Debbie Liang
Researcher at University of California, Berkeley
Berkeley, California, United States
Summary
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Top SchoolDebbie Liang is a researcher and software engineer based in Berkeley with eight years of experience bridging empirical research and production ML systems. Currently pursuing a PhD in Quantitative Marketing at UC Berkeley, she studies platforms, creator economies, and recommendation systems using causal inference, field experiments, and structural models while experimenting with LLMs. Her industry work includes reducing recommendation latency at Instagram, building fraud-detection and large-scale data pipelines at Facebook and Yahoo, and deploying cloud robotics benchmarks in EECS research. She is comfortable shipping end-to-end systems—backend services, iOS prototypes, and DuckDB-enabled analysis pipelines—and has run field studies with thousands of participants. Notably, she blends deep statistical rigor from Harvard Business School collaborations with hands-on feature engineering and production optimization. Debbie’s background shows a rare combination of experimental design, scalable engineering, and applied ML aimed at understanding how algorithms shape real-world behavior.
7 years of coding experience
3 years of employment as a software developer
Master's degree Electrical Engineering and Computer Science, Master's degree Electrical Engineering and Computer Science at University of California, Berkeley
Github Skills (13)
sos5
latency5
matching4
golang4
low-latency4
api3
kubernetes3
infrastructure3
firestore2
flutter2
cloud-native2
firebase2
distributed-systems2
Programming languages (4)
Github contributions (5)
Debbieliang9/Debbieliang9.github.io
Sep 2018 - Feb 2021
Contributions:16 pushes, 1 branch in 2 years 5 months
Debbieliang9/anna
Jan 2020 - Sep 2020
A low-latency, cloud-native KVS
Contributions:106 pushes, 4 branches in 8 months
golanglatencykuberneteskvscloud-native
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