• United Kingdom

20052019
If you made any changes in Pure these will be visible here soon.

Personal profile

Personal Statement

Feng Dong joined the University of Strathclyde from 2nd Sept 2019. He is currently a professor at the Department of Computer and Information Sciences. He was awarded a PhD from Zhejiang University, China.  His recent work has also developed new areas in visual analytics, pattern recognition, AI, parallel computing and GPU, image-based rendering and figure animation.

In brief, Feng Dong's profile can be summarised as follows:

  • Leading and managing collaborative research projects and teams across Europe to conduct externally funded cross-disciplinary research projects in health technology and computational creativity, with a substantial track record in attracting external research funding by gaining around £7 million external research fund (as PI) from the EC and EPSRC since Sept 2007. These include 5 European grants and 3 EPSRC grants (as PI) and project coordinator & leading investigator for 4 collaborative research projects.

  • Network with leading research organisations and researchers across the UK and Europe through jointwork in research grants.

  • Collaboration with medical professionals through collaborative research projects and joint clinical pilots, and active engagement with the end users to empower the society at large in healthcare, targeting significant impact beyond academia.

  • Close working relationships with the industry through joint work in research grants.

  • Over 15 years of teaching practice in the UK with substantial experience in the design and delivery of a wide range of research-informed teaching activities at both post-graduate and under-graduate levels.

Research Interests

Intelligent data analytics and visualization to addressed a range of issues in:

  • AI to support knowledge discovery
  • Visual data analytics
  • Computer vision and image analysis
  • Health data interoperability
  • Medical visualization and computer graphics

Expertise & Capabilities

Main knowledge contributions towards intelligent data analytics fall into a range of areas including:

-   Knowledge discovery in AI for healthcare to support patient self-management of general health and chronic conditions, involving smart monitoring, data validation from heterogeneous sensors,  personal activity and event recognition,  health information recommendation, personal health status estimation and serious gaming.

-    Intelligent data analytics for computational creativity in AI by coordinating the EC-funded Dr Inventor research project and leading the development of the Dr Inventor platform. The Dr Inventor surrogate acts as a personal research assistant, utilising machine-empowered search and computation to bring researchers extended perspectives for scientific innovation by informing them of a broad spectrum of relevant research concepts and approaches, by assessing the novelty of research ideas, and by offering suggestions of new concepts and workflows with unexpected features for new scientific discovery.

-   Visualization and parallel computing (GPU) for large-scale medical data, , including transfer function for feature enhancement in volume rendering of medical data; viewpoint selection and lighting design for volume rendering of medical data; Non-photorealistic volume rendering for feature enhancement from medical data; GPU-based iso-surface extraction from volume data and automated GPU-based parallelisation for images operations and image feature extractions.

-   Visual analytics for health data to support the navigation, query and understanding of health records, clinical driven research in predictive models for cancer growth in response to treatment options, and the discovery of data patterns within patient cohort in both clinical and lifestyle domains

-   Computer vision and machine learning for computer graphics research, including sparse modelling and representation for human motions,  blind motion deblur for natural images,  adaptive texture synthesis for high fidelity images and image based rendering based on inferences in machine learning

-    Health data interoperability to support long-term collection of personal health information by aggregating  electronic and personal health records, lifestyle data and drug information in a decentralised approach to offer easy access to personal medical history, empower the patients, improve self-management, and facilitate clinical research with significant advantages in privacy, security, safety, transparency and data integrity.

The recent active research projects include: 

REAMIT- The project proposes to adapt and apply existing innovative technology to food supply chains in NWE to reduce food waste and hence improve resource efficiency (Project Information: European Commission Interreg North-West Europe, €608,118 for the local institution, from 2019 to 2022.) - Role: Co– Investigator

Aquaculture 4.0 -- The project will bring together several cutting-edge digital technologies including sensor networks for online monitoring, diagnosis, control and optimisation of aquaculture production, 5G communication for low-latency, high data rate, real-time transmission of big data, internet-of- things (IoT) system for big data storage, analytics, modelling and model-based decision making. By integration of these digital technologies, the project will deliver a prototype system of precision Aquaculture 4.0, and demonstrate the economic, environmental and social benefits through pilot applications in China (Project Information: Innovate UK, over £223,209 for the local institution, Feb 2019 – Dec 2021) - Role: Co-Investigator

Industrial Relevance

Feng Dong has gained significant experience in research collaboration through the research projects. In the last 5 years he has participated in 7 median-to-large externally funded research projects, all of which were multi-disciplinary involving  substantial collaborations and knowledge exchange with external partner organisations from a wide range of disciplines including medicine, computational biology, law and industry. In these projects, there were significant amount of activities in project exploitation and dissemination led by the industrial partners.  He has also been engaged in activities with the general public to empower the society at large in healthcare and wellbeing with the goal of providing solutions to real world problems and improving public health. He also has strong links with the industries in China.

And he have worked very closely with healthcare professionals, researchers in healthcare. With them, they have conducted clinical evaluations in the UK and Europe, including:

-        MyHealthAvatar for eye patients in Moorfields Eye Hospital, UK

-        MyHealthAvatar for diabetes patients through Horizon Health Choices, UK.

-        MyHealthAvatar for prostate and breast cancer patients in European Institute of Oncology, Italy

 

Academic / Professional qualifications

  • PhD in Computer Science, Zhejiang University, China
  • PGCERT Higher Education 
  • The Higher Education Academy Fellow -

Fingerprint Dive into the research topics where Feng Dong is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

annotations Physics & Astronomy
Visualization Engineering & Materials Science
Magnetic resonance imaging Engineering & Materials Science
Tumors Engineering & Materials Science
tumors Physics & Astronomy
root-mean-square errors Physics & Astronomy
Computer graphics Engineering & Materials Science
Interoperability Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects 2005 2018

Research Output 2019 2019

Literature explorer: effective retrieval of scientific documents through nonparametric thematic topic detection

Wu, S., Zhao, Y., Parvinzamir, F., Th. Ersotelos, N., Wei, H. & Dong, F., 2 Aug 2019, 18 p.

Research output: Contribution to journalArticle

Open Access
File
Visualization
Semantics

Nephroblastoma analysis in MRI images

Kaba, D., McFarlane, N., Dong, F., Graf, N. & Ye, X., 31 Jul 2019, 38, 2, p. 173-183 11 p.

Research output: Contribution to journalArticle

Open Access
File
annotations
Magnetic resonance imaging
Tumors
tumors
root-mean-square errors