Rupali Bhati is a PhD student and independent reinforcement learning researcher based in Boston with eight years of experience applying RL across healthcare, energy, finance, and multi-agent settings. Her work spans academic research at Northeastern, Université Laval and Mila, plus industry impact—using DQN to boost HVAC efficiency by over 70% and developing risk-averse CARL framed as a Stackelberg game. She teaches and mentors regularly, translating complex RL concepts into workshops and courses for students and professionals. Projects include disentangling non-stationarity in multi-agent games like Diplomacy and interpretable clinical models using GBDTs with SHAP, revealing a blend of theoretical depth and practical interpretability. Colleagues describe her as a researcher who pairs rigorous theory with hands-on solutions that drive measurable operational gains.
8 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at Northeastern University
Master's in Science, Computer Science, Master's in Science, Computer Science at Université Laval
Bachelor of Technology (B.Tech.), Electronics and Communication, Bachelor of Technology (B.Tech.), Electronics and Communication at Delhi Technological University(formerly Delhi College of Engineering)
Noida Batch for Perceptron (Machine Learning) Summer 2018
Contributions:7 pushes in 1 month
perceptronmachine-learningsummerbatch
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Rupali Bhati - PHD Student at Northeastern University