Data Champions Search

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

Specialising in AI-driven diagnostics, precision medicine, and HealthTech innovation

I am a neuroscientist studying human speech perception with the aim of advancing assistive speech (neuro)technologies and AI speech recognition systems. My background is in cognitive neuroscience and medicine.

I am an expert in human neurophysiology methods (electro- and magnetoencephalography, EEG/MEG) combined with machine learning and artificial neural network modelling (deep learning).

Since the beginning of my PhD, I’ve been passionate about reproducible and open science practices such as data and code sharing, and good coding practices including version control. In my department I am leading the adoption of the standardised Brain Imaging Data Structure (BIDS) for storing and sharing human neurophysiology (MEG/EEG) data.