Marie desJardins


Marie desJardins is an American Computer Scientist noted for her research on artificial intelligence and computer science education. She is also noted for her leadership in broadening participation in computing.

Biography

DesJardins grew up in Columbia, Maryland. She received an A. B. in Engineering and Computer Science from Harvard University in 1985.
She received a Ph.D in Computer Science from University of Berkeley in 1992.
In 1991 she joined SRI International, working in the Artificial
Intelligence Center. In 2001 she joined the Department of Computer Science
and Electrical Engineering
at the University of Maryland, Baltimore County as an Assistant Professor. While there she was
promoted to Associate Professor in 2007 and to Professor in 2011.
In 2015, she was appointed Associate Dean for Academic Affairs in UMBC
College of Engineering and Information Technology. She left UMBC in 2018 to become the Founding Dean of the College of
Organizational, Computational, and Information Sciences at
Simmons College in Boston.

Career

DesJardins has explored the effect of the network topology on the efficiency of
team formation in multi-agent systems, showing that scale-free networks are
often the most effective topologies for facilitating team formation and
leading to the development of learning methods for agents to adapt their
behavioral strategies.
She has shown the first approach to trust modeling that explicitly
separates the effect of competence and integrity on
decision making. This framework was later extended to incorporate reputation
or decrease the quality of the set as a whole. Although this “portfolio effect” had occasionally been mentioned in the literature, this work was the first to address this problem a general way, by modeling the tradeoff between the “depth” of the set and its “diversity”.
This work presented a heuristic method for taking advantage of taxonomies, or
hierarchies of values, in Bayesian network learning by searching for the most
effective level of abstraction within the taxonomy, discovering which
distinctions are relevant for the input data, and ignoring the others.
This process reduces the number of parameters that must be estimated, and
simplifies the representation, while preserving the meaningful distinctions in
the domain.
This paper, presenting comprehensive advice to help graduate students navigate
the process of earning an M.S. or Ph.D. and develop strong mentoring
relationships, has been circulated widely to graduate students around the
world and has been translated into multiple languages. It has also been published in IAPPP Communications and excerpted in SHPE, Winter 2000, and in IEEE Potentials.

Awards

In 2018, she became an AAAI Fellow.
Her other notable awards include: