Clare Bates Congdon
Location: (Portland, ME)
Personal Research Web Page: http://www.cs.usm.maine.edu/~congdon/
Keywords: evolutionary computation and machine learning applied to bioinformatics and computational biology, including DNA sequence analysis and phylogenetics, and applied to intelligent agents, including robotics and interactive dynamic games
Posted on: Wednesday, May 27th, 2009
Broad Research Area: AI / Machine Learning / Robotics / Vision, Computer Science Education / Educational Technology, Databases / Information Retrieval / Data Mining, Graphics / Visualization, Scientific/Medical Informatics
Research Interests:
The Bioinformatics and Intelligent Systems Lab at USM focuses on evolutionary
computation and other artificial intelligence approaches as applied primarily
to bioinformatics and intelligent agents.
Bioinformatics projects include:
GAMI, a genetic algorithms approach to DNA motif inference: In this project we
identify patterns in noncoding DNA that have been conserved across
evolutionary time; such elements are good candidates for affecting the
function of genes and will be studied at the bench by our biological
collaborators. This project is motivated by a study of CFTR, the gene for
cystic fibrosis and has expanded to other environmental-response genes; among
GAMI’s strengths is that it is designed to work with long sequences (e.g.,
100k) and many of them (e.g., 100 or more) There are many subprojects here;
one particularly important horizon is that functional elements in noncoding
DNA tend to appear in modules (not in isolation), and we will design a new
system to infer these.
Gaphyl, a genetic algorithms approach to phylogenetics, the inference of trees
representing the evolutionary relationships among species or strains. We are
just starting a swine flu project, which will also mean designing a new
computational approach designed to work specifically with viruses.
My bioinformatics work is collaborative with researchers at the University of
Maine (Orono), Dartmouth College, the Mount Desert Island Biological Lab,
the University of Illinois.
Our recent intelligent agents work has focused on game-playing agents. We won
first place in the 2008 Ms. PacMan competition at the World Congress on
Computational Intelligence (Hong Kong) and the 2009 Unreal Tournament
competition at the Congress on Evolutionary Computation (Trondheim,
Norway). Designing agents for interactive dynamic games is a very similar
research problem to real-time robotics, where inputs must quickly be processed
to determine appropriate outputs, and split-second decisions must be
made. While the competitions have been won with non-learning systems, our
primary interest (and current efforts) are on learning in these environments.
Other projects include:
Lobster CyberCatch, an educational science game designed to teach
middle-school students about math and science concepts in general and lobsters
(and lobstering) more specifically.
VIEWER, a project centered at the University of Maine (Orono) to develop
touch-screen visualization walls for remote conferencing for scientific
collaborations, including informal “water cooler” conversations.
Contact Information:
Clare Bates Congdon
email obfuscated - click to reveal
http://www.cs.usm.maine.edu/~congdon/
Department of Computer Science
University of Southern Maine
Portland, ME
