Research Interests
Scalable Modeling and Optimization
Creating, solving, analyzing, and deploying optimization applications are core activities in systems engineering. The scale and complexity of optimization problems however, is increasing with computational advances that target applications in coupled connected infrastructure networks, economics, robotics, supply chains and control systems.
The key challenges pertaining to larger scale optimization problems comes from the difficulty in modeling complex interactions and hierarchies, and exploiting tractable solution strategies that can handle large-scale (potentially nonconvex) formulations.
This research studies graph-based approaches to formulating optimization models that facilitate complex model management, systematic problem decomposition, and interfacing with parallel optimization solvers.
Cyber-Physical Systems
Modeling and simulating cyber-physical systems is becoming increasingly important, but capturing interdependencies between cyber and physical systems in a coherent manner is technically challenging.
Physical systems (such as chemical processes) can be driven by control systems, which in turn are cyber systems comprised of computing devices (e.g. sensors, controllers, actuators) that execute tasks (e.g., data processing, control action computation) and that exchange information (e.g., measurements and control actions) through a communication network.
This research explores abstractions that facilitate the modeling and simulation of cyber-physical systems. In doing so, we show how new abstractions can facilitate the implementation of optimization and control algorithms and their simulation in virtual environments that involve distributed, centralized, and hierarchical computing architectures.
