Krishna Kumar
Krishna Kumar is an Associate Professor in the Civil, Architecture, and Environmental Engineering at the University of Texas at Austin. Krishna’s work involves developing exascale micro and macro-scale numerical methods for modeling natural hazards. Krishna also develops Scientific Machine Learning algorithms: Graph Network Simulators and Differentiable programming to accelerate numerical methods and solve optimization, design, and inverse problems.
Initiative
Teaching Engineering through Murder Mysteries and Personalized AI Tutor
CE 357: Introduction to Geotechnical Engineering is a third year required undergraduate course that has traditionally been a challenging course for students due to its abstract nature. The average course rating for CE 357 is 3.8 in the last twenty years. I have successfully transformed the lecture modules to achieve a significant increase in interest and students’ performance in the course. Although preliminary work looks promising, I want to scientifically evaluate the effectiveness of the course and publish the findings.
Impacts
Additional Grant Funding (Cockrell School of Engineering)
Alongside Dr. Berkin Dortdivanlioglu, PTF Krishna Kumar received a grant from the Cockrell School of Engineering to use AI for persoanlized tutors in their introduction to programming course. They have since built a personalized tutor for this course, which is available for students privately. They are now in the process of making the tutor public through the Texas Advanced Computing Center (TACC).
Preliminary Study on Teaching an Engineering Course Through Murder Mysteries (ASEE Gulf-Southwest)
PTF Krishna Kumar presented this paper at the American Society of Engineering Education (ASEE) Gulf-Southwest Section Annual Conference in March 2023. This paper discusses usage and results of the Murder Mystery-style assignments Kumar has developed as part of his PTF Initiative.
Read the complete paper <here,> or find the abstract below.