Dilkina is one of the junior faculty leaders in the young field of Computational Sustainability, and has co-organized workshops, tutorials, special tracks at AAAI and doctoral consortium on Computational Sustainability. Her work spans discrete optimization, network design, stochastic optimization, and machine learning.

Dilkina's research focuses on advancing the state of the art in combinatorial optimization techniques for solving real-world large-scale problems, particularly ones that arise in sustainability areas such as biodiversity conservation planning and urban planning. Her work is at the intersection of discrete optimization and machine learning. One key area of research is designing machine-learning-driven combinatorial optimization algorithms, by leveraging the plethora of data generated by solving distributions of real world optimization problems.