Hi!

I am an Applied Scientist on the RAD Movement Science team at Amazon Robotics. I work on ML and RL research for Amazon’s large fleet of mobile robots!

I graduated with a PhD from the Machine Learning Department at Carnegie Mellon University, advised by Prof. Jeff Schneider. My PhD research involved developing adaptive decision making algorithms in decentralized and asynchronous multi-agent systems for robotics search and tracking applications under realistic sensing, communication and resource considerations. I also spent some time during my PhD exploring the usefulness of generative modeling based approaches in decision making. I am always happy to chat, so please reach out if we have shared research interests!

In the summer of 2022, I enjoyed working on reinforcement learning for mechanism design as a research intern at Salesforce.

Previously, I graduated from the Indian Institute of Technology Kharagpur with a combined Bachelors (Hons.) and Masters Dual Degree in Computer Science and Engineering. I was advised by Prof. Pabitra Mitra for my undergraduate thesis.

Publications

Cost-aware Diffusion Active Search
Arundhati Banerjee and Jeff Schneider
Under review, 2025.

Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents
Arundhati Banerjee and Jeff Schneider
ICRA, 2024. | arxiv

MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning
Arundhati Banerjee, Soham Phade, Stefano Ermon, Stephan Zheng
TMLR, 2023. | paper

Cost Aware Asynchronous Multi-Agent Active Search
Arundhati Banerjee, Ramina Ghods, Jeff Schneider
ECAI 2023. | paper | talk

Multi-Agent Active Search using Detection and Location Uncertainty
Arundhati Banerjee, Ramina Ghods, Jeff Schneider
ICRA 2023. | arxiv | website

Decentralized Multi-Agent Active Search for Sparse Signals
Ramina Ghods, Arundhati Banerjee, Jeff Schneider
UAI 2021. | paper

Artificial neural network for identification of short-lived particles in the CBM experiment
Arundhati Banerjee, Ivan Kisel, Maksym Zyzak
Special Issue: Learning to Discover, International Journal of Modern Physics A (IJMPA), 2020. | paper

DeepTagRec: A Content-cum-User Based Tag Recommendation Framework for Stack Overflow
Suman Kalyan Maity, Abhishek Panigrahi, Sayan Ghosh, Arundhati Banerjee, Pawan Goyal, Animesh Mukherjee
ECIR 2019. | paper

Workshop presentations

Decentralized and Asynchronous Multi-Agent Active Search and Tracking when Targets Outnumber Agents
Arundhati Banerjee and Jeff Schneider
Workshop on Adaptive Experimental Design and Active Learning in the Real World @ NeurIPS 2023 | paper

MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning
Arundhati Banerjee, Soham Phade, Stefano Ermon, Stephan Zheng
Generalization in Planning (GenPlan) Workshop @ NeurIPS 2023 | paper

Cost Aware Asynchronous Multi-Agent Active Search
Arundhati Banerjee, Ramina Ghods, Jeff Schneider
MODeM Workshop @ ECAI 2023 | talk

Multi-Agent Active Search and Rescue
Ramina Ghods, Arundhati Banerjee, William Durkin and Jeff Schneider
3rd Robot Learning Workshop : Grounding Machine Learning Development in the Real World @ NeurIPS 2020 | poster

Teaching

Scalability in Machine Learning (10-745)
Teaching Assistant
Carnegie Mellon University, Spring 2022

Introduction to Machine Learning (10-701)
Teaching Assistant
Carnegie Mellon University, Spring 2021

Service

Reviewer for ICLR, ICRA, ICML, NeurIPS, ECAI, AISTATS, T-RO, RAL, TMLR.

Mentored students as part of the CMU Undergrad AI Mentoring Program 2021, 2022, 2023.