AI Seminar: AI and O.R. for Environmental Sustainability

Event Speaker
Bistra Dilkina
Associate Professor of Computer Science, Co-Director, USC Center of AI in Society, University of Southern California
Event Type
Artificial Intelligence
Date
Event Location
KEC 1001
Event Description

With the increasing anthropogenic pressures of urbanization, agriculture, deforestation, other socio-economic drivers as well as climate change, biodiversity and habitat conservation is a key sustainable development goal. Techniques from AI and O.R. and their hybridization have an important role to play in providing both predictive and prescriptive tools to inform critical decision-making, which can help us do more with less in this important application domain. A prime example of the field of Computational Sustainability, this presentation will give several successful examples of the two-way street of research providing useful domain solutions to real-world problems, while advancing core methodology in AI and O.R. Key examples include using deep learning and satellite data for land cover mapping, predicting species distributions under climate change and optimizing spatial conservation planning, as well as developing data-driven techniques to curb illicit wildlife poaching and trafficking.

Speaker Biography

Dr. Bistra Dilkina is an associate professor of computer science at the University of Southern California, co-director of the USC Center of AI in Society, and the inaugural Dr. Allen and Charlotte Ginsburg Early Career Chair at the USC Viterbi School of Engineering. Her research and teaching center around the integration of machine learning and discrete optimization, with a strong focus on AI applications in computational sustainability and social good. She received her Ph.D. from Cornell University in 2012 and was a post-doctoral associate at the Institute for Computational Sustainability. Her research has contributed significant advances to machine-learning-guided combinatorial solving including mathematical programming and planning, as well as decision-focused learning where combinatorial reasoning is integrated in machine learning pipelines. Her applied research in Computational Sustainability spans using AI for wildlife conservation planning, using AI to understand the impacts of climate change in terms of energy, water, habitat and human migration, and using AI to optimize fortification of lifeline infrastructures for disaster resilience. She has over 90 publications and has co-organized or served as a chair to numerous workshops, tutorials, and special tracks at major conferences.