
Optimization and control
Optimization is about finding the best possible solution, given a set of resource constraints. For example, optimization is needed to find the least crowded path for a message as it travels between distant locations, to find the least-energy solution to move a spacecraft into orbit, or to find the most efficient computation graph for a GPU. Control is about using feedback from sensors or other sources to estimate the state of a system and ensure that it performs well in the presence of uncertainty. For example, feedback control is needed to minimize the risk of collapse of the power grid when it is subjected to constantly changing user demand, or to ensure the safe and efficient operation of an electric vehicle’s battery and motor systems under varying environmental conditions and driver demands. ECE researchers apply mathematical reasoning, statistical tools, simulation methods and computational technologies to solve problems throughout engineering and the sciences. We study the methods and algorithms that underlie these procedures in order to optimize and control modern engineered systems.
Faculty
- Eduardo Arvelo
- Jeremy Coulson
- Grigoris Chrysos
- Dominic Gross
- Ramya Korlakai Vinayak
- Kangwook Lee
- Bernard Lesieutre
- Paul Milenkovic
- Robert Nowak
- Dimitris Papailiopoulos
- Line Roald
- Bill Sethares
- Manish Singh
- Lei Zhou
Research labs and facilities
- Data Science Institute (DSI)
- Institute for Foundations of Data Science (IFDS)
- Machine Learning and Optimization Theory (MLOPT) Research Group
- Machines, Algorithms and Data Lab (MADLab)
- Wisconsin Power Systems (WISPO)

Exploring the best path
ECE PhD student Prajwal Bhagwat and Professor Gross are discussing the fault-response of a grid-connected power converter in power systems with significant share of renewable generation and power electronics.