DataTalks.Club From Astronomy to Applied ML - Daniel Egbo
Sep 26, 2025
Daniel Egbo, an astrophysicist turned machine learning engineer, shares his ambitious journey from the stars to data science. He dives into the Meerkat radio telescope's incredible ability to map the galaxy and the fusion of physics and machine learning for star identification. Daniel provides practical advice for beginners in data science, highlighting mentorship and resources. He reflects on his experiences with AI internships and setting up data pipelines with cutting-edge tools. Tune in for insights on skill-building and bridging astrophysics and technology!
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From Nigeria To Meerkat Radio Surveys
- Daniel described moving from Nigeria to Cape Town for a PhD and working with the Meerkat radio telescope array.
- He explained his task: find radio-emitting stars from Meerkat's galactic plane legacy dataset.
Why Multiwavelength Observations Matter
- The electromagnetic spectrum spans radio to gamma rays and different telescopes target different bands.
- Our eyes see optical but radio, infrared and X-ray reveal distinct astrophysical phenomena.
Positional Matches Aren't Enough
- Cross-matching radio detections with other wavelengths relies on precise positional alignment but remains ambiguous in 2D.
- You must combine positional matches with physical source properties to avoid false associations.