Dr Qicheng Yu

Dr. Qicheng Yu is an Associate Professor (Enterprise) at the School of Computing and Digital Media and serves as Subject Standard Board Chair of Computer Science and Applied Computing and is course leader of the Data Analytics MSc and Information Technology (Distance Learning) MSc

Male lecturer Dr Qicheng Yu gazes at the camera.

Dr Qicheng Yu

Qicheng is an Associate Professor (Enterprise) at the School of Computing and Digital Media. He obtained his PhD in Computer Science from London Met.
He has rich experience in both academia and industry and has a keen interest in research and enterprise. He has led and been involved in Innovate UK funded KTPs and Academic Startup Accelerator Programme as well as the university research programmes.

He is one of empowering London Challenge Champions and the leader of SME clinic of Business Data Analysis and involves cross disciplinary research projects to contribute to our city, socially, culturally, environmentally, and economically.

Qicheng Yu is the course leader of the Data Analytics MSc, a PhD supervisor of many PhD research students and serves as internal and external PhD examiners. He teaches both PG and UG subjects in Data Science, AI, and Computer Science.


His research interests include Artificial Intelligence and Machine Learning, Cyber Security, Big Data Management.

He is member of following London Met research Centre and Lab:

  • Cyber Security Systems Research Centre
  • Centre for Applied Research in Empowering Society
  • London Met Lab: Empowering London

PhD supervision projects completed:

  • PDF Steganography
  • New Framework Development for Improving Features Engineering for Fraud Detection of Financial Transactions using Machine Learning Methods
  • A Proactive Chatbot Framework designed to Assist Students based on the PS2CLH Model
  • Web Penetration Testing Automation
  • Investigating the development of a framework for assessing performance and trust of e-Government services / systems


  • Fake review detection using multimodal data with machine learning and deep learning.
  • Almada, Q. Yu and P. Patel, (2023) "NLP K-Means Algorithm Incorporated into a Proactive Chatbot to Assist Failing Students," in 2022 International Conference on Intelligent Computing and Machine Learning (2ICML), Qingdao, China, IEEE 2023 pp. 24-30.
  • Yu, Q., Healy, S., Ravi, I., Patel, P. (2022). Data Ingestion Pipeline and Data Marts to Empower UK Researchers, Academics, and Business and Economic Decision Makers. In: Arai, K. (eds) Advances in Information and Communication. FICC 2022. Lecture Notes in Networks and Systems, vol 438. Springer, Cham. https://doi.org/10.1007/978-3-030-98012-2_13 
  • Almada, Arlindo, Yu Q., & Patel P. (2022) Representation of the Student’s Controllable Performance Features Based on PS2CLH Model ISSN: 2435-9467 – The Barcelona Conference on Education 2022: Official Conference Proceedings https://doi.org/10.22492/issn.2435-9467.2022.27
  • Ikeda, Chie, Ouazzane, Karim, Yu, Qicheng and Hubenova, Svetla (2021) New feature engineering framework for deep learning in financial fraud detection. International Journal of Advanced Computer Science and Applications, 12 (12). pp. 10-21. ISSN 2156-5570. https://dx.doi.org/10.14569/IJACSA.2021.0121202
  • Ikeda,C. , Ouazzane,K. , Yu,Q., (2020), A New Framework of Feature Engineering for Machine Learning in Financial Fraud Detection, 10th International Conference on Advances in Computing and Information Technology (ACITY 2020), November 28~29, 2020, London, United Kingdom Volume Editors : David C. Wyld, Dhinaharan Nagamalai (Eds) ISBN : 978-1-925953-29-9 
  • Almada,A. , Yu,Q. , Patel,P., (2019), PS2CLH: "A Learning Factor Model for Enhancing Students" Ability to Control Their Achievement, Published on: December 23rd, 2019 ACE2019, Toshi Center Hotel, Tokyo, Japan, ISSN: 2186-5892 
  • Liu, L. , Xue, Y., Zhang, J., Liu J., Yu, Q., Li, C. (2014), Web Service Based Grid Workflow Application in Quantitative Remote Sensing Retrieval , In: 2014 IEEE/IGARSS.
  • Yu, Q. and Patel, P. (2011), Teaching Oracle Data Miner using Virtual Machine. In: 2011 Teaching, Learning and Assessment of Databases in Information and Computer Sciences of the Higher Education Academy.
  • Yu, Q., McCann, J.A. and Cai, F.F. (2009), An Agent-Based Adaptive Join Algorithm for Distributed Data Warehousing. In: 2009 IEEE Computation World: Future Computing, Service Computation, Cognitive, Adaptive, Content, Patterns, p.pp.72–77.
  • Cai, F., Yu, Q. and Patel, P. (2002), Issues of web-enabled high performance databases. In: HPC VII: Seventh International Conference on Applications of High-Performance Computing in Engineering. pp.229–239.
  • Cai, F. and Yu, Q. (2000), A framework for data mining and information retrieval on the Internet. In: HPC 2000 International conference on applications of high-performance computing in engineering. pp.467–476.

Professional membership

  • FHEA - Fellow of the Higher Education Academy
  • Member of the Data Science Association
  • Member of the Cyber Security Systems Research Centre at London Met
  • Member of the Intelligent Systems Research Centre at London Met


External funded Project

  • Innovate UK's funded two-year KTP project to develop an “Intelligent data dashboard for Filisia Interfaces LTD”, with a £131,296 grant awarded in 2022 as Knowledge Base supervisor.
  • Innovate UK Cyber Security Academic Startup Accelerator Programme, “SYCAMORE: Vulnerability Analysis and Risk Assessment of Cyber Security Policies” and served as Principal Investigator on a £19,200 grant in 2019.
  • Innovate UK funded two-year KTP project “Knowledge Management System for Coborn Engineering Ltd”, receiving a grant of £115,000 in 2015 as knowledge base supervisor.
  • Innovate UK funded two-year KTP project “Business Forecasting Solution for ASK Electronic Ltd”, receiving a grant of £115,000 in 2015 as an academic supporter.

Internal funded Project

  • London Met Participatory Research, “Developing Local Inclusivity Networks – Assessing barriers and opportunities to amplify young disabled people’s voice and enhance access to local cultural offer”, receiving grant of £19,852 in 2023 as a core team member.
  • London Met rescaling project “Development of Interactive Socio-demographic Map for Multidisciplinary Research”, receiving grant of £3,000 in 2021 as principal investigator.
  • London Met rescaling project “Design of data ingestion pipeline for empowering” receiving grant of £4,000 in 2021 as principal investigator.

Room: T10-01, Tower Building

Phone: +44 (0)20 7133 3674