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AdaptiveCity / SmartCambridge / CDBB Digital Twin



Justas Brazauskas

Justas Brazauskas currently works as a Research Assistant focusing on the Internet of Things, Human-Computer Interaction and Smart Spaces. His previous research experience includes two summers spent at York Cross-disciplinary Centre for Systems Analysis (YCCSA) and UCL Interaction Center (UCLIC). He graduated in 2019 from UCL’s BASc degree with first-class honours majoring in Sciences and Engineering.

Rob Bricheno

Rob Bricheno is a Senior Network Systems Specialist within University Information Services of the University of Cambridge. Among other responsibilities, he is the project lead for the University implementation of network capabilities supporting research in IoT, smart buildings and smart cities.

Matthew Danish

Matthew Danish is a Research Associate in the Systems Research Group at the Department for Computer Science and Technology in the University of Cambridge. Prior to joining the SRG he was a member of the Digital Technology Group. He received his BS in Logic and Computation from Carnegie-Mellon University in 2004 and his PhD in Computer Science from Boston University in 2015. His past research was focused on software correctness verification and program analysis, including a prior project to find bugs in scientific software written in Fortran over several generations. His current research is to build intelligent sensors and an architecture for real-time complex event processing and analysis for digital twins.

Ian Lewis

Ian Lewis is Director of the Adaptive Cities Programme in the Computer Laboratory. His research interests are the real-time collection and analysis of urban sensor data and the evolution of the intelligent Future City. Research themes include sensor networks, intelligent sensor design, real-time processing, prediction, planning and privacy. His PhD, from the Cambridge Computer Lab, was concerned with robust distributed parallel AI.

Dr Jorge Merino

Dr Jorge Merino is a research associate at the Institute for Manufacturing of the University of Cambridge specialised in data quality evaluation and certification, and data management. He has experience working with international organisations from public and private sectors including insurance, banks, IT, certification authorities, and other universities. He is a Certified Information System Auditor by ISACA as well.

Previously, Dr J. Merino worked as a research fellow and assistant professor at the Institute of Information Systems and Technology in the University of Castilla-La Mancha in Spain. During that time, he participated in several funded research projects, was part of the organisation committee of various international conferences, and registered an industrial property. He was part of the secretariat of the AEN/CTN 71/SC 09 on Big data in AENOR International (Spanish Association for Normalisation and Certification). He also actively participated in international committees for standardisation of ISO/IEC JTC1, such as ISO/IEC JTC1 WG9 on Big Data, ISO/TC 184 SC4 WG13 on Industrial Data. He gained his Ph.D. in Advance Information Technologies from the University of Castilla-La Mancha in 2017.

Research Interests:

Data governance, and management, and value, Data quality analysis and certification, Big Data, and decision making.


Richard Mortier or mort is Professor in Computing & Human-Data Interaction, and President of Christ’s College. He returned to the Computer Laboratory in 2015, having spent time at Sprint ATL, Microsoft Research Cambridge, Vipadia Limited and Horizon Digital Economy Research at the University of Nottingham. He is interested in the intersection of systems and HCI, and the ways that design of our computing infrastructure constrains and enables our interactions.

Dr Nicola Moretti

Dr Nicola Moretti is a research associate at the Engineering Department of the University of Cambridge. He works with the Asset Management Group at the Institute for Manufacturing (IfM) and is part of the Centre for Digital Built Britain (CDBB). His research focuses on developing and demonstrating effective approaches and tools for integrating building and infrastructure data from different sources to support asset management. This involves identifying data requirements for asset management, defining asset information models and integrating such data with the BIM/GIS models. Nicola completed the PhD at the Architecture, Built Environment and Construction Engineering Department at Politecnico di Milano, Italy. The doctoral research aimed at reengineering asset management business processes, through new information management capabilities offered by digital tools and approaches. During the PhD he also focused on improved information management for use phase of the assets and GIS/BIM integration for Facility and Asset Management.

Vadim Safronov

Vadim Safronov is studying for a PhD in the Systems Research Group at the University of Cambridge Dept. of Computer Science and Technology. His interests are the development of advanced heterogenous networks suitable for intelligent sensor information flow in future buildings and urban regions.

Rohit Verma

Rohit Verma is a Research Associate in the Systems Research Group at the Department of Computer Science and Technology, University of Cambridge. Prior to joining University of Cambridge, he was a PhD student in the Complex Network Research Group at the Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, India (2016-2020). His primary area of research has been in the field of sensor data collection and analysis obtained from multi-modal sources. Currently, he is focused on the real-time aspect of data being generated by sensors deployed in the built environment. The idea being not only to store and learn from the data that these citywide sensors generate but to make crucial decisions with minimum latency.

Xiang Xie

Dr Xiang Xie is a Research Associate in DIAL, who is conducting researches related to the Digital Twins project. He obtained his PhD degree from Zhejiang University in 2018. During his PhD study, he focused on providing an accurate assessment, awareness and identification for anomalies in water distribution systems using data-driven monitoring techniques. Specifically, with the help of affordable sensing and communication equipment, system-wide smart metering is implemented to collect heterogeneous data from different sources and operational information is mined using machine learning techniques to intelligentize the urban water supply infrastructure. Prior to this, he received his Bachelor’s degree in Control Engineering from Huazhong University of Science and Technology in 2013. His research interests include: machine learning, data mining and the implementation of IoT technique in condition monitoring.