Dr. Quanbin Sun
Quanbin Sun (QS)’s research expertise is in machine learning, modelling and simulation, data management, and data analytics. Currently, he is working with UK Network Rail and its partners on visualising railway bridges for engineer inspection and automatically detecting bridge defects (the preliminary research and development of the concept proof prototype were carried out during 2018 – 2020). In 2016 - 2019, he conducted a project as his Postgraduate Certificate in Higher Education study which leveraged machine learning and topic modelling for the automatic analysis of free-text responses from national student survey (with over 10,000 statements) to inform effective teaching strategies. In 2013 - 2016, he worked at the University of Manchester where he involved in several multi-million projects with various roles in the software development. For example, he was the technical lead at the Alliance Manchester Business School for the IT Systems' migration project (part of the University of Manchester' One IT project, over £100M); he worked on the Linked Database System project (£40M+ investment from GSK-GlaxoSmithKline, the system was used for the Salford Lung Study which involved over 4,200 consenting patients, 80 GP practices and 130 pharmacies); the Asthma "e-Lab" project for STELAR (£12M, MRC(The Medical Research Council)). In 2009 - 2012, he was an EPSRC (The Engineering and Physical Sciences Research Council) funded researcher at Salford Centre for Research and Innovation (SCRI – an EPSRC research centre) where he worked in lifecycle management of healthcare. He also studied in crowd behaviour modelling as his PhD and developed a novel generic model to simulate individual behaviours and scenarios during that same period.
He is currently an active researcher in the Intelligent Software Systems Lab (ISSL). Within this lab, she investigates applying established approaches (e.g., neural network, objective decision-making, secure design) in novel ways, thereby producing intelligent solutions for software systems in a myriad of domains.