Home » News » Somdatta Goswami receives Johns Hopkins’ William H. Huggins Excellence in Teaching Award

Somdatta Goswami receives Johns Hopkins’ William H. Huggins Excellence in Teaching Award

The award honors faculty members for outstanding teaching at the undergraduate and graduate levels at Johns Hopkins University’s Whiting School of Engineering.

March 25, 2026

by Danielle McKenna

Somdatta Goswami, assistant professor in the Department of Civil and Systems Engineering (CaSE), has been selected as this year’s recipient of the William H. Huggins Excellence in Teaching Award, which recognizes outstanding teaching at both the undergraduate and graduate levels in the Whiting School of Engineering.

Goswami’s recognition is the result of a selection process involving input from students, faculty, staff, and alumni. The award reflects her contributions to the educational mission of CaSE and her commitment to fostering a strong culture of learning within the Whiting School of Engineering.

Faculty must have taught for at least three years before being nominated. Award criteria include actively engaging students, building community, impacting students’ lives, promoting diversity, and fostering a culture of inclusion.

“Somdatta is an exceptional teacher and mentor,” said Jamie Guest, the Hackerman Family Department Head of Civil and Systems Engineering. “Her courses are thought-provoking and well-organized, she makes herself available to students, and her approach has clearly had a positive impact on student learning and the department’s sense of community.”

“It is incredibly motivating to receive this award, and I’m grateful for the opportunity to learn alongside my students,” Goswami said. “In a time when the education landscape is rapidly evolving with tools like AI and large language models, it feels especially meaningful to support students as they develop the curiosity and critical thinking needed to navigate the changing world.”

Goswami holds a secondary appointment in the Department of Applied Mathematics and Statistics and is a member of the Data Science and AI Institute (DSAI), the Institute for Data-Intensive Engineering and Science (IDIES), and the Hopkins Extreme Materials Institute (HEMI). Her research interests focus on advancing the fields of scientific computing, computational mechanics, and machine learning, encompassing fundamental and applied aspects.