MMAE Faculty Honored with 2020 Excellence in Teaching Award
Ankit Srivastava, associate professor of mechanical and aerospace engineering in the Department of Mechanical, Materials, and Aerospace Engineering, is the recipient of the MMAE 2020 Excellence in Teaching Award for his commitment to inspiring students through impactful and contemporary teaching methods.
For Srivastava, teaching provides an opportunity to communicate the powerful connection between math and engineering in solving complex problems of relevance to society.
“I tell my students that they have powerful tools at their disposal—software, coding languages, algorithms, and more—which were scarcely available to engineers even 10 years ago, and that they would be doing their education a great disservice if they hesitate away from embracing it all,” says Srivastava.
In his experience, students find this approach to be much more engaging and builds excitement in the classroom. Srivastava says that he demands a lot from students in terms of learning new coding languages, which he describes as the languages of the future.
“I have no great philosophy for teaching. I just find some things exciting and try to communicate those to the students, and I see them respond positively,” he adds.
Srivastava says that the course he particularly enjoys teaching in the department is MMAE 350 Computational Mechanics. This course focuses on the mathematical tools that are fundamental for modern-day engineering students and recent graduates. However, if not taught correctly, he notes that students may not fully grasp the relevance of the material.
Srivastava explains that his primary goal as instructor is to help students understand the importance of exploring the use of numerical methods to solve engineering problems.
“In my teaching, particularly of this course, I have tried to be guided by this thought process. In my mind math is fun and exciting because it lets engineering students solve complex problems of the real world instead of just toy problems,” he adds. “It gives them the power to understand something deeply instead of developing just a superficial understanding.”
Srivastava’s research spans fundamental aspects of wave propagation in heterogeneous media, inverse material design for wave control, micromechanics and homogenization theory, and data-driven mechanics based upon deep learning algorithms.
