Aleksei Umnov
Machine Learning Engineer at Wayve
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolAleksei Umnov is a machine learning engineer and researcher with 11 years of industry experience and a seven-year track record of designing and deploying AI systems across computer vision, signal processing, and distributed environments. He combines deep academic credentials (PhD-level training in physics and mathematics from MSU) with hands-on production experience at companies from Yandex to Wayve, plus applied research on resource-efficient object detection. Aleksei has led educational product teams and lectured extensively, translating complex ML concepts for both technical teams and non-expert audiences and shaping curriculum on transformers, transfer learning and LangChain. He contributes to open educational ML resources—fixing subtle PCA orthogonality bugs and improving course materials—showing attention to mathematical correctness as well as pedagogy. Comfortable bridging research and engineering, he excels at turning prototype models into reliable production components for constrained hardware. Based in London, he brings a pragmatic research mindset and strong communication skills to cross-functional ML initiatives.
11 years of coding experience
12 years of employment as a software developer
Computer Science, Computer Science at Yandex School of Data AnalysisLomonosov Moscow State University
English, Russian, Hebrew
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
pandas10
jupyter-notebook10
machine-learning10
python10
data-visualizations7
data-visualisation7
data-visualization7
Programming languages (2)
Github contributions (5)
esokolov/ml-course-hse
Nov 2016 - Dec 2017
Машинное обучение на ФКН ВШЭ
Role in this project:
Data Scientist
Data ScientistContributions:12 commits, 14 pushes, 2 comments in 1 year
Contributions summary:Aleksei contributed to a machine learning course repository by uploading and fixing files related to a PCA (Principal Component Analysis) seminar. They fixed an orthogonality error within the PCA components, ensuring the proper functionality of the algorithm. Additionally, the user added and improved statements, and fixed a bug, indicating a focus on refining the educational content and ensuring data accuracy.
python
alexeyum/scikit-learn
Feb 2015 - Jun 2017
scikit-learn: machine learning in Python
Contributions:16 pushes in 2 years 4 months
pythondata-sciencelearn-machine-learningsklearnmachine-learning
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