Nikolaos Efthymiou
Connections beneath the surface.
Connections beneath the surface.
My expertise lies at the intersection of Theoretical Computer Science, Combinatorial Optimization, and Machine Learning, disciplines I leverage to tackle inherently intractable, structured problems. In my current role, I tackle challenging optimization problems within the airline industry. As I look ahead, with AI models and agentic architectures reshaping the frontier of intelligence, I am inspired to advance systems rooted in formal and verifiable reasoning and principled design. I play the piano, I love classical music, especially Bach and Rachmaninoff, and I find beauty in the structures underlying both mathematics and music.
A few explorations at the intersection of Machine Learning, algorithms and structure.
Research on differentiable loss functions for unsupervised SAT solving with Graph Neural Networks using tools from Probabilistic Combinatorics.
Approximation algorithms and impossibility results for a new, two-stage version of consistent clustering. Natural extension to a stochastic optimization setting.
Survey on expanders, their combinatorial construction of Reingold et al. and some surprising applications in Proof Complexity, a major subfield of Complexity Theory.
Supervised Learning methods for speeding up a Constraint Programming solver for the Cyclic Hoist Scheduling Problem.
Tackling large-scale discrete optimization problems in crew scheduling.
Research in Machine Learning for Combinatorial Optimization: neural SAT solvers and differentiable primal-dual algorithms for MaxCUT.
Recognition for airline Operations Researchers in the early stages of their careers.
Specialization in Computer Science Theory. Master thesis jointly conducted at SWISS and the Risk Analytics and Optimization laboratory led by Prof. Daniel Kuhn.
Discrete algorithms for passenger recovery problems. Cost-saving contributions to the OPSD project of Lufthansa Group.
Specialization in Big Data. Research in Machine Learning for Scheduling Problems.
Development of clustering algorithms for internal applications.
National competition for film music composition.