Professor Xianghua Xie
Professor
Computer Science
Telephone: (01792) 602916
Email: JavaScript is required to view this email address.
Room: Office - 224
Second Floor
Computational Foundry
Bay Campus
Professor Xianghua Xie is currently leading a research team on Computer Vision and Machine Learning (http://csvision.swan.ac.uk) in the Department of Computer Science, Swansea University. He was a recipient of an RCUK Academic Fellowship (tenure-track research focused lectureship) between September 2007 and March 2012. He was appointed as a Senior Lecturer from October 2012, then an Associate Professor in April 2013, and a full Professor from March 2019. Prior to his position at Swansea, He was a Research Associate at the Computer Vision Group, Department of Computer Science, University of Bristol, where he completed both his PhD (2006) and MSc (2002) degrees.  
 
Professor Xie has strong research interests in the areas of Pattern Recognition and Machine Intelligence and their applications to real-world problems. He has been an investigator on several research projects funded by external bodies, such as EPSRC, Leverhulme, NISCHR, and WORD. Among his research works, those of significant importance include detecting abnormal patterns in complex visual and medical data, assisted diagnosis using automated image analysis, fully automated volumetric image segmentation, registration, and motion analysis, machine understanding of human action, efficient deep learning, and deep learning on irregular domains. By 2019, he has published over 140 fully refereed research publications and (co-)edited several conference proceedings. He is an associate editor of IET Computer Vision and an editorial member of a number of other international journals and has chaired and co-chaired several international conferences, e.g. BMVC2015 and BMVC2019.
 
More information can be found from his group website: http://csvision.swan.ac.uk

Areas of Expertise

  • Computer Vision
  • Image Processing
  • Pattern Recognition
  • Machine Learning
  • Big Data
  • Medical Image Understanding
  • Medical Imaging

Publications

  1. Xie, X. Graph Convolutional Neural Network for segmentation of immunostained Hodgkin Lymphoma histology images 23rd Conference in Medical Imaging, Understanding and Analysis
  2. Xie, X. Feature analysis of the choroid in optical coherence tomography images–limitations and opportunities Investigative Ophthalmology & Visual Science 60 9 3461 3461
  3. Xie, X. Coupled s‐excess HMM for vessel border tracking and segmentation International Journal for Numerical Methods in Biomedical Engineering e3206
  4. Xie, X. Consistent Segment-wise Matching with Multi-Layer Graphs (CGVC2018 Poster) International Conference on Geometric Modeling and Processing
  5. Xie, X., Zhou, S. Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities and Challenges IEEE Reviews in Biomedical Engineering 1 1

See more...

Teaching

  • CS-M87 MRes Visual Computing Project

    This module is the project phase of the MRes degree in Visual Computing.

  • CSC345 Big Data and Machine Learning

    This module provides a broad introduction to artificial intelligence, machine learning, pattern recognition, and their applications to big data problems. The students will gain understanding and knowledge of the theoretical foundations of learning, learn effective machine learning techniques, and acquire practical know-how in applying some of those theories and techniques to real world problems. Topics include big data concept, data mining, learning theories, supervised and unsupervised learning, and reinforcement learning.

  • CSC770 Visual Computing Project Development

    In this module students will be presented with an overview of the research area of Visual Computing. They are introduced into the topic, the background and the aims of their project. They write a detailed specification which will be the basis of their research project. Guidance as to appropriate research methodologies is provided.

  • CSCM10 Computer Science Project Research Methods

    This module will introduce students to some fundamental research methodologies and good practice in research. They will undertake background research including a literature review and specify the aims of their MSc project.

  • CSCM45 Big Data and Machine Learning

    This module will discuss in-depth some of the most widely used and state-of-the-art artificical intelligence and machine learning techniques and their applications to big data problems. The students will gain both theoretical understanding of learning and practical know-how in applying those theories to real world problems. Topics include big data concept, data mining, learning theories, supervised and unsupervised learning, and reinforcement learning.

  • CSCM77 Computer Vision and Deep Learning

    This module introduces students to the important and modern topics and concepts of computer vision and deep learning, including image processing, feature extraction, camera calibration, stereo vision, motion and tracking, recognition, deep neural network and its application to vision problems. It teaches techniques that are used to understand and interpret the contents of images and videos and dissects state-of-the-art vision systems, such as Microsoft Kinect. Practical examples in Matlab are provided throughout the lectures.

Supervision

  • Data Visualisation and Data Mining/Pattern Recognition for Biological Data. (current)

    Student name:
    PhD
    Other supervisor: Prof Mark Jones
  • Machine learning and application to life science data (current)

    Student name:
    PhD
    Other supervisor: Dr Benjamin Mora
  • Unravelling Polypharmacy: Determining interaction patterns between medications using complex electronic health records for better patient care. (current)

    Student name:
    PhD
    Other supervisor: Dr Shang-Ming Zhou
  • 'Applications of Machine Learning to EEG Audification and Sonification' (current)

    Student name:
    PhD
    Other supervisor: Dr Stephen Lindsay
  • Use of advanced analytics to predict defects in surface inspection systems (current)

    Student name:
    EngD
    Other supervisor: Prof Chenfeng Li
  • Electroencephalography Sequential Data Word Embedding in Decision Deadlocks (current)

    Student name:
    MRes
    Other supervisor: Dr Jingjing Deng
  • Detection and Characterisation of Type II Solar Radio Bursts (current)

