Dmitry Ulyanov
Quantitative Researcher
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolDmitry Ulyanov is a quantitative researcher at XTX Markets with 12 years of experience blending cutting-edge computer vision research, machine learning engineering, and startup leadership. He co-founded and led Avaturn/in3D—bringing 3D body-scanning technology from research to product—and holds a PhD supervised by Victor Lempitsky and Andrea Vedaldi with a visiting stint at Oxford. His background spans research roles at Yandex and Samsung AI, an internship at Google, and prolific open-source contributions such as a parallel t-SNE implementation and the influential deep-image-prior project. Dmitry is also a competitive data science prize-winner and designed a Coursera course on winning data science competitions, reflecting both practical modeling skill and teaching ability. Based in London, he combines entrepreneurial grit with rigorous academic foundations to ship research-driven, production-ready ML systems.
12 years of coding experience
11 years of employment as a software developer
Visiting PhD Student, Visiting PhD Student at University of OxfordPhD Computer Vision, PhD Computer Vision at Skolkovo Institute of Science and TechnologyLomonosov Moscow State University
Russian, English
Github Skills (20)
pytorch10
python10
image-processing10
scikit10
pandas10
machine-learning10
exploratory-data-analysis10
eda10
numpy10
image-restoration10
scikit-learn10
neural-network10
computer-vision10
jupyter-notebook10
c-language9
Programming languages (13)
Github contributions (5)
DmitryUlyanov/Multicore-TSNE
Oct 2016 - Aug 2020
Parallel t-SNE implementation with Python and Torch wrappers.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:69 commits, 35 PRs, 68 pushes in 3 years 10 months
Contributions summary:Dmitry primarily contributed to the core t-SNE implementation, making improvements to the C++ code and the Python wrapper. They addressed code formatting issues (tabs to spaces), updated the build process for the Torch wrapper, and introduced type checking in the Python code. Their work involved improving the library's usability and integration with PyTorch.
pythonparalleltorchmulticorewrappers
DmitryUlyanov/deep-image-prior
Nov 2017 - May 2020
Image restoration with neural networks but without learning.
Role in this project:ML Engineer
ML EngineerContributions:50 commits, 7 PRs, 38 pushes in 2 years 5 months
Contributions summary:Dmitry primarily contributed to the project by modifying and adding code related to image processing and restoration techniques. They fixed errors in existing feature inversion files and added comments to utility functions. The user also worked on a super-resolution notebook and added a new restoration notebook, indicating involvement in developing and refining image restoration methodologies. These contributions suggest a focus on the application of neural networks for image-based tasks.
pythonrestorationdeep-learningimage-restorationneural-networks
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