Hi!

I am a PhD student in the Machine Learning Department at Carnegie Mellon University, advised by Prof. Jeff Schneider. My research interests broadly include reinforcement learning and sequential decision making. My PhD research involves 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. Lately, I have been 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

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 (2023, 2024), ICRA 2023, ICML (2023, 2024), NeurIPS 2023, ECAI 2023, AISTATS 2024.

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