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Dr Fang Wang

 
Dr Fang Wang is a Senior Lecturer in the Department of Computer Science at Brunel University London. She received a PhD in artificial intelligence from the University of Edinburgh and worked as a senior researcher in the research centre of British Telecom (BT) Group, before she joined Brunel University London in 2010. Dr. Wang has published a number of papers in books, journals and conferences and filed a series of patents.Dr. Wang is an established teacher and researcher in computer science and artificial intelligence. Her research interests include nature-inspired computing, agents, intelligent information processing, intelligent distributed computing, cognitive radio networks, e-learning and cloud education, cognitive science and computer vision. She actively participated in a number of EU, EPSRC,  BT long term research projects and received several technical awards, including the Gordon Radley Technical Premium Highly Commended award of BT and ACM Best Student Paper Award at the Third International Conference on Autonomous Agents. She is on the editorial board of Aloy Journal of Soft Computing and Applications (AJSCA) and serves on many program committees.
"My expertise are mainly in artificial intelligence and its applications, including machine learning, optimisation, data mining and data analysis. We are especially good at developing adaptive, self-organising and intelligent algorithms to improve system performance, by learning from histories and exploring (implicit) associations or information automatically, even in a dynamically changing environment. The algorithms are usually specially designed to suit different customer needs and applications.”
Main Interest:
Dr. Wang’s main research interest is in artificial intelligence and its applications. This includes using nature-inspired techniques such as intelligent agents, swarm intelligence, evolutionary computing and neural networks to solve real world applications such as network optimisation, radio spectrum management, decentralised computing, user analysis, self-organising communities, and so on.
 
Ongoing Work:
  • Network optimisation, including routing, network topology optimisation, resource management and many others on both computing networks or wireless sensor networks;
  • Cyber security, including intelligent intrusion detection to detect and prevent various types of attacks on computing networks or Cloud;
  • Intelligent education systems, including intelligent student profiling, automatic learning group formation, group recommendation and mobile education games development;
  • e-Commerce, such as Business-to-Business (B2B) transaction network analysis based on data collected from eBay and automatic product recommendations to businessmen;
  • User services including user experience analysis and learning, personalised user services and self-organising user communities;
  • Smart transportation systems associated with smart cities, including journey planning, time and passenger prediction and instant alerts.