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Dr Qiuji Yi

Assistant Professor

Department: Computer and Information Sciences

As for the research, I have published 19 academic papers in multi-dimensional data processing/feature extraction and fusion, data-driven twin modelling and inversion analysis. Nine are top journal articles, including NDT&E international (IF,4.0), IEEE transactions and industrial informatics (IF 11.6). IEEE Internet of Things Journal (IF,10.2), Composite Part B: Engineering (IF,11.0), Philosophical Transactions of the Royal Society A and IEEE Sensors. One of my papers about proposing an automatic delamination detection framework using Kernal Principal Component is the most cited work in the top journal(https://doi.org/10.1016/j.ndteint.2018.12.010). 
Besides my track record in the field, I am also developing and delivering an agreed personal research plan and participating in institutional and collaborative research, with industry stakeholders(Rolls-Royce, GKN aerospace and the National Composite Centre), and other University partners(TU Delft, UESTC, Newcastle University)
I am also actively looking for research funding opportunities and have secured quite a few fellowship funding such as EU ITN project NDTonAIR(€3.8m), ERSRC: Techno-Economic framework for Resilient and Sustainable Electrification (EP/R030294/1) £1.0m, EPSRC grant: Certest - Certification for Design - Reshaping the Testing Pyramid, EPSRC(EP/S017038/1) £6.9m. 

I am particularly excited about advancing fundamental AI capabilities while addressing pressing engineering needs in Manufacturing and Maintenance, such as AI for nondestructive testing and structural health monitoring. I look forward to exploring these multidisciplinary problems with collaborators from academia and industry.

 

Qiuji Yi

As a researcher in Artificial Intelligence and Machine Learning, my long-term goal is to apply effective, robust, and interpretable machine learning methods to characterise, image and inversion of complex structures such as carbon fibre composites. I hope to develop physics-informed machine learning tools. My research focuses on developing AI and ML tools for various instruments, including eddy current testing, thermography and ultrasound. My developed AI tools include clustering, matrix factorisation, supervised learning, support vector machines, and deep learning networks for advancing defect detection techniques.

Electrical and Electronic Engineering PhD April 06 2021


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