Philipp Kranen
Principal Software Engineer at Microsoft
Bergheim, North Rhine-Westphalia, Germany
Summary
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Rockstar🎓
Top SchoolPhilipp Kranen is a Principal Software Engineer with over a decade of experience translating research-grade data mining, streaming analytics, and deep learning into production services at Microsoft. He has driven features across Azure Cognitive Services—spanning Speech-to-Text, Text-to-Speech and Azure OpenAI integrations—while previously contributing to the Microsoft Cognitive Toolkit (CNTK) with focused work ensuring GPU matrix correctness for CUDA BLAS. With a PhD-level background from RWTH Aachen and a foundation in applied research on stream processing, he blends rigorous academic methods with pragmatic engineering at cloud scale. Philipp’s career spans IoT telemetry, custom vision, and real-time analytics, and he brings a knack for tightening numerical reliability in ML pipelines that often goes unseen but is critical to model correctness. Based in Bergheim, Germany, he combines deep systems thinking with hands-on contributions to open-source deep learning infrastructure.
10 years of coding experience
15 years of employment as a software developer
Informatik, Informatik at UIB, SpanienDr. rer. nat. Informatik, Dr. rer. nat. Informatik at RWTH Aachen UniversityInformatik Wirtschaftswissenschaften, Informatik Wirtschaftswissenschaften at TU Delft
English, Spanish, German
Stackoverflow
36reputation
479reached
3answers
0questions
Github Skills (14)
unit-testing10
cuda10
c-language10
deep-learning10
deep-neural-networks10
cprogramming-language10
machine-learning9
object-detection6
neural-network6
cntk6
text-to-speech6
azure6
speech6
azure-cognitive-services6
Programming languages (5)
Github contributions (5)
microsoft/CNTK
Nov 2015 - Sep 2017
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
Role in this project:
ML Engineer
ML EngineerContributions:276 commits, 10 PRs, 483 pushes in 1 year 10 months
Contributions summary:Philipp's commits primarily revolve around modifying and converting existing unit tests for the GPUMatrix class within the Microsoft Cognitive Toolkit (CNTK) repository. The changes involve refactoring tests, introducing new tests for CUDA BLAS functionalities, and adding missing assertions to the tests to improve their robustness. The user is focused on ensuring the correctness of GPU matrix operations using CUDA BLAS libraries, a key component in deep learning tasks.
pytorchpythondeep-learningc-plus-plusmachine-learning
liqunfu/CNTK
May 2017 - Jun 2017
Contributions:19 commits in 1 month
pytorchdeep-learningcomputer-visionmachine-learningcognitive
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