Raj Palleti
I'm a Robotics/NLP researcher at Stanford University currently working on robotic manipulation via language and human-robot interaction. I previously did research at Stanford ML Group and Stanford Hospital working with Percy Liang, Dorsa Sadigh, and Jure Leskovec on various projects spanning healthcare ML, supply chain optimization, and language-guided robotics.
My work focuses on making robots more adaptable and intuitive to interact with through natural language. I'm particularly interested in shared autonomy approaches that combine human input with learned models to enable more reliable and sample-efficient robot learning. A key focus has been developing methods for robots to understand and incorporate natural language corrections during task execution.
Beyond robotics, I work on applications of machine learning in healthcare, including using deep learning and transfer learning for ECG analysis and risk prediction. I'm also interested in supply chain optimization, where I've worked on using graph neural networks to model production functions and forecast transactions between firms.
Publications
Learning production functions for supply chains with graph neural networks
Serina Chang, Zhiyin Lin, Benjamin Yan, Swapnil Bembde, Qi Xiu, Chi Heem Wong, Yu Qin, Frank Kloster, Alex Luo, Raj Palleti, J. Leskovec
arXiv.org 2024
No, to the Right: Online Language Corrections for Robotic Manipulation via Shared Autonomy
Yuchen Cui, Siddharth Karamcheti, Raj Palleti, Nidhya Shivakumar, Percy Liang, Dorsa Sadigh
IEEE/ACM International Conference on Human-Robot Interaction 2023
Transfer learning enables prediction of myocardial injury from continuous single-lead electrocardiography
B. Jin, Raj Palleti, Siyu Shi, A. Ng, J. Quinn, P. Rajpurkar, David Kim
J. Am. Medical Informatics Assoc. 2022
Shared Autonomy for Robotic Manipulation with Language Corrections
Siddharth Karamcheti, Raj Palleti, Yuchen Cui, Percy Liang, Dorsa Sadigh
Investigating Language Model Cross-lingual Transfer for NLP Regression Tasks Through Contrastive Learning With LLM Augmentations
Raghav Ganesh, Raj Palleti