Aneesh C. Muppidi

Aneesh Muppidi

I am an undergrad at Harvard, majoring in Computer Science and Neuroscience and concurrently pursuing a Master’s in Computer Science. At Harvard's Kempner Institute for the Study of Natural and Artificial Intelligence, I am a KURE Undergrad Research Fellow and advised by Prof. Samuel Gershman. At the Harvard Computational Robotics Lab, I am advised by Prof. Heng Yang. I've also completed research internships at MIT BCS with Ila Fiete and Microsoft Healthcare R&D.

My driving question is simple: how can agents plan for the unknown? I aim to explore this through the lenses of optimization, reinforcement learning, and world models.

[Email] / [Google Scholar] / [Github] / [Follow on X] / [LinkedIn]

Recent News

My work on Parameter-Free Lifelong Reinforcement Learning was accepted to RLC (Spotlight), RSS (Spotlight), and TTIC workshops. Checkout the website and code.

Preprints and Publications

Fast TRAC: A Parameter-free Optimizer for Lifelong Reinforcement Learning
Aneesh Muppidi, Zhiyu Zhang, Heng Yang
arXiv preprint RL optimization vision-based
arXiv, Bibtex, Code, Website

                            
							@misc{muppidi2024pickpaceparameterfreeoptimizer,
								  title={Pick up the PACE: A Parameter-Free Optimizer for Lifelong Reinforcement Learning}, 
								  author={Aneesh Muppidi and Zhiyu Zhang and Heng Yang},
								  year={2024},
								  eprint={2405.16642},
								  archivePrefix={arXiv},
								  primaryClass={cs.LG}
								  url={https://arxiv.org/abs/2405.16642}, 
							}

Resampling-free Particle Filters in High-dimensions
Akhilan Boopathy, Aneesh Muppidi, Peggy Yang, Abhiram Iyer, William Yue, Ila Fiete
ICRA 2024 optimization robotics vision-based
arXiv,Bibtex

Speech Emotion Recognition using Quaternion Convolutional Neural Networks
Aneesh Muppidi, Martin Radfar
ICASSP 2021 vision-based
arXiv, Bibtex

                         @misc{muppidi2021speechemotionrecognitionusing,title={Speech Emotion Recognition Using Quaternion Convolutional Neural Networks}, author={Aneesh Muppidi and Martin Radfar},year={2021},eprint={2111.00404},archivePrefix={arXiv},primaryClass={cs.SD}url={https://arxiv.org/abs/2111.00404}, }

Robotics/RL Classes and Projects

Final Project: Let's Learn Agency: Learning Emergent Agent and Non-Agent Trajectory Representations
MIT 6.8200 with Pulkit Agrawal
mixed RL
Project Report, Video Presentation

Final Project: Generating Suboptimal Expert Demonstrartions with Large Language Models
MIT 6.4212 with Russ Tedrake; project advised by Lirui Wang
positive robotics
Project Report, Video Presentation

Final Project: Rapid Learning Mechanisms and Neural Representations in Reinforcement Learning
Harvard PSY 2350R with Sam Gershman; project advised by Jay Henning
mixed RL
Project Report, Code, Research Notebok

Final Project: Diffusion Policy for Classical Control Problems
Harvard ES158 with Heng Yang
negative RL robotics vision-based
Project Report, Code, Slides

Final Project: Visualizing Collaborative Multi-Agent Reinforcement Learning
Harvard CS271 with Johanna Beyer and Hanspeter Pfister
positive RL
Project Report, Slides