The Alexander von Humboldt Foundation has selected ECE professor Mohamed Ali Belabbas as the recipient of one of its 2024 Friedrich Wilhelm Bessel Research Awards. The Humboldt Foundation supports collaborations of outstanding researchers from around the world with German scientists. It presents only about 20 Bessel Research Awards per year worldwide across all research disciplines. The awards are given to “internationally renowned academics from abroad in recognition of their outstanding accomplishments in research.”
Written by Jenny Applequist
The award recognizes his work on geometric approaches to optimization.
The Humboldt Foundation supports collaborations of outstanding researchers from around the world with German scientists. It presents only about 20 Bessel Research Awards per year worldwide across all research disciplines. According to its website, the awards are given to “internationally renowned academics from abroad in recognition of their outstanding accomplishments in research.”
“I am very happy to have received this award,” said Belabbas, who is an Associate Professor in Electrical & Computer Engineering and affiliated with the Coordinated Science Laboratory. “I know these are highly sought-after, and I am honored to have been selected.”
The Foundation will also support multiple visits by Belabbas to Aachen, Germany, where he will collaborate with Prof. Dr.-Ing. Christian Ebenbauer, the Chair of Intelligent Control Systems at RWTH Aachen University.
Belabbas is an expert in geometric control, and a primary goal of the collaboration will be to find new solutions for extremum-seeking—an area in which Ebenbauer is a leading expert—that draw on geometric control principles.
Extremum-seeking is an algorithmic approach that makes it possible to perform optimization without knowing the gradient of a function. The gradient is an analytic quantity that provides useful information about a problem, so gradients are used whenever feasible. However, for many applications, the computation of the gradient would be either too costly to do, or simply impossible—and that’s where extremum-seeking comes in.
Belabbas said that techniques from geometric control, differential geometry, and Riemannian geometry all have potential applications to the problems in extremum-seeking and distributed optimization that he and Ebenbauer plan to focus on first. “I hope that bringing geometric and distributed control to this area will push the boundaries of extremum-seeking,” he said.
Belabbas added that for the next phase of the collaboration, he thinks they should go on to consider Hamiltonian control systems as well. They are needed for certain problems, such as trajectory optimization, that are important in robotics and in other areas that are attracting growing attention, such as spaceflight and quantum control.
“So this is also something that I hope to investigate jointly with [Ebenbauer],” he said. “These questions are also related to optimization. So this umbrella of a geometric approach to optimization covers all the bases that we have here.”
“In the long term,” Belabbas said, “applications of this research may range from autonomous driving to trajectory design for spacecraft.”