I'm a PhD student in the CS program at University of Utah, advised by Dr. Daniel Brown. I graduated with an MS in Electrical and Computer Engineering from Carnegie Mellon University, where I was very fortunate to work under the guidance of Dr. Carlee Joe-Wong.

Education

PhD in Computer Science, August 2022 - Present (Expected May 2026)

University of Utah
M. S. in Electrical and Computer Engineering, August 2019 - December 2020

Carnegie Mellon University
B.E. in Electronics and Communication Engineering, August 2014 - June 2018

Panjab University

Research Interests

Reinforcement Learning from Human Feedback (RLHF)
Human-AI Interaction and Alignment
Applied Reinforcement Learning
Interpretability and Explainability in Deep RL (XRL)

Selected Awards

2023 - Won 1st place in Paradigm Challenge at Human-AI Teaming Hackathon organized by U.S. Army Research Lab.
2022 - Awarded Graduate Fellowship from School of Computing, University of Utah.
2018 - Awarded Honors degree in B.E. by Panjab University , given to top 5 students in the whole department for maintaining the highest GPA throughout 4 years consistently.

Publications

Can Differentiable Decision Trees Enable Interpretable Reward Learning from Human Feedback?

Akansha Kalra, Daniel S. Brown

In Reinforcement Learning Journal (RLJ) | Presented at the Reinforcement Learning Conference (RLC), Amherst Massachusetts, August 9–12, 2024

Interpretable Reward Learning via Differentiable Decision Trees

Akansha Kalra, Daniel S. Brown

Oral Presentation at ML Safety Workshop, NeurIPS 2022
Refer to RLJ|RLC publication for extended version of this work.

Machine Learning on Volatile Instances: Convergence, Runtime, and Cost Tradeoffs

Xiaoxi Zhang, Jianyu Wang,Li-Feng LeeTom Yang, Akansha Kalra, Gauri Joshi, Carlee Joe-Wong

In IEEE/ACM Transactions on Networking 30.1 (2021): 215-228