Networks and Optimization
Developing algorithms to tackle complex optimization and large scale data-analysis problems by combining techniques from mathematics and computer science.
In today’s society, complex systems and massive datasets surround us. From transport and traffic, to behavioral economics and biology, real-world applications demand that we identify optimal solutions among a huge set of possibilities, as well as patterns from gigantic datasets. Our research group, Networks and Optimization (N&O), does fundamental and high-impact research which tackles these challenging problems.
As our main activity, we develop algorithms to efficiently solve optimization problems from areas such as planning, scheduling and routing, and data analysis problems in domains such as pattern matching and indexing. Our expertise ranges from discrete to continuous optimization, in both centralized and decentralized settings, as well as the design and analysis of data structures. We focus both on the development of problem-specific methods as well as general algorithmic techniques. To design the next-generation of algorithms, we combine insights and approaches from diverse areas within mathematics and computer science. In particular, we explore and exploit the varied combinatorial, geometric and algebraic structures underlying our problems, such as graphs, matroids, strings, lattices and polynomials.
In collaboration with our industry partners, we have applied the algorithms developed within the group to solve a variety of real-world problems that are both complex and data-intensive. We are always interested in new algorithmic challenges arising in applications and are open to new cooperations.
Watch our group video to get a glimpse of our activities or read more information on the Networks and Optimization research group.
Together with colleagues of Dutch universities we also organize a Dutch Seminar on optimization.
Current projects with external funding
- Algorithms for PAngenome Computational Analysis (ALPACA)
- Constance van Eeden Fellowship (Constance van Eeden)
- Networks (Networks)
- Networks COFUND postdocs (Networks COFUND postdocs)
- Optimization for and with Machine Learning (OPTIMAL)
- Optimization for and with Machine Learning (OPTIMAL2)
- Pan-genome Graph Algorithms and Data Integration (PANGAIA)
- Towards a Quantitative Theory of Integer Programming (QIP)
- Tensor modEliNg, geOmetRy and optimiSation (TENORS)