Simon Chan
Managing Partner at FirsthandVC
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolSimon Chan is a serial entrepreneur and technology investor with 13+ years of hands-on engineering and 15 years of tech leadership across London, Hong Kong, Beijing, Guangzhou and the SF Bay Area. He co-founded PredictionIO, an open-source machine learning server later acquired by Salesforce, and led product and platform work for Salesforce Einstein, demonstrating rare fluency from research-quality ML (PhD, UCL) to shipped production systems. Today he runs FirsthandVC and produces the AI Agent Conference, backing and convening founders building agentic AI while also personally angeling into notable exits like Caper and Indus.ai. Simon combines full-stack product engineering experience—contributing UI and training workflows to PredictionIO—with deep operator experience scaling teams and product-market fit in China and the U.S. He frequently sits at the intersection of academia, open source and venture, leveraging a global network to accelerate early-stage AI businesses.
13 years of coding experience
14 years of employment as a software developer
University College LondonB.S.E. Computer Science, B.S.E. Computer Science at University of MichiganStanford StartXMozilla WebFWDMaster of Science - MS (Incomplete) Management Science & Engineering, Master of Science - MS (Incomplete) Management Science & Engineering at Stanford University
Github Skills (9)
html10
css10
javascript10
predictionio10
scala10
backbonejs9
backbone9
machine-learning8
big-data7
Programming languages (3)
Github contributions (5)
apache/predictionio
Feb 2013 - Aug 2015
PredictionIO, a machine learning server for developers and ML engineers.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:49 commits, 10 pushes, 1 branch in 2 years 7 months
Contributions summary:Simon contributed to the admin UI of the PredictionIO project by implementing and modifying user interface elements and backend logic. They added functionality for features like an "unseen only" setting, a delete engine button, and the display of the engine status. The user also worked on data split settings for simulated evaluations, demonstrating involvement in the user interface and potentially the underlying data processing logic related to machine learning model evaluation. Further modifications added the ability to train a data model, enhancing the platform's utility.
pythondata-sciencemachine-learningmlopsbig-data
PredictionIO/PredictionIO-Docs
Mar 2013 - May 2014
Contributions:51 commits in 1 year 2 months
project-documentation
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