Imperial College London

Mr Liuqing Chen (Kevin Chen)

Faculty of EngineeringDyson School of Design Engineering

Research Postgraduate







Dyson BuildingSouth Kensington Campus





Liuqing Chen (Kevin Chen) is a PhD student at Imperial College London, starting from September 2015. He is supervised by Peter Childs. His general research interests include: the application of artificial intelligence in design and manufacturing; data-driven design; AI design; design creativity; computational creativity; creativity tool design; data mining and data visualization; machine learning and deep learning; Natural Language Processing (NLP).

The research projects he has completed or has been doing include:
- A data-driven creativity engine
- An artificial intelligence based combinational creativity
- Creative design interpretation with artificial intelligence
- Human-in-the-loop design with machine learning

He got his bachelor degree at Ocean University of China in 2012, and then went to Shandong University as a postgraduate, which was suspended one year later. He got the full scholarship (Erasmus Mundus program) from EU and obtained a master degree at Politecnico di Torino in Italy from 2013 to 2015. 

Research Projects

A data-driven creativity engine design B-Link: Developed a data-driven tool for information retrieval and creative knowledge discovery. The tool aquatically collected over 3 million keywords from digital academic publications by NLP, and built a massive network graph for mining knowledge associations and new insights by means of proposed far-association algorithms.

Artificial intelligence for combinational design: Proposed a new Generative Adversarial Networks (GAN) model for generating synthesised images, and programmed the model using Tensorflow in a case study in which qualitative data was transformed into quantitative data for evaluation and model validation.

Interpreting innovative design with artificial intelligence:To capture the composition of a combinatorial design solution, an integrated deep learning model was proposed. In the model, convolutional neural networks (CNN) was used for image recognition and long-short term memory (LSTM) was applied for named entity extraction and relation classification.



Google Scholar



Chen L, Wang P, Dong H, et al., 2019, An artificial intelligence based data-driven approach for design ideation, Journal of Visual Communication and Image Representation, Vol:61, ISSN:1047-3203, Pages:10-22

Garvey B, Chen L, Shi F, et al., 2019, New directions in computational, combinational and structural creativity, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Vol:233, ISSN:0954-4062, Pages:425-431

Han J, Park D, Shi F, et al., 2019, Three driven approaches to combinational creativity: Problem-, similarity- and inspiration-driven, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Vol:233, ISSN:0954-4062, Pages:373-384

Han J, Shi F, Chen L, et al., 2018, A computational tool for creative idea generation based on analogical reasoning and ontology, Ai Edam, Vol:32, ISSN:0890-0604, Pages:462-477

Han J, Shi F, Chen L, et al., 2018, The Combinator – a computer-based tool for creative idea generation based on a simulation approach, Design Science, Vol:4, ISSN:2053-4701

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