Yaroslav Salii obtained his PhD in Numerical Methods, Mathematical Modeling, and Software from Ural Federal University in 2018, supervised by Professor Aleksandr G. Chentsov. During his PhD studies, he worked on dynamic programming (DP) and derived methods for generalizations of TSP with Precedence Constraints (TSP-PC). His work helped improve state-of-the-art by obtaining the first optimal solutions for the long-standing ry48p.3 and kro124p.4 instances from TSPLIB, and a number of TSPLIB-derived minmax and time-dependent instances of TSP-PC. As a postdoctoral researcher, he is working on establishing an infrastructure for comparative studies in advanced optimal control methods for very large-scale networks, achieved by running these methods over realistic epidemic models of variable scale that have transportation networks and per-node control in the form of lockdown level. The proof-of-concept implementation (see https://github.com/yvs314/epi-net-m) can produce models with up to 64,734 nodes using the population points, commute, and air travel networks in the contiguous U.S., and numerically compute the optimal control for 180-day scenarios with up to 4830 nodes.