Hey there! 👋

I'm Plamen, a passionate software engineer and open-source contributor since 2018.

🚀 About Me

My journey in open source began at DAI-Lab, where I tackled a variety of complex challenges that deepened my skills in software design, machine learning, and distributed systems. Since then, I've been actively contributing to open-source projects that aim to push the boundaries of data science, automation, machine learning and synthetic data generation.

👨‍💻 What I’m Working On

I'm currently part of the awesome team at DataCebo, the creators of SDV — the largest ecosystem for synthetic data generation and evaluation. My work spans feature development, code refactoring, and maintaining several open-source tools, including:

• SDV: The Synthetic Data Vault, a powerful synthetic data generation tool that maintains the same format and statistical properties as the real data.

• RDT: Reversible Data Transforms, a Python library for transforming raw data into fully numerical data.

• CTGAN: A collection of deep learning-based synthetic data generators for single table data.

• Copulas: A Python library for modeling multivariate distributions and sampling from them using copula functions.

• DeepEcho: A synthetic data generation Python library for mixed-type, multivariate time series.

• SDMetrics: A library that evaluates synthetic data by comparing it to the real data you're trying to mimic.

• SDGym: Synthetic Data Gym, a framework for benchmarking the performance of synthetic data generators based on SDV and SDMetrics.

🏆 Notable Contributions

Here are some of the other open-source projects I’ve contributed to over the years:

• SteganoGAN: A tool for creating steganographic images using adversarial training.

• MLPrimitives: Pipelines and primitives for machine learning and data science.

• MLBlocks: A simple framework for composing end-to-end tunable machine learning pipelines.

• BTB: Bayesian Tuning and Bandits, a tool for hyperparameter tuning and model selection.

• AutoBazaar: An AutoML system combining BTB, MLPrimitives, and MLBlocks.

• mit-d3m-ta2: MIT-Featuretools TA2 submission for the D3M program.

• ATM: Auto Tune Models, an AutoML system designed with ease of use in mind.

• Orion: A machine learning library for unsupervised time series anomaly detection.

• SigPro: An end-to-end solution for efficiently applying multiple signal processing techniques to raw time series data.

• Draco: A collection of end-to-end solutions for machine learning problems commonly found in monitoring wind energy production systems.

🤝 Let’s Connect

If you’re working on something interesting — especially around synthetic data, AutoML, or time series — I’d love to hear from you!

• 💼 LinkedIn
• 🐦 X (Twitter)