Phone: (434) 982-2277
Fax: (434) 982-2214
Email: soffa@cs.virginia.edu
Office: 206 Olsson Hall, UVa
ary Lou Soffa received her Ph.D. in computer science
from the University of Pittsburgh in
1977. She joined the Department of
Computer Science as Chair and Owen. R. Cheatham Professor in 2004.
Her efforts in diversity and mentoring were recognized by the White
House in 1999 with a Presidential Award for Excellence in Science,
Mathematics and Engineering Mentoring. She was elected an ACM Fellow in
1999, and serves on the Board of the Computing Research Association (CRA) and CRA-W. She has served on
the Executive Committees of both SIGSOFT and SIGPLAN, as well as
conference chair, program chair or program committee member for numerous
leading conferences. She was a member of the Editorial Board of ACM
Transactions on Software Engineering Methodology and ACM Transactions on
Programming Languages and Systems. She has been very active over the
years in improving the participation of women in computer science. Mary
Lou graduated 12 Ph.D. students and 54 Master Students, over half of
which were women.
offa's research interests include optimizing
and parallelizing compilers, program analysis, and software tools
for debugging and testing programs. Her current work includes
developing a framework for optimizations that can be used to determine
important properties of optimizations, and in particular, the
profitability of optimizations. The unifying framework includes code,
optimization and resource models for systematically exploring the
application of optimizations. The framework will provide both analytical
and experimental models for understanding, predicting and verifying the
properties of optimizations (i.e., performance impact and interactions).
Also, practical and automatic strategies to drive the application of
optimizations based on the models are part of the framework. By applying
these model-based optimization strategies, the goal is that optimizing
compilers will be able to produce higher quality code and use different
paradigms than what is possible with current approaches.