    Student name:
    MRes
    Other supervisor: Dr Adeline Paiement
  • Artificial Intelligence and the steel industry - development of an automated quality inspection system (current)

    Student name:
    EngD
    Other supervisor: Dr Andrew Tappenden
  • Higher dimensional colour mapping and transfer functions for image and volume data (provisional) (current)

    Student name:
    PhD
    Other supervisor: Prof Mark Jones
  • Visualization and Machine Learning. (current)

    Student name:
    PhD
    Other supervisor: Prof Mark Jones
  • Visual Text Understanding (current)

    Student name:
    PhD
    Other supervisor: Dr Jingjing Deng
  • Consistent Correspondences for Shape and Image Problems (current)

    Student name:
    PhD
    Other supervisor: Dr Gary Tam
  • Computer vision based analysis of multi-modal images of the Martian surface (current)

    Student name:
    PhD
    Other supervisor: Dr Bob Laramee
  • Deep Learning on Irregular Domains (current)

    Student name:
    PhD
    Other supervisor: Dr Jingjing Deng
  • Irregular Domain Deep Learning (current)

    Student name:
    PhD
    Other supervisor: Dr Gary Tam
    Other supervisor: Dr Michael Edwards
  • 4D reconstruction of solar active regions from multi-spectral images using deep learning. (current)

    Student name:
    PhD
    Other supervisor: Dr Adeline Paiement
  • Large scale characterisation of galaxy morphology: a deep learning approach (current)

    Student name:
    PhD
    Other supervisor: Dr Gianmassimo Tasinato
  • Tentative title: Learning saliency in image collection (current)

    Student name:
    PhD
    Other supervisor: Dr Gary Tam
  • Feature Driven Learning Techniques for 3D Shape Segmentation (awarded 2019)

    Student name:
    PhD
    Other supervisor: Dr Gary Tam
  • 'Generative Modelling in Non-Euclidean Domains' (awarded 2019)

    Student name:
    MRes
    Other supervisor: Dr Adeline Paiement
  • 'Deep Learning Methods for Texture Analysis in Medical Imaging' (awarded 2018)

    Student name:
    MSc
    Other supervisor: Dr Adeline Paiement
  • Representation Learning in Irregular Domains. (awarded 2018)

    Student name:
    PhD
    Other supervisor: Prof Mark Jones
  • Exploring emerging data types for epilepsy research: Electronic Healthcare records, free texts and genetic mutation. (awarded 2018)

    Student name:
    PhD
    Other supervisor: Dr Seo-Kyung Chung
    Other supervisor: Prof Mark Rees
  • 'Adaptive Learning for Segmentation and Detection. ' (awarded 2017)

    Student name:
    PhD
    Other supervisor: Dr Gary Tam

Career History

Start Date End Date Position Held Location
March 2019 Present Professor Department of Computer Science, Swansea University
April 2013 February 2019 Associate Professor Department of Computer Science, Swansea University
October 2012 March 2013 Senior Lecturer Department of Computer Science, Swansea University
September 2007 September 2012 Lecturer (RCUK Academic Fellow) Department of Computer Science, Swansea University
August 2007 September 2007 Research Associate Department of Computer Science, Bristol University
February 2006 July 2007 Research Assistant Department of Computer Science, Bristol University
February 2011 November 2005 Research Assistant and PhD Student Department of Computer Science, Bristol University

Key Grants and Projects

  • Biomedical Research Unit: Advanced Medical Image Analysis and Visualisation, Collaborator 2011 - 2015

    led by Prof. John (Bangor University), Funded by NISCHR, £325K to Swansea (£1.5M in total)

  • Non-Invasive Quantification of Complex Heart Valve Lesions: A model-based approach using 3D echocardiography, Co-investigator 2011 - 2014

    led by Dr. van Lool (Swansea University), Funded by NISCHR, £58K

  • Patient Specific Multi-view Coronary Geometry and Computational Disease Modelling, Principal investigator 2010 - 2012

    , Funded by WORD, £144K

  • NISCHR Registered Research Group: Medical Image Analysis and Visualisation, Co-investigator 2010 - 2013

    led by Prof. Zwiggelaar (Aber University), Funded by NISCHR, £95K

  • PRINS: Prior-independent Shape Regularisation for Geometric Active Contours, Co-investigator 2006 - 2008

    led by Prof. Mirmehdi (Bristol University), Funded by The Leverhulme Trust, £65K

  • PARSER: Parking and AiR pollution SEnsoRs for Smart Cities 2019 - 2015

    Co-PI, funded byI nnovateUK, £319,514.40

  • Fraud Detection in Dual use of Goods using Explainable Machine Learning and Visual Analytics 2018 - 2019

    PI, funded by InnovateUK, £172,139.08

  • Predictive Big Data Analytics for Human Health and Animal Wellbeing 2018 - 2020

    PI, SÊR CYMRU Cofund, £63,082.35

  • Data Release - Trust, Identity, Privacy and Security 2016 - 2019

    Co-investigator, led by Prof. Mark Jones (Swansea), funded by EPSRC (EP/N028139/1 and EP/N027825/1) £1.6M

  • Wales Research Institute on Visual Computing (RIVIC), I am leading a sub-programme on 3D Vision and Medical Image Analysis 2009 - 2013

    , Funded by WAG, £5M

Research Groups