Dat Tran
Partner & Co-CEO at Philipps & Byrne
Berlin, Germany
Summary
🤩
Rockstar🎓
Top SchoolDat Tran is a seasoned technology and business leader with 11+ years of experience building AI-first teams and products, currently serving as Partner & Co-CEO at DATANOMIQ in Berlin. He has led AI organizations at scale—most notably founding Axel Springer AI—and turned data teams into profit centers, generating millions in incremental revenue through productized data services. A hands-on engineer and open-source advocate, he’s contributed to popular projects like imagededup and image-super-resolution, improving ML tooling, testing, and deployment. He combines an entrepreneurial upbringing with formal training in operations research and finance, enabling him to bridge research, product and commercial outcomes. Known for shipping working software fast, he mentors across AI, product and leadership while advising multiple startups on strategy and go-to-market execution.
11 years of coding experience
10 years of employment as a software developer
The University of SydneyMaster of Science (MSc), Operations Research/Econometrics, Master of Science (MSc), Operations Research/Econometrics at Humboldt-Universität zu BerlinBachelor of Arts (BA), Accounting and Finance, Bachelor of Arts (BA), Accounting and Finance at The Berlin School of Economics and Law
German, English, Vietnamese
Stackoverflow
2,388reputation
376kreached
56answers
6questions
pyspark
top-5%
tensorflow
top-5%
python
top-5%
Github Skills (27)
opencv10
pytest10
python10
package-management10
setuptools10
testing10
machine-learning10
cicd10
super-resolution10
tensorflow10
object-detection10
computer-vision10
py9
multithreading9
pyspark9
Programming languages (10)
Github contributions (5)
datitran/object_detector_app
Jun 2017 - Dec 2018
Real-Time Object Recognition App with Tensorflow and OpenCV
Role in this project:
ML Engineer
ML EngineerContributions:19 commits, 3 PRs, 20 pushes in 1 year 5 months
Contributions summary:Dat focused on improving the performance and functionality of a real-time object recognition application. They initially implemented a working version using OpenCV, then experimented with TensorFlow optimization techniques. The user refactored the code to improve performance by removing dependencies and moving video capture to multithreading and outsourcing the object recognition process. Finally, the user added visualizations of the detected objects.
object-recognitionrecognitiontensorflowreal-time
idealo/image-super-resolution
Dec 2018 - May 2021
🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
Role in this project:MLOps Engineer
MLOps EngineerContributions:1 release, 2 reviews, 34 commits in 2 years 5 months
Contributions summary:Dat primarily focused on improving the project's testing and documentation while also contributing to code quality and deployment. They fixed issues with input files in the prediction module and refactored test setups. They updated the build and deployment processes by integrating extra requirements for testing. Furthermore, they worked on automating the documentation and fixing its content. They also made adjustments to the versioning and Docker configurations to make the project easier to build and deploy.
adversarialresidualtensorflowawscomputer-vision
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