Open to opportunities

Rohit Bokade

@rohitbokade

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PhD candidate in Industrial Engineering with expertise in reinforcement learning.

United States

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What I'm looking for

I seek a challenging role in a forward-thinking company that values innovation and collaboration, where I can leverage my research skills and contribute to impactful projects.

I am currently pursuing a PhD in Industrial Engineering at Northeastern University, where my research focuses on multi-agent reinforcement learning for urban traffic signal control. My thesis, titled 'Towards Practical Multi-Agent Reinforcement Learning for Urban Traffic Signal Control,' aims to optimize traffic management systems using advanced AI techniques. I have developed an open-source Python-based platform that integrates SUMO and CityFlow simulators, facilitating large-scale MARL research. My work has been published in reputable journals, showcasing my commitment to advancing the field.

In addition to my academic pursuits, I serve as a Principal Investigator for a pro bono research initiative focused on carbon capture technologies. I collaborate with climate-tech startups to design control frameworks that enhance energy efficiency and CO2 capture. My experience also includes developing deep learning models for real-time defect detection in manufacturing, significantly improving inspection accuracy and efficiency. I am passionate about applying my skills in machine learning and statistical modeling to solve complex engineering challenges.

Experience

Work history, roles, and key accomplishments

Current

Principal Investigator

Current

Independent / Collaborative Research

Feb 2025 - Present (1 year 4 months)

Leading a research initiative focused on carbon capture using Reinforcement Learning to optimize direct air capture (DAC) technologies. Collaborating with climate-tech startups to design control frameworks and overseeing the development of simulation environments, RL models, and benchmarking protocols.

Reinforcement Learning Direct Air Capture (DAC)Simulation RL Models Benchmarking

Research Assistant

Northeastern University

Sep 2018 - Apr 2025 (6 years 7 months)

Developed and open-sourced a Python platform for Multi-agent Reinforcement Learning (MARL) traffic signal control, integrating SUMO and CityFlow simulators. Implemented deep variational inference for inter-agent communication and developed a Graph-GMVAE framework for offline MARL. Also developed a deep learning model for real-time defect detection in manufacturing and co-authored a literature revi

Python Sumo CityFlow Multi Agent RL Deep Learning Variational Inference Graph GMVAE Thermal Imaging

Statistical Modeling Co-op

Woods Hole Oceanographic Institution

Jan 2018 - Aug 2018 (7 months)

Investigated computational statistics and metaheuristics to develop inverse models from coral growth patterns. Developed a toolbox using MATLAB and R for retrieving and visualizing climate data and optimized coral growth models, significantly reducing computation time. Co-authored a journal article analyzing the impact of ocean acidification on coral growth.

Metaheuristics Inverse Models Matlab R Climate Data Coral Growth Models Ocean Acidification Meta Heuristics

Industrial Engineering Intern

Kinetic Gears Pvt. Ltd.

Sep 2015 - May 2016 (8 months)

Conducted shop-floor analysis to identify efficiency issues and developed an improved fixture design. Executed stress and fatigue analysis to evaluate design longevity and optimize performance. Co-authored and presented a conference paper on the proposed design.

Efficiency Improvement Fixture Design Stress Analysis Fatigue Analysis Performance Optimization