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In my early research, I focused on foundational artificial intelligence (AI), but Ive always been interested in addressing real world problems by advancing AI. Research can be broadly categorized based on how closely it is linked to practical applications and on how much it advances our foundational understanding. My research falls in the so-called Pasteur quadrant, which represents user-inspired basic research. Initially, I worked on general application areas and techniques, such as AI planning and constraint-based optimization. However, I always wanted my work to have broader societal impact, so I started to work on different types of problems. With my collaborators, I conceived the field of computational sustainability, motivated by my desire to focus on societally meaningful and impactful problems, as opposed to the more traditional commercial applications of computer science, as pursued by large tech companies.

What I find particularly exciting about computational sustainability is that its a two-way street: on one hand, we inject computational thinking and methodologies into sustainability challenges, but on the other hand, addressing sustainability problems leads us to novel computational challenges. These interactions lead us to develop new computational approaches that are general and can be applied to different domains. For instance, one of the first problems that I encountered was designing scientific experiments for nitrogen management in fertilizers, which is challenging both from a computational and a statistics perspective because the experimental design needs to eliminate complex interdependencies and biases of the fertilizer treatments. The project led my research group to develop a general methodology for unbiased sampling under complex combinatorial constraints. I also worked on designing wildlife corridors for different species, which is a network design problem. There, we borrowed ideas from the area of complex networks, such as how to design robust large-scale smart electric grids, and in turn, we developed new methodologies for network design.

See the article here:

Little by little, a bird builds its nest - Nature.com