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
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.
News
- August 2021: Attending RO-MAN 2021 to present our work on Unpacking Human Teachers' Intentions For Natural Interactive Task Learning.
- June 2021: I gave a talk at the 41st Soar Workshop describing my intended thesis direction.
- March 2021: Super thrilled to be a part of the HRI Pioneers '21 cohort and present a poster at my first ever HRI conference!
- February 2021: Excited to be attending the AAAI Doctoral Consortium and presenting a poster at the AAAI-21 conference!
- August 2020: Attending the online ACS 2020 workshop and serving as the technical chair.
- July 2020: Our paper on understanding human teaching intentions to design ITL robots was accepted to the RSS 2020 AI+ACR workshop.
- May 2020: Excited to start my remote internship with the System Sciences lab at PARC!
- August 2019: Attending ACS 2019 (August 2-5) at Massachusetts Institute of Technology in Cambridge, Massachusetts.
- July 2019: Our paper Towards using transparency mechanisms to build better mental models was accepted to the 7th Goal Reasoning Workshop at ACS 2019!
- May 2019: I gave a talk at the 39th Soar Workshop presenting results from my internship project.
- April 2019: Participating in the 2019 CRA-W Grad Cohort Workshop (April 12-13) in Chicago.
- June 2018: Our extended abstract Establishing Common Ground with Learning Robots was accepted to the RSS 2018 workshop on Models and Representations for Natural Human-Robot Communication!
- May 2018: I gave a talk at the 38th Soar workshop presenting results from a recent pilot study. (slides)
- Feb 2018: I will be working as a research intern at Intel Labs, Santa Clara this summer!
- Oct 2017: My research is once again funded by Intel Corporation for FY 2017!
- June 2017: I gave a talk at the 37th Soar workshop on my current research project. (slides)
- July 2016: My research is funded by Intel Corporation for FY 2016.
- June 2016: I gave a talk at the 36th Soar workshop. (slides)
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)