Engineering Discipline:
- Electronics and Electrical Engineering
Biography:
Dr. Yunjie Yang is a Senior Lecturer (Equivalent: Associate Professor) at The University of Edinburgh. He is also an affiliate of the Edinburgh Futures Institute (EFI), the Edinburgh Generative AI Laboratory (GAIL) fellow, and part of the Edinburgh Centre for Robotics. He was the Chancellor’s Fellow in Data Driven Innovation (2018-2023) and Bayes Innovation Fellow (2023-2024). He received his PhD 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 his PhD, he briefly worked as a Postdoctoral Research Associate in Chemical Species Tomography at The University of Edinburgh before he secured the lectuership.
His research interests focus on AI-powered sensing and imaging, machine learning, digital twins, soft sensors & electronics for robotics. His research has led to over 130 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 awardee of the prestigious European Research Council (ERC) Starting Grant.
Dr. Yang serves as 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 the regular reviewer for over 70 high-impact international journals (including Nature Communications, PNAS). He served as the track/session chair of several international conferences. He is the recipient of the 2024 IEEE J. Barry Oakes Advancement Award (For demonstrated exceptional expertise, innovation and leadership in the field of Instrumentation and Measurement), 2015 IEEE I&M Society Graduate Fellowship Award, and multiple Best Paper Awards. He is a Senior Member of IEEE, a Fellow of the International Society for Industrial Process Tomography (FISIPT), and a Fellow of the Higher Education Academy (FHEA).
Research group: UoE SMART Group www.yangresearchgroup.com
Press coverages:
- ERC StG: Dr Yunjie Yang awarded €1.5 million European Research Council Starting Grant | School of Engineering
- 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
(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 healthcare
- Soft robotics perception and control
- Machine learning for inverse problems
Specialities:
- Soft sensors
- Soft robotics
- Digital twins
- Tomographic imaging and applied machine learning
Further Information:
We welcome undergraduates, graduates, and postdocs who are interested in joining our group. Please feel free to contact us at anytime.