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Personal profile
Personal Statement
David Li is a senior lecturer in optoelectronics at the Department of Biomedical Engineering. He received his PhD in electrical engineering from the National Taiwan University. He then joined the Industrial Technology Research Institute (ITRI), Taiwan, as an R&D engineer working on 1.25-12.5Gbps optical communication chipsets and wireless communication IP, knowledge transfer, and international joint projects (with the Carnegie Mellon University, Pittsburgh, USA).
From 2007 to 2011, he worked at the Institute for Integrated Micro and Nano Systems, University of Edinburgh, on the European projects "MEGAFRAME" and "METOXIA" for CMOS single-photon avalanche diode (SPAD) based fluorescence lifetime imaging microscopy (FLIM) cameras and analogue front-end circuits. He has invented several hardware-embedded imaging processors, which resulted in the first video-rate FLIM imaging on CMOS SPAD arrays.
From January 2014, he joined the Centre for Biophotonics, SIPBS, after his first lecturing position in biomedical engineering and embedded systems at the School of Engineering and Informatics, University of Sussex (where he led a research team working on industry-funded projects).
His current research interests include CMOS imaging and sensor systems, embedded systems, digital signal processing, mixed-signal integrated circuits, fluorescence-based sensing systems, and artificial intelligence. He has been working with researchers within the UK and overseas, such as the Netherlands, Switzerland, Belgium, Germany, France, China, the USA, and Taiwan.
Please also check https://scholar.google.co.uk/citations?hl=en&user=UFE7FyIAAAAJ.
David would be delighted to collaborate with colleagues or hear from potential PhD candidates interested in exploring any aspect of imaging/sensing and embedded systems research.
Teaching:
BE905: Biosignal Processing and Analysis (Spring 2022)
Available Studentships for 2022:
3-year PhD studentships in time-resolved spectroscopy/imaging and machine-learning techniques for biomedical applications are expected to start on 1st October 2022.
Candidates interested in these projects could send CVs and transcripts to him for further discussions.
Other funding opportunities for post-graduate students, postdoc researchers and visiting scholars:
China Scholarship Council: http://en.csc.edu.cn/ (for students in China)
Commonwealth Scholarships: http://cscuk.dfid.gov.uk
EU Marie Sklodowska-Curie Fellowships: http://ec.europa.eu/research/mariecurieactions/
Newton Fellowships: http://www.newtonfellowships.org
Royal Society Newton Mobility Grant: https://royalsociety.org/grants-schemes-awards/grants/newton-mobility-grants/
Royal Academy of Engineering Newton Mobility Grant: http://www.raeng.org.uk/grants-and-prizes/international-research-and-collaborations/newton-fund-programmes/newton-research-collaboration-programme
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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PAIM (πM): portable AI-enhanced fluorescence microscope for real-time target detection
Jiao, Z., Zang, Z., Wang, Q., Chen, Y., Xiao, D. & Li, D. D. U., 31 Aug 2023, In: Optics and Laser Technology. 163, 12 p., 109356.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Compact and robust deep learning architecture for fluorescence lifetime imaging and FPGA implementation
Zang, Z., Xiao, D., Wang, Q., Jiao, Z., Chen, Y. & Li, D. D-U., 2 Mar 2023, (E-pub ahead of print) In: Methods and Applications in Fluorescence .Research output: Contribution to journal › Article › peer-review
Open AccessFile2 Downloads (Pure)
Datasets
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Data for: "Investigations on average fluorescence lifetimes for visualizing multi-exponential decays"
Li, Y. (Creator), Sapermsap, N. (Contributor), Safar, M. (Contributor), Cunningham, M. R. (Contributor), Chen, Y. (Contributor) & Li, D. (Creator), University of Strathclyde, 3 Dec 2020
DOI: 10.15129/062e9e11-9d7f-49e5-a332-2b10d695bdcd
Dataset