Lyudmil Vladimirov
Senior Postdoctoral Research Associate In Data Fusion
Liverpool, England, United Kingdom
Summary
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Senior🎓
Top SchoolLyudmil Vladimirov is a Senior Postdoctoral Research Associate in Data Fusion with 11 years of experience combining advanced research and hands-on software development. He specializes in Bayesian inference, target tracking and algorithmic design, and is a core contributor and back-end developer to the widely used Stone-Soup tracking framework, implementing key state and transition models and Kalman filter functionality. Based at the University of Liverpool, he has led applied projects with government and industry partners (including Dstl and the US Air Force), taught at international conferences, and maintained multiple Python research packages. Comfortable across Python, C++, MATLAB and TensorFlow, he bridges theoretical methods and production-ready code—often tackling time-varying systems (e.g., Ornstein–Uhlenbeck models) that reveal practical nuances beyond standard filters.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Electrical and Electronics Engineering, Doctor of Philosophy (Ph.D.), Electrical and Electronics Engineering at University of Liverpool
Greek, Bulgarian, English
Stackoverflow
86reputation
8kreached
3answers
0questions
Github Skills (9)
object-oriented-programming10
python10
kalman-filter10
scipy9
linear-algebra8
pandas6
restructuredtext6
rest6
numpy6
Programming languages (9)
Github contributions (5)
dstl/Stone-Soup
Apr 2018 - Sep 2022
A software project to provide the target tracking community with a framework for the development and testing of tracking algorithms.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:22 reviews, 78 commits, 34 PRs in 4 years 5 months
Contributions summary:Lyudmil implemented core functionality for the `stone-soup` project, including a `GaussianState` object and transition models such as `ConstantVelocity1D`, `ConstantVelocity2D`, and `ConstantVelocity3D`. They also worked on Kalman and Extended Kalman filter predictor and updater classes, adding features and refining their measurement prediction and update processes. Furthermore, the user contributed by adding the `OrnsteinUhlenbeck` transition model for time-variant systems.
target-trackingtestingtesting-and-developmenttracking
sglvladi/meshquitto
Mar 2017 - Aug 2020
Contributions:26 commits, 24 pushes, 1 branch in 3 years 5 months
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