Christopher Xie
Research Scientist, Reality Labs at Meta
Greater Seattle Area United States
Summary
👤
Senior🎓
Top SchoolChristopher Xie is a research scientist at Meta Reality Labs with 12 years of experience applying machine learning, computer vision, and robotics to real-world systems. He holds a Ph.D. from the University of Washington and built a research trajectory spanning industry labs (Meta, Google, NVIDIA, Microsoft) and academia, focusing on robot learning, nonstationary online learning, and vision-driven control. Christopher brings hands-on experience from projects ranging from Project Aura at Google to robot learning at UC Berkeley and scalable ML coursework contributions on GitHub, including a sentiment-prediction notebook for a machine-learning specialization. Based in the Greater Seattle Area, he combines deep theoretical training with practical implementation skills across internships and research roles, and has a track record of turning complex probabilistic and control ideas into working systems. A less obvious strength is his longevity in cross-disciplinary research teams, enabling him to bridge probabilistic inference, planning, and large-scale ML engineering.
12 years of coding experience
11 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Computer Science and Engineering, Doctor of Philosophy (Ph.D.) Computer Science and Engineering at University of WashingtonBachelor’s Degree Electrical Engineering and Computer Science, Bachelor’s Degree Electrical Engineering and Computer Science at University of California, BerkeleyMiddle College High
Github Skills (5)
decision-tree10
logistic-regression10
machine-learning10
python10
sentiment-analysis9
Programming languages (6)
Github contributions (5)
learnml/machine-learning-specialization
Feb 2016 - Mar 2016
Role in this project:
ML Engineer
ML EngineerContributions:19 commits, 23 pushes in 23 days
Contributions summary:Christopher created and added a course folder for the machine-learning specialization. The main contribution is the creation of an iPython notebook for predicting sentiment from product reviews using logistic regression. Further modifications include minor changes to the notebook, and adding more blank notebooks related to decision trees and boosting algorithms, suggesting a focus on implementing and understanding various machine learning models.
chrisdxie/uois
Sep 2019 - Sep 2021
Contributions:22 commits, 20 pushes, 3 branches in 2 years
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