Turing Fellow, Professor of Artificial Intelligence - The University of Manchester
Professor Hujun Yin is a member of Manchester’s Conference Ambassador Programme, having established a Local Organising Committee which successfully secured the rights to host the prestigious IDEAL conference in Manchester.
International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)
IDEAL is an annual international conference dedicated to emerging and challenging topics in intelligent data analytics and associated machine learning systems and paradigms. The conference provides a unique opportunity and stimulating forum for presenting and discussing the latest theoretical advances and real-world applications in Computational Intelligence and Intelligent Data Analysis. Authors and researchers are warmly invited submit their latest findings and research work to the conference.
Prof Yin acted as Chair of the following conference editions:
2019 marked its 20th edition and after 17 years it returned to Manchester, the birthplace of Artificial Intelligence, with the support and co-sponsorship of the Alan Turing Institute, Springer and IEEE CIS UK & Ireland. In 2022 Manchester will host a hybrid edition of the conference with delegates able to attend in-person or Tke part virtually.
Professor Yin is a Turing Fellow of the Alan Turing Institute, a senior member of IEEE, and a member of the EPSRC Peer Review College. He has been the Vice Chair of the IEEE CIS UK Ireland Chapter since 2019. He leads a team of 12 researchers working in vision systems and machine learning with strong emphasis on real-world and industrial applications.
He has delivered plenary talks to numerous national and international meetings. He has been an Associate Editor for the IEEE Transactions on Cybernetics since 2015 and served as Associate Editor for IEEE Transactions on Neural Networks between 2006-2009. He had been a member of Editorial Board of International Journal of Neural Systems from 2005 till 2020.
His research interests range from theories and applications of neural networks, self-organising and deep learning systems in particular; image processing, enhancement and recognition; face recognition; nonstationary signal or time series modelling and prediction; dimensionality reduction and manifold learning. Though his core expertise is in unsupervised and manifold learning, he has developed a variety of methods in a wide range of fields such as gene expressions analysis, protein peptide spectral sequencing, neural signal decoding, signal/image based industrial monitoring, financial time series modelling, robust image feature extraction, as well as hyperspectral image analysis for plant monitoring. Recently he is particularly interested in solving practical, industrial problems using deep learning frameworks, where unsupervised or data-independent means can be derived for efficient learning. Targeted applications include robust recognition of deformed image objects, imaging inverse problems, and enhanced modelling interpretation for noisy and intermittent signals.
