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Dr Yulia Hicks

Dr Yulia Hicks

Senior Lecturer - Teaching and Research

School of Engineering

+44 (0)29 2087 5945
S/1.07, E/3.24
Available for postgraduate supervision



I am a Senior Lecturer in Computer Vision and Machine Learning. I graduated from Moscow State University in Applied Mathematics and from Cardiff Uniuversity in Computer Science and then completed my PhD in Computer Vision and Machine Learning at Cardiff University. I have extensive expertise in motion capture and tracking of human motion, statistical motion and behaviour modelling, pattern recognition, and multi-modal signal processing with particular application to medical problems. I contributed to over 60 peer reviewed publications and have secured, with colleagues, external grant support totalling more than 1.3 million pounds. I am a member of Medical Engineering Research Group, Co-Chair of the IROHMS Working Group on Ethical and Explainable AI, and a Co-Director of the Human Factors Technology Lab – an interdisciplinary research lab between the Schools of Engineering, Computer Science and Psychology.

My current research interests are in 

  • Explainable AI
  • Human action and activity recognition in video
  • Modelling and characterisation of human motion for neuro-degenerative diseases and back pain
  • Biomedical image and signal processing
  • Applications of AI in Engineering 



  • 2003: PhD (Computer Vision), Cardiff University, Cardiff, UK
  • 1997: BSc (Computer Science), Cardiff University, Cardiff, UK
  • 1996: MSc (Applied Mathematics), Moscow State University, Moscow, Russia

Career overview

  • 2011 - present Senior Lecturer in Computer Vision and Machine Learning, School of Engineering, Cardiff University
  • 2004 - 2011      Lecturer in Computer Vision and Machine Learning, School of Engineering, Cardiff University
  • 2001 - 2003      Research Associate, School of Computer Science and Informatics, Cardiff University

Professional memberships

IEEE Senior Member, BMVA member.

Technical Committees

  • From 2017 ExCo member of IET Vision and Imaging professional network.
  • From 2017 ExCo co-opt member of British Machine Vision Association (BMVA).

Academic positions

PhD External examiner for Nottingham Trent University, UK (2019); Loughborough University, UK (2014, 2017, 2018); Strathclyde University, UK (2016); Kingston University, UK (2011, 2013); Newcastle University, UK (2016); Ulster University, UK (2015); Deakin University, Australia (2015, 2018); Strasbourg University, France (2019).

MSc degree external examiner for the School of Computer Science, Kingston University, UK (2014-2017).

Academic Prizes

  • Best Paper Award at IEEE LifeTech 2020, Kyoto, Japan.
  • Best paper award at Irish Machine Vision and Image Processing Conference, September 2004, Dublin, Ireland.
  • Best Demonstration Paper prize at British Machine Vision Conference 2000.

Invited talks

  • Aberystwyth University, February 2020: Towards more sensitive, objective and consistent clinical assessments for neurodegenerative disorders.
  • Newcastle University, July 2017: Towards more sensitive, objective and consistent clinical assessments for neurodegenerative disorders.
  • Bath University, July 2010: Statistical Models in Audio, Image and Video Processing.
  • Kingston University, June 2010: Incremental Gaussian Mixture Models and Hidden Markov Models
  • Surrey University, February 2010: Incremental Gaussian Mixture Models
  • Swansea University, December 2009: Statistical Models in Audio, Image and Video processing
  • Grenoble Institute of Techology, March 2008: Multimodal Blind Source Separation.

Organising conferences

  • General Chair for the 30th British Machine Vision Conference, Cardiff, September 2019. 
  • Invited Session Chair for 19th, 20th, 21st, 22nd and 24th International Conference on Knowledge Based and Intelligent information and Engineering Systems (KES2015, KES2016, KES2017, KES2018, KES2020).
  • Organising Chair for the 14th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2010) hosted by Cardiff School of Engineering in September 2010.
  • Publicity Chair for the IEEE Workshop on Statistical Signal Processing hosted by Cardiff School of Engineering in August 2009.
  • Publicity Chair for the International Digital Signal Processing Conference (DSP2007), hosted by Cardiff School of Engineering in July 2007.























