Applied Mathematics Professor Honored as SIAM Fellow
The Society for Industrial and Applied Mathematics has named Illinois Tech Professor of Applied Mathematics Fred Hickernell as a member of the 2026 class of SIAM fellows in recognition of his outstanding research and service to the community.
“I was humbled and somewhat surprised to hear the news,” Hickernell says. “I am grateful to God for the many times that He has given me new research ideas and unexpected academic opportunities. I am also grateful to my colleagues who nominated me and wrote in support of my nomination.”
Hickernell is joined by 24 research peers in this year’s SIAM fellows class. The society cited Hickernell’s mathematical and statistical innovations and contributions to the analysis of high-dimensional integration and approximation, as well as his outstanding scientific leadership. He also has served SIAM through mentoring, journal editorship, conference organization, and academic administration.
“It is an honor to be recognized by my peers,” he says. “It is also a reflection on the great work being done at Illinois Tech and in our discipline. I am grateful for the many collaborations with students and scholars. Let me also thank the government sponsors and the Illinois Tech alumni who have supported our research financially.”
Hickernell will be recognized during a reception at the 2026 SIAM Annual Meeting in Cleveland in July 2026. SIAM fellows are feted for their contributions in advancing the fields of applied mathematics, computational science, and data science.
Hickernell’s research group at Illinois Tech has been developing software libraries that gives researchers access to cutting-edge computational methods to solve their finance, engineering, and scientific problems. These efforts have conveniently consolidated software that promotes important theory that supports novel computational methods that help solve high-dimensional integration and function approximation problems, or problems with large amounts of variables and need a more even distribution of data points and estimations. Making these methods easily available was lacking in the past.
“As computational power increases, the scientific and engineering problems of interest become more complex and require more flexible and robust computational methods, especially in the realm of uncertainty quantification,” Hickernell says. “Our research group and collaborators are attempting to contribute to theory, methodology, and software to have a complete problem-solving ecosystem for high-dimensional integration and function approximation.”
The efforts of Hickernell and his research have helped build open-source libraries used by researchers, such as the Guaranteed Automatic Integration Library for MATLAB and qmcpy in Python.
