Eric Zhu
Seattle, Washington, United States
Summary
🤩
RockstarEric Zeng is a computer security and privacy researcher and software engineer with 13 years of experience who focuses on real-world harms people face on online platforms and builds prototypes and measurements to mitigate them. He holds a PhD from the University of Washington and has published at USENIX Security, CHI, IMC, and NDSS. Eric blends full‑stack engineering (React, TypeScript, Chromium), infrastructure (Kubernetes, Docker), ML/data science (LLMs, R, pandas) and human‑centered methods to design and evaluate usable security features—from HTTPS ecosystem fixes to smart‑home authentication. On the open‑source side he contributes to Microsoft’s autogen project (improving flaml test coverage and API clarity) and to ekzhu/datasketch where he implemented and optimized MinHash and HyperLogLog primitives for scalable similarity and cardinality estimation. Now a Postdoctoral Associate in Washington, D.C., he brings a pragmatic, measurement-driven approach that connects user research, statistical analysis, and production engineering.
14 years of coding experience
Github Skills (19)
unittesting10
pytest10
python10
testing10
data-serialization10
data-structure10
serialization10
computer-engineering10
hyperloglog10
unittest10
data-structures10
unit-tests10
unittests10
unit-test10
data-science9
Programming languages (11)
Github contributions (5)
microsoft/autogen
Sep 2023 - Apr 2025
A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
Role in this project:
Software Engineer & Test Automation
Software Engineer & Test AutomationContributions:19 releases, 2009 reviews, 859 PRs in 1 year 6 months
Contributions summary:Eric's commits primarily focused on enhancing the robustness and testing capabilities of the `flaml` submodule within the `autogen` repository. They implemented new tests for training logs, Python loggers, and the overall `flaml` module, thereby increasing test coverage. Furthermore, the user contributed to fixing code, clarifying API documentation and modifying core features such as structured output.
agenticagentic-agiagentsaiautogen
ekzhu/datasketch
Mar 2015 - Aug 2022
MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble and HNSW
Role in this project:Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:28 releases, 31 reviews, 191 commits in 7 years 6 months
Contributions summary:Eric primarily contributed to the core data structure and related functionality of the project, evidenced by the addition and refinement of MinHash, HyperLogLog, and related similarity measures. Their work included the implementation and optimization of these probabilistic data structures, serialization capabilities, and benchmark creation for measuring performance and accuracy. The user also demonstrated expertise in applying these data sketches for cardinality estimation and Jaccard similarity calculations.
operationpythonensembledata-summarydatalog
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