Laura Graesser
Senior Research Scientist at Google DeepMind
Montreal, Quebec, Canada
Summary
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Rockstar🎓
Top SchoolLaura Graesser is a Senior Research Scientist at Google DeepMind with a decade of experience advancing reinforcement learning and robot learning. She co-authored the textbook Foundations of Deep Reinforcement Learning and contributed significant code to its companion PyTorch library SLM-Lab, reflecting a blend of rigorous theory and production-quality implementation. Her career spans research and engineering roles at Google and robotics work including vision-based autonomous driving at NVIDIA, showing strength in both applied systems and core ML research. Trained in computer science (NYU) after a PPE degree from Oxford, she brings uncommon cross-disciplinary thinking to complex learning problems and a track record of improving code quality, testing, and modular RL infrastructure.
10 years of coding experience
7 years of employment as a software developer
Master of Science (MS), Computer Science, Master of Science (MS), Computer Science at New York UniversityBA, Politics, Philosophy & Economics, BA, Politics, Philosophy & Economics at Oxford University
Github Skills (8)
neural-network10
deep-reinforcement-learning10
pytorch10
testing10
ppp8
dqn8
reinforcement-learning8
pytest8
Programming languages (1)
Github contributions (5)
kengz/SLM-Lab
Nov 2017 - Sep 2019
Modular Deep Reinforcement Learning framework in PyTorch. Companion library of the book "Foundations of Deep Reinforcement Learning".
Role in this project:
ML Engineer
ML EngineerContributions:2 reviews, 353 commits, 160 PRs in 1 year 10 months
Contributions summary:Laura primarily focused on implementing and testing neural network components for a deep reinforcement learning framework. Their contributions included the implementation of a multi-layer perceptron network and tests to verify its functionality. They also refactored and parameterized existing unit tests, demonstrating a focus on code quality and maintainability. Furthermore, the user added replay memory implementation for the algorithm.
deep-reinforcement-learningbenchmarkframework-learningreinforcementmodular
lgraesser/MultimodalGame
Feb 2018 - Sep 2018
Contributions:259 commits, 3 PRs, 181 pushes in 7 months
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