Michael Mesarcik

Applied Machine Learning Scientist

📍 Amsterdam
📧 mimesarcik@gmail.com
🔗 GitHub | LinkedIn | Google Scholar

About

I am an applied machine learning scientist with a PhD in anomaly detection for distributed sensing systems. I have experience in both the space and energy sectors, and a strong background in research and production environments.

Projects

• ROAD – Anomaly detection for radio telescopes (PyTorch)
• RFI-NLN – Semantic segmentation for RFI detection (TensorFlow)
• DL4DI – VAE-based data visualisation for radio telescopes (TensorFlow)
• REDTOOTH – Real-time acoustic data transmission (Java)
• DRFM – Radar signal processing system for a low cost FPGA (Verilog)

Education

• PhD, Informatics – University of Amsterdam (2019–2024)
• MSc, Electrical Engineering – University of Cape Town (2017–2018)
• BSc, Computer Engineering – University of Cape Town (2013–2016)

Experience

• Machine Learning Researcher – Alliander (2024–Now)
• Teaching Assistant – University of Amsterdam (2020–2022)
• Data Science Intern – DataProphet (2018–2019)

Selected Publications

• ROAD: The Radio Observatory Anomaly Detector – Astronomy & Astrophysics, 2023
• Learning to detect RFI without seeing it – MNRAS, 2022
• DL-assisted data inspection in radio astronomy – MNRAS, 2020

Theses

• Machine learning-based anomaly detection for radio telescopes, 2024