Imperial College London


Faculty of EngineeringDyson School of Design Engineering

Senior Lecturer



+44 (0)20 7594 8853n.li09 Website




1M03Royal College of ScienceSouth Kensington Campus





Dr Nan Li’s research addresses one of the major challenges facing the transport industry world-wide: developing technological breakthroughs in the manufacturing and design of high-performance lightweight vehicles for a more environmentally-friendly footprint. Nan joined Dyson School of Design Engineering in May 2017. Before joining the School, she studied and worked in the Metal Forming and Materials Modelling Group of the Department of Mechanical Engineering, Imperial College London (2009-2017). Her current main research focuses are:

  • Developing innovative manufacturing processes for lightweight structures. She has conducted extensive research work on developing novel low-cost hot forming processes for high-strength, lightweight and complex-shaped panel components for a family of advanced lightweight materials, e.g. ultra-high strength steels, Al alloys, Ti alloys, and Polymer Matrix Composites.
  • Lightweight structural design and optimisation. Key enablers proposed by Dr Nan Li: New design methods relating to hierarchical optimisation and manufacturing constraints; advanced Design-Manufacturing-Integrated modelling; and Machine-Learning empowered Design for Manufacturing.

All of Dr Nan Li’s research projects address real industrial needs, and tackle scientific and technological challenges in materials processing, manufacturing processes, and structural designs, through fundamental studies based on experimental, analytical, and numerical methodologies.

The applications of Dr Nan Li’s research are primarily in automotive and aerospace industries.

Dr Nan Li is highly motivated to explore technological innovations, particularly in relation to the design of novel manufacturing processes, mechanical testing methods and apparatus, and lightweight vehicle structures. She has authored 60 publications and contributed 10 patents. Her proposed research on producing vehicle lightweight structures is favoured by a wide range of industrial collaborators, including SAIC MOTOR (UK and China), Aisin Takaoka (Japan), TATA Steel (Europe), AP&T (Sweden), Lotus (UK), PAB Coventry (UK), Impression Technologies (UK), ESI (France), AVIC (China), Shougang (China), Doncasters (UK), Monolith AI (UK), etc. Nan was awarded the ‘Rowbotham Medal’ 2017 by the Institute of Materials, Minerals and Mining (IOM3) in recognition of her outstanding contribution to the development of the innovative use of materials for automotive applications. She was also recognized as one of the ‘Top 50 Women in Engineering under 35’ in 2017.  


Interested PhD applicants should send an up-to-date CV to Dr Nan Li, with GPA included. Suitable candidates will be required to complete an electronic application form at Imperial College London in order for their qualifications to be addressed by College Registry. Qualified candidates will be interviewed in due course.




Attar HR, Foster A, Li N, 2023, Development of a deep learning platform for sheet stamping geometry optimisation under manufacturing constraints, Engineering Applications of Artificial Intelligence, Vol:123, ISSN:0952-1976, Pages:1-23

Zheng K, He Z, Qu H, et al., 2023, A novel quench-form and in-die creep age process for hot forming of 2219 thin aluminum sheets with high precision and efficiency, Journal of Materials Processing Technology, Vol:315, ISSN:0924-0136, Pages:1-13

Zhu L, Wang X, Sun L, et al., 2022, Optimisation of selective laser melted Ti6Al4V functionally graded lattice structures accounting for structural safety, Materials, Vol:15, ISSN:1996-1944, Pages:1-26

Brooks R, Wang H, Ding Z, et al., 2022, A review on stamp forming of continuous fibre-reinforced thermoplastics, International Journal of Lightweight Materials and Manufacture, Vol:5, ISSN:2588-8404, Pages:411-430


Attar HR, Foster A, Li N, 2022, Optimisation of panel component regions subject to hot stamping constraints using a novel deep-learning-based platform, The 19th International Conference on Metal Forming, IOP Publishing, Pages:1-11, ISSN:1757-8981

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