Find a Data Champion

I am a post-doctoral physicist working in the Department of Radiology, with a background in programming and a particular interest in medical image analysis and software design. I am currently involved in setting up and maintenance of processes for data storage, sharing and archiving for the department - with particular emphasis on medical research image data.
 

I am a Research Associate in the Department of Biochemistry, where I specialise in electron cryo-microscopy (cryo-EM). I use cryo-EM data for the reconstruction and computational modelling of antibiotic transport by bacterial tripartite efflux pumps and related membrane proteins.

I work in cognitive neuroscience and neuroimaging, with a strong focus on research data management, reproducible analysis, and open science practices. Before training in cognitive neuroscience, I was educated in computer science and trained as a systems analyst, which continues to shape how I approach research data, workflows, and computational methods. In my current work, I focus on brain imaging data, including data organisation in BIDS (Brain Imaging Data Structure), preprocessing and analysis pipelines, and the development of practical training materials to support researchers in using modern computational tools and adopting good data practices.

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