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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.

I received my PhD in cognitive neuroscience from Basque Center on Cognition, Brain and Language (BCBL). I am currently a Research Associate at the Department of Psychology, University of Cambridge. My interests include data processing and analysis methods for neuroimaging techniques employed in developmental research (fNIRS & EEG). I am interested in the application of these methods for investigating functional connectivity and cognitive processes such as social cognition and language acquisition in infant populations.

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

Additional areas of expertise:

Plant science, Quantitative genetics.