Yoav Segev

Yoav Segev is a Research Fellow in the Games, Dynamics and Strategic Control Division at the Institute for Advanced Dynamic Uncertainty. He holds a PhD in Operations Research from the Technion – Israel Institute of Technology (Faculty of Industrial Engineering & Management), where his doctoral research developed new duality frameworks for a class of infinite-dimensional convex programmes arising in continuous-time stochastic optimisation. His thesis established sharp conditions for strong duality and primal attainment in Banach-space-valued programming problems, with applications to optimal stopping and stochastic control.

Before joining IADU, Segev held a visiting research position at the Weizmann Institute of Science, where he contributed to work on algorithmic aspects of large-scale linear programming and polyhedral combinatorics. His methodological interests span convex and nonconvex programming, min-max optimisation and saddle-point theory, semidefinite relaxations for combinatorial problems, and variational inequalities in infinite-dimensional spaces. He is particularly drawn to the interface between optimisation theory and dynamic game theory — an interest that shapes his current work at IADU on numerical solution methods for Hamilton-Jacobi-Isaacs equations and the design of efficient first-order algorithms for mean field game systems.

At IADU, his research focuses on provably convergent optimisation algorithms for high-dimensional HJI systems, approximation hierarchies for strategic control problems under uncertainty, and variational methods for the computation of Nash equilibria in continuous-time differential games.