We devise intelligent systems capable of demonstrating competence and adaptability in complex uncertain environments and sensitive social contexts. Our proposed approaches present attractive scalability properties, which ensure that they can be deployed in the real-world. They also possess robustness properties that make them particularly suitable for socially sensitive settings.

We are devising data-driven robust AI approaches to assist severely resource constrained NGOs dedicated to biodiversity preservation. Indeed, a very effective strategy to help preserve animals and their habitats consists in purchasing or easing parcels of land over time, thereby precluding them from being lost to human degradation. This work is supported through an NSF CMMI Operations Engineering 4 year $535k grant entitled "Preserving Biodiversity via Robust Optimization." For this research, we have partnered with Conservation Biologists at Panthera, at the Wildlife Conservation Society, and at the U.S. Geological Survey. We will pilot our proposed conservation plans in Central America to help protect the Jaguar.

The gorilla, the elephant, and the jaguar, just some of the species that are critically endagered and that we seek to protect in our conservation work

For example, we are investigating the problem of selecting a subset of nodes (individuals) in a virtual or physical (social) network that can act as monitors capable of "watching-out" for their neighbors (friends) when the availability or performance of the chosen monitors is uncertain. Such problems arise for example in the context of "Gatekeeper Trainings" for suicide prevention on college campuses (in particular on those campuses that are under-resourced). Our proposed algorithm is being prepared to be piloted in dorms at the University of Denver by our collaborator there in the School of Social Work (IRB under review). This work is supported by a grant from the Army Research Laboratory Army Research Office (subaward from USC School of Social Work). We also recently received generous support from the Living to Love Another Day Foundation to help deploy our algorithm on the University of Southern California campus.

Exploring algorithmic fairness in robust graph covering problems

(*) A. Rahmattalabi, P. Vayanos, A. Fulginiti, E. Rice, B. Wilder, A. Yadav, M. Tambe

In Proceedings of the 33rd Conference on Neural Information Processing Systems, NeurIPS, 2019.

note: acceptance rate ~21% in year of submission

Staying ahead of the game: adaptive robust optimization for dynamic allocation of threat screening resources

(**) S.M. Mc Carthy, P. Vayanos and M. Tambe

In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI), pp. 3770-3776, 2017.

Chance-constrained optimization for refinery blend planning under uncertainty

Y. Yang, P. Vayanos, and P.I. Barton

Industrial & Engineering Chemistry Research, 56 (42), pp. 12139–12150, 2017.

A constraint sampling approach for multi-stage robust optimization

P. Vayanos, D. Kuhn and B. Rustem

Automatica, 48(3):459-471, 2012.

Decision rules for information discovery in multi-stage stochastic programming

P. Vayanos, D. Kuhn, and B. Rustem

In Proceedings of the 50th IEEE Conference on Decision and Control, pp. 7368-7373, 2011.

Hedging electricity swing options in incomplete markets

P. Vayanos, W. Wiesemann, and D. Kuhn

In Proceedings of the 18th IFAC World Congress, pp.846-853, 2011.