Katelyn Gao
Generative AI Research Engineer
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolKatelyn Gao is a generative AI research engineer based in San Francisco with over a decade of experience applying machine learning and statistics to real-world problems. After earning a Ph.D. in Statistics from Stanford and a BS in Mathematics and Economics from MIT, she spent eight years at Intel Labs rising to Staff AI Research Scientist, focusing on 3D generation, meta-learning, and reinforcement learning. She now drives generative AI research at NVIDIA, bringing a rare combination of theoretical rigor and product-minded experimentation. Katelyn’s work bridges foundational generative models and applied systems research, with a track record of moving ideas from intern projects to production-scale research efforts. Colleagues note her ability to translate complex statistical concepts into practical model improvements and to mentor cross-disciplinary teams. She combines deep academic training with an engineer’s instinct for measurable impact in production ML.
10 years of coding experience
8 years of employment as a software developer
Bachelor of Science - BS Mathematics Economics, Bachelor of Science - BS Mathematics Economics at Massachusetts Institute of TechnologyDoctor of Philosophy (Ph.D.) Statistics, Doctor of Philosophy (Ph.D.) Statistics at Stanford University
Chinese, Spanish
Github Skills (44)
gazebosim9
robotics9
webots9
robot9
gazebo-simulator9
deep-reinforcement-learning8
robotics-simulation8
autonomous-robots8
mixed-models8
reinforcement-learning8
robot-simulator8
simulation8
meta7
mujoco7
benchmark7
Programming languages (2)
Github contributions (5)
isl-org/MetaLearningTradeoffs
Nov 2020 - Oct 2022
Source code for the NeurIPS 2020 Paper: Modeling and Optimization Trade-off in Meta-learning.
Contributions:22 commits, 3 PRs, 1 push in 1 year 10 months
meta-learningneurips-2020reinforcement-learningmetaneurips
kxgao/scalable-crossed-mixed-effects
Jun 2017 - Jun 2017
Scalable estimation for crossed mixed models
Contributions:7 commits, 7 pushes, 1 branch in 1 day
estimationscalablemixed-modelsmachine-learning
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