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D Higham

Professor D Higham

Professor D Higham

Research Fellow

Interests and expertise (Subject groups)

Grants awarded

Scheme: Leverhulme Trust Senior Research Fellowship

Organisation: University of Strathclyde

Dates: Sep 2013-Sep 2014

Value: £39,437

Summary: Many modern applications produce patterns that change over time. Important examples arise in mobile telecommunications, on-line trading, smart-metering and on-line social networking. Information such as ‘who called who’, ‘who Tweeted who’, ‘who facebooked who’, and ‘people who bought his book also bought…’ These emerging, data-rich disciplines generate large, high-frequency interaction sequences that demand new computational tools. It is possible to extend standard network concepts, such as paths, walks and geodesics, but any new ideas must respect the arrow of time. Two key observations are that (a) there is an asymmetry in the spread of information around the network (if A meets B and then B meets C, a message may pass A to C, but not vice versa), and (b) using simple aggregate or snapshots to summarize the overall connectivity fails to respect the asymmetry and systematically overestimates the spread of information. This calls for new ideas in modelling and analysing real-time interactions. My work has involved designing new computer algorithms that extract key information this type of evolving interaction setting. I have collaborated with colleagues in social media/digital advertising in order to study Twitter networks, and also with colleagues in experimental neuroscience in order to study interaction networks involving brain regions. The new algorithms allows us address questions such as (a) is the network operating normally? (b) which parts are most central/vulnerable/peripheral? (c) how will the interactions evolve? These questions are of great interest in a range of application areas, including smart energy metering, traffic flow, advertising and computer security. The research has led to new mathematical ideas, computer algorithms, and results where new insights have been extracted from Twitter data and from brain imaging studies.

Stochastic Modelling and Simulation for Interaction Networks

Scheme: Wolfson Research Merit Awards

Organisation: University of Strathclyde

Dates: Aug 2012-Jul 2017

Value: £50,000

Summary: My work ranges from technical, rigorous analysis of computational methods on nonlinear problems to practical modelling and algorithm development on real data sets in collaboration with experimental scientists and non-academic colleagues. My most technical research is conducted in collaboration with colleagues in stochastic analysis. We like to prove rigorous results about the accuracy and complexity of computational tools. Where necessary, we also developed improved tools. I have recently focused on methods that are widely used in biochemistry; for example, in systems that model the way that genes regulate proteins in the cell. In the past couple of years I have developed some new collaborations with colleagues in the Future Cities arena. Here the availability of new data sets (crowd movement, transport, air quality, energy useage, CCTV, crime,...) is generating novel research challenges that can dramatically improve well-being and security in urban environments. Sometimes the scale and variety of data makes it necessary to completely re-think the existing methodologies. I am also continuing a long-term collaboration with Bloom (Leeds), a marketing agency who use cutting edge analytics to study social media activity on behalf of their clients. Bloom provide my team with research challenges and cutting edge data sets, and we provide advice and rapid access to our (public domain) research results. Further, by giving us access to their social media experts, Bloom allow us validate our research results in a practical setting. An exciting promotional video is available at

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