Professor Marko Bacic is an Engineering Associate Fellow in Control Systems and Gas Turbine Functionality at Rolls-Royce and also holds a University Research Lectureship at Oxford since July 2012. He completed his degree in Engineering and Computing Science (2001) and his D. Phil in Model Predictive Control (2004) both from the University of Oxford.
Kang Li is Professor of Smart Energy Systems at the University of Leeds where he is the Director of Institute of Communication and Power Networks. He has over thirty years of research experience working on a wide range of control engineering applications in energy, transport and manufacturing. Prior to joining the University of Leeds, he was a Professor of Intelligent Systems and Control at the Queen’s University of Belfast from 2011-2018. He received his PhD in Control Theory and Applications from Shanghai Jiaotong University in 1995, and had various research experiences at Shanghai Jiaotong University, Delft University of Technology, and the Queen’s University Belfast before he started his academic career in the UK in 2002.
A control engineer by training, his work spans many research topics, covering nonlinear system modelling and identification, intelligent control, and AI and machine learning, but his greatest interest is on the development of holistic sensing, modelling, control, and optimization techniques to support low carbon transition of different sectors. His work on energy management and control of energy intensive manufacturing processes funded by EPSRC has led to the development of a minimal-invasive edge-cloud based energy monitoring and analytic platform (Point Energy Technology) which has been successfully used in polymer processing and food processing to support process monitoring, control and energy management, winning 2015 Institute of Measurement and Control ICI prize for the best application paper, 2016 Northern Ireland Science Park INVENT award, the finalist of 2016 Sustainable Energy Awards by Ireland Sustainable Energy Authority, and 2015 Outstanding Award for Knowledge Transfer Partnership. Kang has made systematic contributions to battery energy management, covering electro-thermal modelling of batteries for State of Charge estimation, model predictive control framework for battery charging and discharging considering multiple objectives and constraints, machine-learning based state of health estimation, and development of FBG optic fibre sensing system and associated AI orchestrated data driven analytic platform for battery management. These research works have been highly cited and adopted in engineering applications either through innovation demonstration projects or through research students working in the industry after graduation. Funded by Ofgem, EPSRC and industry, he is currently leading the development of a new microgrid technology, namely railway energy hubs to accelerate railway decarbonization, with the first live demonstrator to be built in Scotland, with the purpose to support the roll-out of the energy hub technology to as many of 2500 railway stations across UK, providing services of substantial scale to both railway and power grids.
Raul Arribas is a GNC (Guidance, Navigation, and Control) Engineer with professional experience at leading aerospace organizations, including Airbus Space and Rolls-Royce. His work spans spacecraft mission design, algorithm development, and system validation, showcased through his impactful contributions to high-profile projects such as the ESA/NASA-JPL Mars Sample Fetch Rover and more recently on European Space Agency’s FORUM mission.
An Imperial College London Master’s graduate with First-Class Honours, Raul is also a member of the Royal Aeronautical Society, Raul combines a robust technical foundation with problem solving in engineering. Beyond current engineering projects, Raul is passionate about industry-academia partnerships, organising several collaborations with Oxford, and Imperial College London.
Raul brings a solid technical foundation, creative problem-solving, and a positive mindset to his work.
7th February 2025, 10:00 – 12.30
29 January 2025, 14:00 – 16:55.
Yuanbo Nie is a Lecturer in Control and Systems Engineering at the University of Sheffield. He holds an MSc in Aerospace Engineering from Delft University of Technology, an MSc in Advanced Computational Methods from Imperial College London, and a Ph.D. in Aeronautics from Imperial College London, awarded in 2021.
Asiya Khan is an Associate Professor of Multimedia Communication & Intelligent Control at the University of Plymouth and recently seconded to DSIT Science and Technology Fellowship (AI Risk Adviser). She received her BEng (Hons) in Electrical & Electronic Engineering from University of Glasgow, MSc in Communications, Control & Digital Signal Processing from Strathclyde University and PhD from University of Plymouth. She has contributed to pedagogic scholarship and research especially in addressing under-represented groups in engineering. She has received funding from RAEng under the ‘Diversity Impact Programme’ supporting neurodiverse and disabled engineering students and towards developing engineering education for social inclusion. She has received funding from the British Council, EPSRC and EU ERDF.
Daniel Auger is Professor of Electrification, Modelling and Control at Cranfield University, where he is a member of the Advanced Vehicle Engineering Centre. He completed his MEng(Hons) and his PhD at the University of Cambridge. Before taking up an academic appointment, he worked for BAE Systems and MathWorks Consulting Services.
Gianmario Rinaldi is a Lecturer in Mechanical Engineering (Education & Research) at the Department of Engineering in the Faculty of Environment, Science and Economy. He is also the Faculty Advisor and the Academic Lead of the Formula Student Project. His research expertise is in control, estimation, and optimisation methods, with application to multienergy systems, power systems, and clean/autonomous vehicles.
ACE Network Forum 2025, the inaugural flagship event for members of the Automatic Control Engineering (ACE) Network.