Data Champions Search

Alexandra Gushurst-Moore is the Research & Impact Coordinator of the Fitzwilliam Museum and Coordinator of Cambridge Visual Culture. Her doctoral thesis, entitled “The Making of Modern Fantasy in the Visual Arts of England, ca. 1850–1920”, was supervised by Elizabeth Prettejohn at the University of York. She holds an MA(Hons) from the University of Edinburgh and an MSt from the University of Oxford. Beyond her research, she has professional experience of and interest in British educational policy, the strategy and operation of HEIs and the facilitation of inclusive and supportive research cultures.

Ali is Senior Data Scientist and public-health/transport modeller in the Public Health Modelling (PHM) group at the MRC-Epidemiology Unit, University of Cambridge. He is interested in active travel, specifically cycling research. 

Coming from a Computer Science background, with a focus on modelling in his PhD, for over a decade, he has been doing data-driven research and development. This includes Exploratory Data Analysis (EDA), Statistical Modelling (or Machine Learning), Interactive Visualization, all with a focus on collaborative, transparent and reproducible research. In addition, he leads in project management and managing, training, guiding, and teaching of junior staff in his Unit. His work can be seen on GitHub at https://github.com/usr110.

Aurora is a PhD student at the University of Cambridge. Prior to earning her MPhil degree from Cambridge with Distinction and Best Dissertation Award, she majored in Spanish and minored in Psychology with a First and Best Dissertation Award. Her research lies at the intersection of applied linguistics, psychology, and sociology. In addition to research training in areas such as experimental design, she is particularly interested in digital scholarship and open research.

Dequn Teng (he/him) is a third-year Ph.D. candidate in Engineering at the University of Cambridge’s Institute for Manufacturing (IfM), Centre for Technology Management. His doctoral research examines human–algorithm interactions' effects on analytical creativity in algorithmic trading, funded by Cambridge Trust. His long-term research identity focuses on emerging algorithmic innovations' effects on strategic value creation, bridging insights from computer engineering, technology management, and organizational strategy. He also serves as a Research Assistant in the International Business and Strategy Group at the CJBS, where he contributes to research on regenerative innovations and strategies.

Prior to his Ph.D., Dequn completed an MPhil in Engineering (IfM, University of Cambridge) under Prof. Veronica Martinez, focusing on supply chain risk modeling with machine learning–enabled blockchain systems. He earned a first-class honors degree in Computer Science and Electrical Engineering from the University of Liverpool (2+2 routine) and further enriched his expertise as a visiting student at Stanford University.

Beyond academia, Dequn has gained industry experience through roles at Deutsche Bank and Unilever, as well as through venture creation activities at the Entrepreneurship Centre at CJBS. He is also the Vice President of the Cambridge Algorithmic Trading Society (CUATS), fostering interdisciplinary collaboration in algorithmic trading research and practice.

As an educator, Dequn supervise industrial engineering, strategic management, and marketing, and leads coding and analytics sessions—including Brainteaser and LeetCode sessions—within the CUATS, cultivating interactive learning experiences at the intersection of technology, strategy, and innovation.

Publications