Marie desJardins is an American computer scientist, known for her research on artificial intelligence and computer science education. She is also active 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 University 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 (that is, the degree to which an agent is able to carry out its commitments) and integrity (that is, the degree to which an agent is actually committed to complete its part of a joint action) on decision making. This framework was later extended to incorporate reputation (indirect observations provided by third-party agents, with applications to online rating systems and supply chain formation. In many domains, when a set of items is presented as a collection, interactions between the items may increase (due to complementarity) or decrease (due to redundancy or incompatibility) 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 (i.e., which characteristics of the individual items are seen as more or less desirable) and its “diversity” (i.e., how broadly or narrowly distributed the objects in the set are over their possible range). This work presented a heuristic method for taking advantage of taxonomies, or hierarchies of values, in