Dr Yang Yang
Lecturer in Chemical Engineering
- Chemical Engineering
- College of Engineering, Design and Physical Sciences
Summary
Dr. Yang is a Lecturer in Chemical Engineering Department. She is currently leading the Digital Manufacturing Group, which aims to integrate the advanced computational technologies, such as big data, machine/deep learning, simulation and visualisation, to facilitate manufacturers achieve tangible improvements in key metrics. Her multidisciplinary research spans across diverse industrial sectors, addressing the challenges and driving the industry towards a new revolution.
Dr. Yang has a multidisciplinary background. She obtained her BSc and MSc degree in Computer Science from Tianjin University, China and received her PhD sponsored by Overseas Research Scholarships (ORS) and Tetley & Lupton Scholarships (TLS) from University of Leeds. During her PhD, she successfully applied data mining and machine learning techniques to identify the optimal composition of nano-photocatalyst (TiO2). The decisional tool designed and developed by Dr. Yang, which combined process analytical technology (PAT), image analysis and machine learning techniques, was sponsored and adopted by GlaxoSmithKline Pharmaceuticals (GSK) for its nanoparticle product line. Due to her outstaning performance, Dr.Yang was awarded Chinese Government Award for Outstanding Self-financed Students Abroad in 2010.
Prior to joining Brunel, Dr. Yang worked at Imperial College London and University College London as a postdoctoral researcher. During this period, Dr. Yang accumulated great knowledge and experience in biopharmaceutical manufacturing process and personalised medicine development. Collaborated with UCB and Eli Lily, the leaders of biopharmaceutical industries in UK, Dr. Yang established process models and ecnomic models of biomanufacturing process using discrete-event modelling and Monte Carlo simulation methods. A decision-support tool which combined the process models, ecnomic models and machine learning models for facility fit analysis had been greatly complimented by biopharmaceutical industry users. Supported by Pall Corporation, Merck and Medimmune, Dr. Yang’s research of digital twins for continuous biomanufacturing process awarded funding by Future targeted healthcare manufacturing hub at UCL. She is currently holding Brunel Research Initiative & Enterprise Funding for digital twin system of hydrogen production.
Multi-omics data analysis for personalised medicine development is another research intrest of Dr. Yang. She led a collaboration with Shanghai Pulmonary Hospital (China) to construct a decision-support tool with big data analysis for personalized diagnosis and treatment of lung cancer. She is currently collaborating with Life Science Department for cancer and drug dependency analysis.
Qualifications
Fellow of Higher Education Academy, UK, 2023
PhD in Chemical Engineering, Leeds University, UK, 2011
MSc in Computer Science, Tianjin University, China, 2007
BSc in Computer Science, Tianjin University, China, 2004
Responsibility
Dr. Yang is the International Coordinator and Academic Exchange Coordinator of Department of Chemical Engineering.
Newest selected publications
Davies, WG., Babamohammadi, S., Yang, Y. and Masoudi Soltani, S. (2023) 'The rise of the machines: A state-of-the-art technical review on process modelling and machine learning within hydrogen production with carbon capture'. Journal of Natural Gas Science and Engineering, 118. pp. 1 - 20. ISSN: 1875-5100 Open Access Link
Yang, Y., Xu, L., Sun, L., Zhang, P. and Farid, SS. (2022) 'Machine learning application in personalised lung cancer recurrence and survivability prediction'. Computational and Structural Biotechnology Journal, 20. pp. 1811 - 1820. ISSN: 2001-0370 Open Access Link
Zhang, H., Yang, Y., Zhang, C., Farid, SS. and Dalby, PA. (2021) 'Machine learning reveals hidden stability code in protein native fluorescence'. Computational and Structural Biotechnology Journal, 19. pp. 2750 - 2760. ISSN: 2001-0370 Open Access Link
Yang, Y., Velayudhan, A., Thornhill, NF. and Farid, SS. (2017) 'Multi-criteria manufacturability indices for ranking high-concentration monoclonal antibody formulations'. Biotechnology and Bioengineering, 114 (9). pp. 2043 - 2056. ISSN: 0006-3592 Open Access Link
Yang, Y., Velayudhan, A., Thornhill, NF. and Farid, SS. (2015) 'Manufacturability Indices for High-Concentration Monoclonal Antibody Formulations', inComputer Aided Chemical Engineering. Elsevier. , 37. pp. 2147 - 2152.