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. She has multidisciplinary background of BSc and MSc in Computer Science and PhD in Chemical Engineering.
Dr. Yang has twelve-years academic experience of applying her data science and machine learning knowledge for multi-field process modelling and analysis. In 2011, Dr. Yang received her PhD degree 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). Besides five high quality journal papers, the decisional tool designed and developed by Dr. Yang, which combined with Process analytical technology (PAT) and machine learning techniques, was sponsored and adopted by GlaxoSmithKline Pharmaceuticals (GSK) for its nanoparticle product line.
Dr. Yang joined Imperial College London and then University College London as a postdoctoral research associate. During this period, Dr. Yang accumulated great knowledge and experience in biopharmaceutical manufacturing process and personalised medicine and established collaborations with both academical and industrial partners. Collaborated with UCB and Eli Lily, the leaders of biopharmaceutical industries in UK, Dr. Yang established process models and Cost of Goods (CoGs) models of biomanufacturing process with discrete-event modelling and Monte Carlo simulation manufacturing facility fit analysis. A decision-support tool which combined the process models, CoGs models and machine learning models using decision tree algorithms had been greatly complimented by biopharmaceutical industry users. Supported by world leading pharmaceutical companies, Pall Corporation, Merck and Medimmune, Dr. Yang’s independent research of digital twins for continuous biomanufacturing process was awarded £5000 funding by Future targeted healthcare manufacturing hub in UCL. Dr. Yang has 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.
Qualifications
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 responsible for the international engagement of Department of Chemical Engineering.
Newest selected publications
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.
Wang, XZ., Yang, Y., Li, R., Mcguinnes, C., Adamson, J., Megson, IL. and et al. (2014) 'Principal component and causal analysis of structural and acute in vitro toxicity data for nanoparticles'. Nanotoxicology, 8 (5). pp. 465 - 476. ISSN: 1743-5390