I am a PhD candidate in Computer Science and Engineering at the University of Michigan, Ann Arbor. I am advised by Prof. John E. Laird and am a member of the Soar lab.

I am currently on the job market for postdoctoral and research scientist roles starting Fall 2023.

  preetir (at) umich (dot) edu

Curriculum Vitae


My area of research is Human-Robot Interaction; I study non-expert mental models of Interactive Task Learning (ITL) robots in a situated teaching interaction setting.

I study non expert mental models of robots in a situated teaching interaction setting. I conduct human participant studies to study the role of robot failures on the non-expert’s mental model of the robot. My goal is to build interaction mechanisms in an Interactive Task Learning robot that leverage natural human interaction patterns and help the human build a better mental model of the robot.


Past Projects

Mining Insights from Hardware Errata Documents
We use errata documentation to identify interesting patterns and conclusions about product bugs, such as common sources of errors. Over the course of this project, we created a database of over 2,000 different ARM errata and experimented with natural language processing methods ranging from Word2Vec, non-negative matrix factorization and recurrent neural networks. This project was a part of EECS 573 (Microarchitecture) in Fall 2016 implemented as a three person group. Code(GitHub)

Motivated Learning – Replication project
This is a replicated project based on the study specified in “Graham, J., Starzyk, J. A., Ni, Z., He, H., Teng, T. H., & Tan, A. H. (2015, July). A comparative study between motivated learning and reinforcement learning. In 2015 International Joint Conference on Neural Networks (IJCNN)(pp. 1-8). IEEE.” in order to test the hypothesis that Motivated Learning earns a higher average reward than Reinforcement Learning in the custom dynamic environment specified. The hypothesis was replicated successfully. This project was a part of EECS 592 (Advanced Artificial Intelligence) in Winter 2016.

Index-based Load Optimization in MySQL
Implementation of an automated drop-and-rebuild-indexes scheme around a load operation to improve performance of loading huge data into databases in MySQL. This project was a part of EECS 584 (Advanced Database systems) in Fall 2015 implemented as a two person group. Code(GitHub)