Artificial Intelligence and Machine Learning

Dr Matthew Danish

Research background

Matthew Danish is a Research Associate within the Networks and Operating Systems group of the Systems Research Group in the Department of Computer Science and Technology. He is interested in systems verification, sensor networks, machine learning, infrastructure and the built environment.

CDBB projects involved with

West Cambridge Digital Twin

CAR: creation and through-life management of built assets and infrastructure

The past 20 years have witnessed a transformation in digital technologies that touches every part of life in Great Britain. We now take completely for granted the ability to take digital photographs, access precise locations using satellite technologies, and access a world of information from the...

Aerial Swarm Robotics for Active Inspection of Bridges

Bridge collapses are expensive and often tragic events that need to be avoided by detecting faults early. This project aims to create a coordinated aerial swarm system to inspect the cracks in bridge structures, improving the monitoring coverage and efficiency.

Feasibility of an Operating System for Interspatial Networking in a Built Environment

Osmose is a project aimed at building a new operating system for situated environments that can handle the demands of thousands of sensors and actuators that need to run in a coordinated and highly reliable fashion.

Complex Systems Network

This network takes an interdisciplinary approach to understanding the state-of-the-art in use of modelling support for infrastructure planning decision making, both in industry and policy practice, and in the research community; needs of the practitioner community for research and innovation on...

Network for Ontologies (FOuNTAIN)

This research network aims to take a user-centric approach to interacting with information, from search and retrieval to browsing and exploration, and to consider the following three dimensions: (1) the lifecycle of information, from its creation, delivery, capture, preservation, management...

Vision Network

This research network contributes to developing the research agenda, enhancing the level of performance and digitisation for future smart cities and smart construction at citizen, portfolio, organisation and project levels. The objective of the Vision Network is to delineate the current augmented...

Machine Learning and AI in the Built Environment

This project improved the foundations for applying tried-and-tested machine learning (ML) approaches to the built environment. This mini project reduced the cost of creating and deploying ML systems by creating versatile and extendable Application programming interfaces (APIs), data management...

A digital twin prototype for journeys to work in Cambridge

How can digital twins address city-level problems such as transportation planning as work patterns change? This project represents an early trial to address the research gap on city-level digital twins. The pilot is focused on commutes, such as journeys to work, which are a key determinant of peak-...

Crowdsourcing Data in Mining Spatial Urban Activities

This project aims to understand how check-in data from social media is distributed around Cambridge and what kinds of spatial segmentation can be identified. It validated the social media data on urban segregation using observation and questionnaires. We conducted pilots at Cambridge in the UK and...