Kenneth Tran
CTO at Koidra
Greater Seattle Area United States
Summary
🤩
Rockstar🎓
Top SchoolKenneth Tran is a CTO based in the Greater Seattle Area with 10 years of experience building scalable machine learning systems that blend physics, scientific computing, and applied ML. He leads Koidra’s work on physics-aware AI for real-world production problems in food and energy, translating mathematical models into distributed, production-ready systems. Previously a Principal Applied Scientist at Microsoft Research, he drove model-based reinforcement learning for real-world control tasks, including an autonomous greenhouse project that outperformed expert growers and data center energy optimization work. He holds a Ph.D. in Computational and Applied Mathematics from UT Austin and specializes in computer vision, optimization, and distributed systems. An active engineer as well as leader, he’s contributed to open-source tooling like vertica-python by improving streaming data retrieval for a major analytics database and also co-founded a biomass company in Vietnam focused on displacing coal.
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 (9)
databases10
drivers10
data-base10
driver10
relational-databases10
python10
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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