My research is in the general area of theoretical computer science, particularly the areas of Approximation Algorithms, Online Algorithms, and Computational Economics. I work on developing models, algorithms, and markets for resource allocation, decision making, and provisioning problems. These problems arise in a variety of applications -- designing a data network, facility location and clustering, data center scheduling, allocating ad slots, scheduling ride-shares, and civic budgeting. In these contexts, my work has addressed several research challenges.
Intractability: Exactly optimal solutions are often inefficient to compute due to the discrete, combinatorial nature of the problems, and we seek efficient algorithms that compute provably approximate optimal solutions.
Uncertainty: When future demand is uncertain, we seek algorithms that either work with specified models of uncertainty, or are robust to any possible future.
Pricing and Incentives: In settings where participants may lie about the utility of resources to them, we seek to design appropriate pricing schemes and incentives.
Fairness: In settings where resources are shared and the allocation has to be fair to the participants, we seek models for fairness and devise efficient algorithms for these models.
Some Recent Publications
Robust allocations with diversity constraints. NeurIPS '21
Approximately stable committee selection. STOC '20
Proportionally fair clustering. ICML '19
Please see here for a complete list of courses.
Fall '21: CPS 330: Algorithm Design
Spring '21: CPS 230: Discrete Mathematics
Fall '20: CPS 532: Graduate Algorithms
Spring '20: CPS 630: Randomized Algorithms
Spring '19: CPS590: Algorithms for Decision making at Scale
D205, Levine Science Research Center,
308 Research Drive, Durham NC 27708-0129.
Phone: (919) 660-6598
Email address: <first_name> @cs.duke.edu