Artem Artemev
AI Research Scientist at MediaTek Research 聯發創新基地
Cambridge, England, United Kingdom
Summary
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Rockstar🎓
Top SchoolArtem Artemev is an AI research scientist based in Cambridge with nine years of experience blending probabilistic modelling, Bayesian methods and production-grade software engineering. Currently a PhD candidate at Imperial College and an AI Research Scientist at MediaTek Research, he has held research visits at Oxford and Cambridge and led applied ML teams at Secondmind where he specialised in Gaussian processes and uncertainty estimation. A pragmatic developer as well as a researcher, Artem contributes to GPflow—refactoring core TensorFlow-based GP code and improving optimization and model persistence—reflecting his knack for making advanced inference methods robust and maintainable. His background ranges from embedded and game-industry engineering to self-driving car systems training, giving him an unusually broad perspective on deploying ML in real-world products.
9 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy - PhD Machine Learning, Doctor of Philosophy - PhD Machine Learning at Imperial College LondonNanodegree Self Driving Car Engineer, Nanodegree Self Driving Car Engineer at UdacityMaster's degree Telecommunications Engineering, Master's degree Telecommunications Engineering at ULSTU (Ulyanovsk State Technical University)
Русский, Английский
Stackoverflow
516reputation
14kreached
17answers
3questions
Github Skills (9)
machine-learning10
tensorflow10
python10
gaussian-processes10
computer-engineering9
gaussian6
blas6
tensorflow-probability6
gpflow6
Programming languages (10)
Github contributions (5)
GPflow/GPflow
Jul 2017 - Sep 2022
Gaussian processes in TensorFlow
Role in this project:
Back-end Developer
Back-end DeveloperContributions:27 releases, 42 reviews, 697 commits in 5 years 2 months
Contributions summary:Artem's commits primarily focus on refactoring code within the gpflow library to align with a new one-parameter-one-variable scheme. They made adjustments to core GPflow files, modifying existing code in order to accommodate changes related to TensorFlow features and variables, notably the addition of a parameter’s transformed representation to the computations. The commits also encompass improvements to the internal optimization and model saving functionalities of the library.
information-theorygpflowdeep-learningmachine-learningmarkov-chain-monte-carlo
awav/conjugate-gradient-sparse-gp
Apr 2022 - Oct 2022
Contributions:191 commits, 10 pushes in 6 months
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