Kenneth Tran
CTO at Ayo Biomass
Greater Seattle Area United States
Summary
🤩
Rockstar🎓
Top SchoolKenneth Tran is a CTO and machine learning leader with 11 years of experience building scalable ML and computer vision systems that bridge physics, scientific computing, and production software. He holds a Ph.D. in Computational and Applied Math and moved research innovations—like model-based RL for real-world control—into deployed systems at Microsoft Research and later at Koidra for applications such as autonomous greenhouse and data center controls. As a founder and board director in renewable biomass, he pairs technical depth with commercial and operational experience across hardware-in-the-loop domains. Kenneth is hands-on in systems engineering and databases too, contributing streaming iterator improvements to the widely used vertica-python client to optimize large-scale data retrieval. Colleagues describe him as someone who translates advanced math and optimization theory into robust, distributed ML pipelines that work in messy real-world environments.
11 years of coding experience
8 years of employment as a software developer
Ph.D Computational and Applied Math, Ph.D Computational and Applied Math at The University of Texas at AustinExchange Computational Mathematics, Exchange Computational Mathematics at KTH Royal Institute of TechnologyBachelor of Science - BS Math and Computer Science, Bachelor of Science - BS Math and Computer Science at Dean's Scholars Honors Program
Github Skills (7)
databases10
driver10
python10
relational-databases10
sql-database10
vertica10
database10
Programming languages (10)
Github contributions (5)
vertica/vertica-python
Apr 2015 - May 2015
Official native Python client for the Vertica Analytics Database.
Role in this project:
Back-end Developer / Database Engineer
Back-end Developer / Database EngineerContributions:2 releases, 16 commits, 5 PRs in 27 days
Contributions summary:Kenneth primarily contributed to the `vertica-python` library by implementing and refactoring code related to streaming iterators. Their work involved modifications to the `Cursor` class, allowing for efficient data retrieval. This includes changes to the `execute`, `fetchone`, and `fetchall` methods to support streaming. These updates focused on enhancing how data is processed and retrieved from the Vertica database, leading to improvements in the library's performance.
analyticsdriverpythonpython-clientanalytics-database
MSRDL/Deep4Cast
Feb 2015 - Jun 2019
Probabilistic Multivariate Time Series Forecast using Deep Learning
Contributions:31 commits, 24 PRs, 47 pushes in 4 years 5 months
forecastingpytorchdeep-learningtemporal-datamultivariate-time-series
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