Human-Centred Computing News
Exoplanet Validation with Machine Learning: 50 new validated Kepler planets
Dr Theo Damoulas (Department of Computer Science) along with Dr David Armstrong (Department of Physics) and Jevgenij Gamper (Department of Mathematics) have developed probabilistic machine learning algorithms that can separate out real planets from fake ones in the large samples of thousands of candidates found by telescope missions such as NASA’s Kepler and TESS. The results of which have led to fifty new confirmed planets, the first to be not only ranked but also probabilistically validated by machine learning.
The paper "Exoplanet Validation with Machine Learning: 50 new validated Kepler planets" has been accepted to the Monthly Notice of the Royal Astronomical Society, DOI: 10.1093/mnras/staa2498
Wearable IoT Electronic Nose for Urinary Incontinence Detection
Work performed by Computer Systems Engineering student Michael Shanta for his 3rd year project, supervised by Dr. Marina Cole and Dr. Siavash Esfahani in the School of Engineering, was written up in a paper that was recently accepted for presentation at the IEEE Sensors 2020 Conference.
For his 3rd year project Michael worked on developing machine learning techniques for an Electronic Nose in order to classify odours based on the sensor responses. The system aims to detect incontinence incidents, allowing alerts to be sent to relevant personnel from an IoT network via a cloud server.
Dr Sathyawageeswar Subramanian joins the department as a Research Fellow
Dr Sathyawageeswar Subramanian has joined the department to work as a Research Fellow on the "Foundations of classical and quantum verifiable computing" project, which is led by Dr Tom Gur.
Sathya completed his PhD in quantum computing at the University of Cambridge under the supervision of Prof. Richard Jozsa. His primary interests are quantum algorithms and computational complexity theory.