I teach the following modules in the School of Engineering:

  • 1st year  Engineering Computing
  • 2nd year Programming for Engineering
  • 3rd year Object-Oriented Engineering Computing
  • 4th year  Artificial Intelligence

In addition to teaching on the above modules, I supervise undergraduate projects in Computer Vision, Machine Learning, Deep Learning and AI.

Sensitive, objective and consistent clinical assessments for neurodegenerative diseases

Neurodegenerative disease is an umbrella term for a range of conditions, which primarily affect the neurons in the human brain. Examples of neurodegenerative diseases include Parkinson’s, Alzheimer’s, and Huntington’s disease. Neurodegenerative diseases are currently incurable and debilitating conditions that result in progressive degeneration and/or death of nerve cells. This causes problems with movement (called ataxias), or mental functioning (called dementias).

In clinical practice, these diseases and their symptoms are assessed using a number of special tests, such as Mini Mental State Examination (MMSE) and Clock Drawing Test (CDT) for dementias or Unified Huntington’s (Parkinson’s) Disease Rating Scale (UH(P)DRS) for Huntington’s (Parkinson’s) disease.

Traditionally, these tests are administered by expert clinicians and are assessed on the basis of observations, thus the assessments are limited by inter- and intra-rater variability, subjective bias and categorical design. At the same time, there is ongoing research in more effective treatments for such diseases, thus necessitating the development of more sensitive, consistent and objective means of assessment for such diseases.

Over the last ten years, we have developed, together with the School of Medicine, a number of machine learning algorithms addressing this need.

Please contact Dr Yulia Hicks for more information.

Artificial Intelligence (AI) for health and sports monitoring

We are developing advanced AI algorithms for analysis of the data obtained with a variety of devices, such as mobile phones, video cameras, inertial measurement units (IMU) and electromyography (EMG) units for sport and health monitoring. The data is analysed and appropriate feedback is provided to the subject and medical professionals in order to monitor the condition of the patient and to develop personalised treatment appropriate for the condition. The applications so far have included Huntington’s disease and lower back pain.

This research is a result of collaboration with the School of Medicine and with the School of Healthcare Sciences.

Please contact Dr Yulia Hicks for more information.

Explaining deep learning models for human action and activity recognition

The popularisation of video surveillance and the vast increase of video content on the web has rendered video one of the fastest growing resources of data. In such videos, humans are arguably the most interesting subjects.

Deep learning methods have demonstrated success in many areas of computer vision, including human action and activity recognition. However, to be confident in their predictions, their decisions need to be transparent and explainable. The aim of this project is to develop algorithms capable of explaining the decisions made by the deep learning methods, specifically when applied to human activity recognition.

This research is a result of collaboration with the School of Computer Science and Informatics.

Please contact Dr Yulia Hicks for more information.


Title People Sponsor Value Duration
Bridging the gaps Featherston C, Hicks Y, Davies C, Preece A, Patrick J, Elias S, Found P EPSRC 47152 01/04/2012 - 31/03/2013
Improving EEG reading of brain states for clinical applications using a data-driven joint model of FMRI and EEG. Wise R (PSYCH), Hicks YA, Charron CS EPSRC 26595 01/05/2011 - 30/04/2012
Signal processing solutions for the networked battlespace McWhirter J, Hicks Y EPSRC via Loughborough 515519 01/04/2013 - 31/03/2018
Automated assessment of timing and movement signatures in Huntingdon's disease Busse (HCARE) Holt C, Hicks Y Wellcome Trust 16301 01/01/2016 - 31/12/2016
Multi-modal blind source separation algorithms Chambers JA, Hicks YA, Sanei S, (with COMSC) Engineering and Physical Sciences Research Council (transferred to Loughborough) 223190 01/05/2005 - 01/05/2008
Modelling, analysing and exploiting dependencies between the parts of a moving human figure in the context of computer graphics applications Hicks YA The Nuffield Foundation 1200 01/08/2005 - 30/09/2005
Multi-Camera Real Time 3D Reconstruction of Urban Environments Hicks Y General Dynamics 19060 01/10/2007 - 30/09/2010
Adaptive models of human motion Dr YA Hicks Engineering and Physical Sciences Research Council 206291 01/01/2007 - 30/06/2009
Video-assisted audio source separation Dr YA Hicks QinetiQ Ltd 13200 01/10/2004 - 30/09/2007


