers
in analyzing the data from a multivariate
perspective. A novel similarity metric for this
representation will be the cornerstone to the
development of a search engine for large
medical databases.
p r O G r a m d e Ta i L
WEDNESDAY | SEPT 29
Multi-Agent Fault Tolerance Inspired by
a Computational Analysis of Cancer
Presenter: Megan Olsen (University of
Massachusetts, Amherst)
In cancer biology, it is known that cancer
cells can disappear without therapy, but
not how. We propose that cells communicate
such that primarily malfunctioning cells
(tumors) die. We also propose that this same
communication can be used as inspiration
for a fault-tolerance mechanism for multiagent
and distributed systems to remove
faulty agents using only local information.
I examine the communication protocols
necessary for removing these faults in both
systems.
PhD Forum 2 –
Architecture
Location: Hanover CDE
Mentor: Patty Lopez (Intel)
Predictor Virtualization: Teaching Old
Caches New Tricks
Presenter: Ioana M. Burcea (University of
Toronto)
We present Predictor Virtualization (PV),
a technique that takes advantage
of the existing memory hierarchy (i.e.,
processor caches and main memory) to
emulate large prediction tables for hardware
optimizations. PV increases the utility of
traditional caches: in addition to being
accelerators for slow off-chip memories, the
on-chip memory hierarchy becomes leverage
for effective predictor-based hardware
optimizations.
Coordinated System Level Resource
Management for Heterogeneous
Many-Core Platforms
Presenter: Vishakha Gupta (Georgia
Institute of Technology)
challenge posed by future architectures
A is the efficient exploitation of their many
and sometimes heterogeneous cores. This
is exacerbated by multiple facilities for data
movement and sharing across cores on such
platforms. Our work aims to enable high
performance program execution and efficient
resource utilization in such platforms. Hence,
we propose to virtualize platforms to allow
for flexibility in targeting functionality,
schedule VMs efficiently and create underlying
system-level technologies.
Throughput-Driven Optimizations for
Programming Multi-Core Platforms
Presenter: Rebecca Collins (Columbia
University)
Multi-core architectures are ubiquitous
today, and there is a need for
high level programming tools that capture
an application’s parallel substructure
without placing too great a burden on the
programmer. My research includes two
domain-specific tools that raise the level of
programming abstraction while enhancing
system throughput for data-driven applications.
New Investigators
1 – Real World
Applications
Location: Hanover AB
Mentor: Andrea Danyluk (Williams
College)
Hybrid Methods for Generating
and Evaluating Style-Specific
Accompaniment
Presenter: Ching-Hua Chuan (University of
North Florida)
Creating distinctive harmonizations in an
identifiable style may be one of the most
difficult tasks for amateur song writers. To
model and assist in this creative process, we
present a hybrid computer system combining
knowledge of musical theory and statistical
learning. The system is capable of learning
a style from only a few examples to create
Anita Borg Institute for Women and Technology | Grace Hopper Celebration of Women in Computing 17