Speaker:Alan Kuhnle

'Scalable and Learned Algorithms for Discrete Optimization'

          
Abstract:

In this talk, I present the work of the Optimization and Learning Systems Lab at Florida State University on the design of scalable algorithms for optimization problems on big data. In particular, I describe our work on linear-time, parallelizable algorithms for combinatorial optimization problems arising from online social networks. Finally, I give an overview of future directions of the lab, which include augmenting algorithms with learned components to improve practical performance; optimization with incomplete information; and submodular planning.

Bio: 

Alan Kuhnle is Assistant Professor of Computer Science at the Florida State University, where he directs the Optimization and Learning Systems Lab. His work focuses on the design and analysis of scalable algorithms for ubiquitous combinatorial optimization problems arising in data science applications, such as vehicle routing and marketing on social networks. He is the recipient of the First Year Assistant Professor Award at Florida State University in 2020 and his work has led to 34 publications in leading academic journals and conferences. He has served on the program committee of leading machine learning conferences and is Associate Editor of Journal of Combinatorial Optimization.