Imperial College London


Faculty of Natural SciencesDepartment of Mathematics

Research Associate







1009bElectrical EngineeringSouth Kensington Campus





Lan Wang is engaging in postdoctoral research in clinical analytics and data science in the Department of Surgery and Cancer at Imperial College London. She received Ph.D. degree in electrical engineering research from Imperial College London, UK, in 2018, M.Sc. degree in Communications and Signal Processing from Imperial College London and B.Sc. degree in Electronic Engineering from Shandong University, China. Before studying at Imperial College, she worked as a senior R&D engineer in integrated telecommunication network management systems in China. She has been involved in  Cancer Pathway project (2019-present), EU FP7 PANACEA (proactive autonomic management of cloud resources, 2014-2016) and the H2020 project KONFIDO (Secure and Trusted Paradigm for Interoperable eHealth Services, 2017-2019). Her research interests are in data science in Healthcare, machine learning and data analytics for improving Quality of Service and saving energy in Cloud computing systems and computer networks.     



Wang L, 2017, The random neural network for cognitive traffic routing and task allocation in networks and the cloud, Probability in the Engineering and Informational Sciences, Vol:31, ISSN:0269-9648, Pages:540-560

Brun O, Wang L, Gelenbe E, 2016, Big data for autonomic intercontinental overlays, Ieee Journal on Selected Areas in Communications, Vol:34, ISSN:1558-0008, Pages:575-583


Wang L, Brun O, Gelenbe E, 2017, Adaptive workload distribution for local and remote clouds, IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, Pages:3984-3988, ISSN:1062-922X

IEEE, 2016, Publisher’s Information, 2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), IEEE

Gelenbe E, Wang L, 2016, Real-Time Traffic over the Cognitive Packet Network, International Science Conference on Computer Networks 2016, Springer International Publishing, Pages:3-21, ISSN:1865-0929

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