A Clemson University professor is starting the new year with an honor that goes to leading scholars and professionals working in the field of computational intelligence.
Ganesh Kumar Venayagamoorthy was selected as a distinguished lecturer by the IEEE Computational Intelligence Society (CIS). Venayagamoorthy is the Duke Energy Professor of Power Engineering and Professor of Electrical and Computer Engineering at Clemson and a Fellow of the IEEE.
As a 2023-25 distinguished lecturer, Venayagamoorthy will present stimulating lectures on topics in the areas of his research specialties. Topics include: distributed artificial intelligence in the future of smart grids; swarm intelligence and applications in power and energy systems; and computational intelligence with emphasis on undergraduate education.
The idea behind the program is to give master’s and Ph.D. students suggestions to help solve their research problems and to promote computational tools and techniques to undergraduates.
As many as seven distinguished lecturers are appointed each year, and they serve three-year terms.
Venayagamoorthy expects to travel to universities and colleges in the IEEE regions where the IEEE CIS Chapters exist to give lectures, although which have not yet been determined.
Hai Xiao, chair of the Holcombe Department of Electrical and Computer Engineering, said Venayagamoorthy’s selection as distinguished lecturer was well deserved.
“This honor reinforces Dr. Venayagamoorthy’s position as a leading scholar in computational intelligence and smart-grid research,” Xiao said. “Serving as a distinguished lecturer will position him to recruit new talent to the field of computational intelligence and to assist rising scholars in their research. I offer him my most sincere congratulations.”
In addition to being selected as a distinguished lecturer, Venayagamoorthy also was recently elected to the Board of Governors for the International Neural Network Society, and he had a book that he co-authored come out in December. The title is, “Intelligent Data Mining and Analysis in Power and Energy Systems: Models and Applications for Smarter Efficient Power Systems.”
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