Dr Hicks is a co-director of the Human Factors Technology Lab, an interdisciplinary Research Centre which brings together the Schools of Engineering, Psychology and Computer Science. It houses a variety of motion capture equipment, a 3D video camera and a dedicated research office space with desks and computers. Dr Hicks successfully supervised 18 PhD students (ten as the main supervisor and eight as a co-supervisor) and two MPhil (one main supervisor and one co-supervisor). She is currently supervising five PhD students.

She is currently actively pursuing research and looking for new collaborations in these areas:

  • Explainable AI
  • Human action and activity recognition in video
  • Modelling and characterisation of human motion for neuro-degenerative diseases and back pain
  • Biomedical image and signal processing
  • Applications of AI in Engineering 

Past projects

PhD Students: Current

  • (1st Engin supervisor) Liam Hiley, Explainable human motion recognition.
  • (2nd Engin supervisor) Sam Woodgate, Machine learning techniques for Huntington's motion chararacterisation.

PhD Students: Completed

  • 2020 (1st Engin supervisor) Jack Latham, Signal processing techniquers for Doppler ultrasound cardiac assessme
  • 2019 (1st Engin supervisor) Samia Dawood Shakir Albasri, MODELLING TALKING HUMAN FACES IN 3D. 
  • 2018 (1st Engin supervisor) Hawraa Hassan Abbas, Modelling Conversational 3-Dimensional Faces 
  • 2018 (1st Engin supervisor) Karima Elmasri, A Robust Technique for Detecting Abdominal Aortic Calcification using Dual-Energy X-Ray Absorptiometry.
  • 2018 (2nd Engin supervisor) Patrick Adebayo, DYNAMIC SPECTRUM MANAGEMENT IN 4G BROADBAND ACCESS NETWORK.
  • 2017 (1st Engin supervisor) Zainab Abbas Harbi, Intelligent Clinical Decision Making
  • 2016 (2nd Engin supervisor) Ahmed Saad Kareem Al Saadi, Cognitive Network Framework for Heterogeneous Wireless Mesh Systems.
  • 2016 (1st Engin supervisor) Mahmud Abdulla Mohammad, Video-based Situation Assessment for Road Safety.
  • 2015 (1st Engin supervisor) Alexandre Noyvirt, Human Perception Capabilities for Socially Intelligent Domestic Service Robots.
  • 2015 (2nd Engin supervisor) Obokhai Kess Asikhia, Evaluating Intuitive Interactions Using Image Schemas.
  • 2015 (2nd Engin supervisor) Mohamed Bennassar, Clinical Decision Support System for Early Detection and Diagnosis of Dementia.
  • 2013 (1st Engin supervisor) Ioannis Kaloskampis, Analysing Complex Human Behaviours In Multmedia Streams.
  • 2013 (2nd Engin supervisor) Diman Todorov, Enhanced Interpretation of the Mini-Mental State Examination (MMSE).
  • 2012 (2nd Engin supervisor) Ivan Stankov, Semantically Enhanced Document Clustering.
  • 2012 (2nd Engin supervisor) Engku Fadzli Hassan Syed Abdullah, Automated Mood Boards: Ontology Based Semantic Image Retrieval.
  • 2008 (1st Engin supervisor) Yue Zheng, Modelling, Tracking and Generating Human Interaction Behaviours in Video.
  • 2008 (1st Engin supervisor) Andrew Aubrey, Exploiting The Bimodality of Speech in The Cocktail Party Problem.

Supervised MPhil Students: Completed

  • 2014 (1st Engin supervisor) Sun Peng, Joint EEG/fMRI Signal Model For EEG Separation and Localization.
  • 2008 (2nd Engin supervisor) Mansi Ghodsi, Nonlinear Dynamical System and Support Vector Machine for Detection of Temporomandibular Disorder.