James Hong
Applied ML Scientist at Reve
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolJames Hong is an Applied ML Scientist in Palo Alto with a decade of experience building computer vision and large-scale ML systems, now focused on GenAI data scaling and model training at Reve. He earned BS, MS, and a PhD in Computer Science from Stanford, where he spent seven years driving research on visual analysis of hundreds of thousands of hours of TV news, fine-grained sports action recognition, and generative-image cropping. James blends research rigor with production engineering—his open-source work includes backend and DevOps contributions to projects like Apache Pinot and Stanford’s Puffer, improving real-time OLAP storage and parallelized streaming infrastructure. He’s comfortable across the stack: from data serialization and SQL-driven anomaly pipelines to signal-safe process orchestration and system maintainability. Known for translating large, messy video datasets into deployable models, he pairs academic depth with pragmatic, scalable implementation.
10 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Stanford University
English, Chinese
Github Skills (17)
apache-pinot10
c-language10
java10
javas10
parallel-processing10
sql10
cprogramming-language10
devops10
inotify9
database-design9
json-serialization9
bash8
system-design8
video-streaming6
machine-learning5
Programming languages (10)
Github contributions (5)
StanfordSNR/puffer
Jan 2018 - Mar 2018
Puffer is a free live TV streaming website and a research study at Stanford using machine learning to improve video streaming
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:51 commits, 4 PRs, 106 pushes in 1 month
Contributions summary:James significantly improved the efficiency and reliability of the `puffer` project by focusing on parallelizing processes and optimizing the underlying infrastructure. They refactored the `run_notifier.cc` file to run child processes in parallel, increasing the overall performance. The user also addressed zombie processes and improved the handling of signals within the `notifier` system. Further contributions involved the addition of tools for file cleanup and dependency checking, demonstrating a focus on the entire system's maintainability.
stanfordstreamingmachine-learningvideo-streamingvideo
apache/pinot
Aug 2015 - Sep 2015
Apache Pinot - A realtime distributed OLAP datastore
Role in this project:Back-end Developer
Back-end DeveloperContributions:16 commits in 1 month
Contributions summary:James contributed to the development of Apache Pinot, a real-time distributed OLAP datastore. The commits reveal work on fixing anomaly ranking after schema changes and the serialization/deserialization of result properties for anomaly detection. The code changes focus on the `AnomalyTable` class, including SQL query construction, data serialization, and table creation, demonstrating involvement in the backend data storage and retrieval components.
realtimedata-streamolapapachedatastore
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