Electronics and Electrical Engineering

• Google Scholar

Dr. Yunjie Yang is a Senior Lecturer (Associate Professor) and Deputy Director of Research at the School of Engineering, 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. In 2018, he briefly worked as a Postdoctoral Research Associate in Chemical Species Tomography at the University of Edinburgh before securing the lectureship.

His research interests focus on AI-powered sensing and imaging, soft sensors & electronics, robotics, and embodied/physical AI. His research takes a highly interdisciplinary approach, integrating Sensors, Materials, AI, Tomography, and Robotics (SMART) to advance autonomous physical intelligence. It has led to over 150 peer-reviewed journal and international conference publications, many of which were published in high-impact journals such as Nature/Cell portfolio journals (e.g., Nature Machine Intelligence), IEEE TNNLS, TMI, TIE, and TII. 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 (2024-2029) and ERC Proof of Concept Grant (2026-2027).

Dr. Yang serves as the Associate Editor of IEEE Transactions on Instrumentation and Measurement, IEEE Sensors Reviews, the Editorial Board Member of Scientific Reports, and a regular reviewer for over 70 high-impact international journals (including Nature, Nature Machine Intelligence, Nature Communications, PNAS, TRO, TMI, TIE, TII, TPAMI, AM). He served as the track/session chair of several international conferences and was frequently invited for keynotes/seminars. 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), the IEEE I&M Society Distinguished Lecturer (2027-2029), 2015 IEEE I&M Society Graduate Fellowship Award, and multiple Best Paper/Poster Awards. He is a Senior Member of IEEE, a Fellow of the Young Academy of Europe (FYAE), a Fellow of the International Society for Industrial Process Tomography (FISIPT), and a Fellow of the Higher Education Academy (FHEA).

More information about the Yang Research Group: UoE SMART Group www.yangresearchgroup.com

Press coverage:

• UoE: https://www.ed.ac.uk/news/gloves-to-help-robots-get-to-grips-with-human-tasks
• EI: https://edinburgh-innovations.ed.ac.uk/news/sensor-gloves-could-help-robots-get-to-grips-with-human-tasks
• Channel 4: https://www.channel4.com/news/uk-scientists-finally-granted-eu-funding-post-brexit
• 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)

• 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

• Senior Member of IEEE
• Senior Member and Distinguished Lecturer (2027-2029) of IEEE I&M Society
• Member of IET
• Fellow of ISIPT
• Fellow of the Young Academy of Europe (FYAE)
• Fellow of the Higher Education Academy (FHEA)

• Digital System Laboratory 3 (ELEE09035)
• Signals and Communication Systems 3 (ELEE09027)
• Electrical Engineering 1 Tutorial (ELEE08001)
• Supervision of PhD, MSc, MEng and BEng projects

• Soft sensors & electronics
• Robotic perception
• Soft robotics
• Physical and embodied intelligence
• Tomographic imaging and applied machine learning

We welcome undergraduates, graduates, and postdocs who are interested in joining our group. Please feel free to contact me.

Cristian Sestito is a Research Fellow at the Centre for Electronics Frontiers, University of Edinburgh, UK. Specifically, Cristian is part of the AI Hub for Productive Research and Innovation in eLectronics (APRIL). He earned his BSc (2016) and MSc (2019) in Electronic Engineering, followed by a PhD in Information and Communication Technologies (2023), from the University of Calabria, Italy. His PhD focused on the design and FPGA implementation of convolutional neural networks, with particular emphasis on dilated and transposed convolutions. During 2021–2022, he was a Visiting Scholar at Heriot-Watt University, Edinburgh, UK where he worked on neural network compression techniques. His current research interests include digital AI accelerator design, software simulators for neuromorphic AI, and AI-assisted electronic design automation frameworks.

• PhD in Information and Communication Technologies, University of Calabria, Italy.
• MSc In Electronic Engineering, University of Calabria, Italy.
• BSc in Electronic Engineering, University of Calabria, Italy.

• IEEE Member.

• Hardware acceleration of Convolutional Neural Networks.
• Hardware acceleration of systolic arrays.
• AI-assisted electronic design automation.
• Software modelling of neuromorphic AI accelerators.

• FPGA design.
• Hardware/software co-design methodologies.
• Digital ASIC design.
• LLM-assisted electronic design automation frameworks.

• During his time in the APRIL AI Hub, Cristian is leading R&D activities within Pillar 3: Circuits and Systems Design Automation. His current focus is LaMDA (Language Model-Assisted Electronic Design Automation Framework).

An Electrical and Mechanical Engineer by training, Camilla now teaches about sustainable energy and researches the environmental impacts of energy. Currently this mostly involves developing better tools and techniques to analyse the climate change impact or carbon footprint of changes to the electricity system: whether new renewable energy developments, new transmission lines or even new market operations.

Camilla is a Co-Investigator in the EnergyREV project, leading the work package on developing a Multi-Criteria Assessment tool for Smart Local Energy Systems. She is also the Deputy Co-ordinator on the NEWEST-CCUS project, and is leading a multi-disciplinary international team on the specific task of robustly quantifying the atmospheric-carbon-reduction-potential of waste-to-energy plants with carbon capture and storage.

Camilla is also a keen advocate for diversity in Engineering. She is the founding chair of the School's Molly Fergusson Initiative, and was named one of the Top 50 Women in Engineering - Sustainability in 2020.

Her pronouns are she/her.

• PhD Carbon and Energy Payback of Variable Renewable Generation, University of Edinburgh, 2014
• MEng(Hons) Electrical and Mechanical Engineering, University of Edinburgh, 2004

• Member of the Institution of Engineering and Technology, MIET
• Winner Top 50 Women in Engineering 2020 - Sustainability

• MSc Sustainable Energy Systems - Dissertation supervision
• Technologies for Sustainable Energy - Course-organiser and lecturer

• Life Cycle Assessment (LCA) and carbon footprinting
• Marginal carbon analysis
• Renewable resource assessment
• Daylighting analysis

David holds a Bachelor’s degree in Electronics and Telecommunications Engineering from ESPOL Polytechnic University, Ecuador, and a Master’s degree in Engineering from the University of Applied Sciences and Arts of Southern Switzerland. From 2016 to 2022, he served as a full-time lecturer and researcher at ESPOL Polytechnic University. He is currently pursuing a PhD at the Advanced Care Research Centre, University of Edinburgh, Scotland, UK.David has extensive experience in the design of electronic systems for health monitoring systems. He has contributed to projects focused on gait analysis in Parkinson's disease, impedance spectroscopy for pulmonary oedema detection, and transcutaneous tibial nerve stimulation for the treatment of overactive bladder. His research interests include biomedical engineering applications and the adoption of technology, with a particular emphasis on older adults.

• B.Sc. in Electronics and Telecommunications Engineering (ESPOL Polytechnic University, Ecuador)
• M.Sc. in Engineering (SUPSI, Switzerland)

• IEEE

Tutor and Demostrator :

• Analogue Electronics (Project) 4 /Analogue VLSI Lab
• Sensors and Instrumentation
• Software and Embedded Systems Laboratory 2
• Applications of Sensor and Imaging Systems
• Social and Ethical Aspects of Medicine

• Biomedical engineering
• Advanced care for older adults

• Mixed-signal electronics
• Wearable devices