Neev Parikh

Neev Parikh

Graduate/Undergraduate Student

Brown University


Hi! I’m a concurrent graduate/undergraduate student at Brown University, with research interests in reinforcement learning, robotics, distributed RL and planning. I currently work in the Intelligent Robot Lab at Brown University, led by Professor George Konidaris. I’m also interested in industry work for a full-time position (2022). For a resume, please click here.


  • Artificial Intelligence
  • Distributed RL
  • Planning, Robotics


  • M.Sc. in Computer Science, 2022 (Concurrent)

    Brown University

  • B.Sc. in Computer Science, 2022 (Concurrent)

    Brown University

Recent Publications

*equal contribution

Deep Radial-Basis Value Functions for Continuous Control

We introduce deep radial-basis value functions (RBVFs): value functions learned using a deep network with a radial-basis function (RBF) output layer.

Graph Embedding Priors for Multi-task Deep Reinforcement Learning

We leverage graph-encoded object priors to capture this property and improve the performance of reinforcement learning agents across multiple tasks. We introduce a novel, flexible architecture that utilizes graph convolutional networks (GCNs), which provide a natural method to combine relational information over connected nodes.

Learning Markov State Abstractions for Deep Reinforcement Learning

We introduce a method of learning an abstract state representation for Markov Decision Processes (MDPs) with rich observations.

Locally Observable Markov Decision Process

We introduce a novel structured formulation, the Locally Observable Markov Decision Process, which assumes that partial observability stems from limited sensor range—objects outside sensor range are unobserved, but become fully observed once they are within sensor range.



Research Engineering Intern

Common Sense Machines

May 2021 – Present Boston, MA
Working on large-scale machine learning models (Seq2seq, CV, etc.) for converting video to 3D models

Research assistant

Intelligent Robot Lab

Jun 2020 – Present Providence, RI

Research assistant at Intelligent Robot Lab (irl) at Brown University, led by professor George Konidaris.

  • Current projects:

    • New mathematical framework (LOMDPs) for tasks in robot domains
    • Unsupervised representation learning for Atari task suite
    • Graph-based priors for improving multi-task performance

Teaching Assistant

Department of Computer Science, Brown University

Jan 2020 – May 2020 Providence, RI

Worked as a teaching assistant for Computer Vision (CSCI 1430), taught by Professor James Tompkin.

  • Guided 3 teams in their final projects
  • Revamped Project 5 and helped update 4 other projects for the class
  • Held weekly office hours, biweekly grading of projects and homeworks

Teaching Assistant

Department of Computer Science, Brown University

Sep 2019 – Dec 2019 Providence, RI

Worked as a teaching assistant for Introduction to Reinforcement Learning (CSCI 2951F), taught by Professor Michael Littman.

  • Extended course library framework simpleRL fixing common bugs and improving example visualizations
  • Designed 3 homeworks for the class
  • Guided 3 teams in replicating papers from NeurIPS 2019 for the NeurIPS 2019 Reproducibility Challenge

Machine Learning Intern

Myelin Foundry

Jun 2019 – Aug 2019 Bangalore, India

Worked on a cutting-edge, deep-learning based pipeline to augment VFX workflows.

  • Worked to develop cutting-edge, deep-learning based pipeline to augment VFX workflows for a POC product
  • Researched and managed a company-wide, cloud-compute platform, reducing potential monthly costs by 70%
  • Helped transition MLOps to Microsoft Azure
  • Implemented DeepLabv3+ from ECCV 2018 to develop SOTA pipelines for semantic segmentation tasks
  • Achieved 90% in business-aligned metrics with reasonable inference time

Software Engineering Intern


Jul 2018 – Aug 2019 Bangalore, India
  • Developed integrated data visualization tool in Typescript with ReactJS and Django (Python).
  • Researched RFM analysis to gather business insights using Python.