Engineering Discipline:
- Electronics and Electrical Engineering
Biography:
Dr. Yunjie Yang is a Senior Lecturer (Associate Professor), Chancellor’s Fellow in Data Driven Innovation and Bayes Innovation Fellow at The University of Edinburgh. He is also the research affiliate of the Edinburgh Futures Institute (EFI). He received his Ph.D. in Engineering Electronics from The University of Edinburgh, MSc in Control Science & Engineering from Tsinghua University, and BEng in Measurement & Control Engineering from Anhui University. After obtaining his PhD, he briefly worked as a Postdoctoral Research Associate in Chemical Species Tomography at The University of Edinburgh.
Dr. Yang’s research interests are in the areas of sensing and imaging with AI-powered tomography, machine learning, digital twins, and flexible sensors for wearables and robotics. His research aims to improve observability in industrial (e.g. multiphase flows), robotics (e.g. soft robotics), and biomedical engineering and to address the pressing challenges of efficient utilisation/interpretation of enormous sensory data. His research has led to over 100 peer-reviewed journal and international conference publications, many of which were published in high-impact journals such as Nature portfolio journals (Nature Machine Intelligence, Communications Engineering), IEEE TNNLS, TMI, TII, TBME and TIM. His research has been licensed to overseas research institutes and industry partners and received wide media coverage, including BBC, EFE, USA Today and STV.
He is the Associate Editor of IEEE Transactions on Instrumentation and Measurement, the Editorial Board Member of Scientific Reports, the Guest Editor of IEEE Sensors Journal, and serves as the regular reviewer for over 50 high-impact international journals. He served as the track chair of multiple international conferences. He was the recipient of the 2015 IEEE I&M Society Graduate Fellowship Award. He is the Senior Member of IEEE, the Fellow of the International Society for Industrial Process Tomography (FISIPT), and the Fellow of the Higher Education Academy (FHEA).
Research group: UoE SMART Group www.yangresearchgroup.com
Media coverage:
- BBC: https://www.bbc.co.uk/iplayer/episode/m001m1mr/click-medical-marvels (from 8:12)
- DDI case study: https://ddi.ac.uk/case-studies/smart-e-skin-for-soft-robotic-perception/
- STV: https://news.stv.tv/east-central/edinburgh-university-researchers-develo...
- EFE: Crean la primera "piel electrónica" para que los robots sientan (efe.com)
- The Independent: Best inventions and discoveries of 2023: From robot skin to mind-reading caps | The Independent
- Evening Standard Podcast: https://play.acast.com/s/tech-science-daily/e-skin-boosts-robot-self-awareness
- Metro: https://metro.co.uk/2023/02/24/scientists-develop-electronic-skin-that-allows-robots-to-feel-18340234/
- The Independent: https://www.independent.co.uk/tech/robots-self-aware-ai-skin-b2288369.html
- The Daily Telegraph - https://www.telegraph.co.uk/news/2023/02/23/e-skin-game-scientists-invent-layer-silicone-allows-robots-touch/
- Irish Examiner: https://www.irishexaminer.com/world/arid-41078627.html
- Irish Independent: https://www.independent.ie/world-news/europe/britain/robots-will-be-able-to-touch-and-feel-the-world-like-humans-as-scientists-invent-flexible-e-skin-42357098.html
- Press and Journal: https://www.pressandjournal.co.uk/news/scotland/5432212/e-skin-developed-to-boost-self-awareness-in-soft-robots/
(Office: 1.13 Alexander Graham Bell (AGB) Building)
Academic Qualifications:
- Doctor of Philosophy (PhD), The University of Edinburgh, UK
- Master of Science (MSc) (Outstanding Graduate and Thesis), Tsinghua University, China
- Bachelor of Science (BEng) (Outstanding Graduate), Anhui University, China
Professional Qualifications and Memberships:
- Senior Member of IEEE
- Senior Member of IEEE I&M Society
- Member of IET
- Fellow of ISIPT
- Fellow of Higher Education Academy (FHEA)
Teaching:
- Signals and Communication Systems 3 (ELEE09027)
- Digital System Laboratory 3 (ELEE09035)
- Electrical Engineering 1 Tutorial (ELEE08001)
- Supervision of PhD, MSc, MEng and BEng projects
Research Interests:
- AI-powered multi-modal imaging
- Digital twins for complex systems and digital healthcare
- Soft robotics perception and control
- Machine learning for inverse problems
Specialities:
- Imaging and machine learning
- Soft sensors
- Soft robotics
- Digital twins
Further Information:
We welcome undergraduates, graduates, and postdocs who are interested in joining our group. Please feel free to contact us at anytime.