Summary
Rahul Patel is a PhD candidate at the University of Toronto specializing in machine learning-driven discrete optimization, with 11 years of experience bridging research and applied engineering. He develops novel methods to accelerate multiobjective and stochastic integer programming solvers under Prof. Elias Khalil, building on a master's project co-supervised by Andrea Lodi and Yoshua Bengio that produced an ML-based primal heuristic for stochastic integer programs. His background spans industry and academia—from applied science work on fulfillment optimization at Amazon to research at Mila and hands-on ML engineering—giving him a rare blend of production focus and theoretical depth. Rahul regularly teaches data structures, algorithms, and stochastic optimization, reflecting strong communication skills and commitment to training the next generation. Notably, he pairs optimization expertise with practical system-building experience (including Android and IVR projects), enabling solutions that scale from prototype to deployment.
11 years of coding experience
3 years of employment as a software developer
Master's degree, Applied Mathematics, Master's degree, Applied Mathematics at École Polytechnique de Montréal
Nano Degree, Andriod Basics, Nano Degree, Andriod Basics at Udacity
Doctor of Philosophy - PhD, Machine Learning and Discrete Optimization, Doctor of Philosophy - PhD, Machine Learning and Discrete Optimization at University of Toronto
Bachelor of Technology (B.Tech.), Information and Communication Technology, Bachelor of Technology (B.Tech.), Information and Communication Technology at Ahmedabad University
English, Gujarati, Hindi