Wade Genders is a Machine Learning Engineer with eight years of experience specializing in deep reinforcement learning, traffic microsimulation, and computer vision, currently applying those skills at Flow Labs. He holds a PhD from McMaster University where his thesis developed AI-driven adaptive traffic signal control, and has translated research into real-world impact—most notably field-testing AI signal control that yielded multimillion-dollar annualized savings. Comfortable moving models from lab to deployment, he has built and tested production traffic-control software with transportation agencies and led ITS research teams at the University of Toronto. Wade combines a strong mathematical and machine-learning foundation with interests in philosophy, and maintains an academic presence on Google Scholar reflecting his ongoing research-oriented approach.
8 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Civil Engineering, Doctor of Philosophy (Ph.D.), Civil Engineering at McMaster University
SUMO adaptive traffic signal control - DQN, DDPG, Webster's, Max-pressure, Self-Organizing Traffic Lights
Contributions:14 commits in 1 month
signalddpgadaptivelightspressure
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Wade Genders - Machine Learning Engineer at Flow Labs