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

I provide Bioinformatic support for a few research groups in the Cambridge Stem Cell Institute. For the most part I deal with the analysis of single cell sequencing data, storage management of such data and the deployment of data processing pipelines into HPC environments.

I primarily work with large-scale language datasets, using methods from quantitative and computational social sciences, as well as the digital humanities. I also teach associated research methods, like statistics, data wrangling, and visualization. 

Outside my research, I am an active science communicator, primarily via two websites that I author: Effectiviology (https://effectiviology.com/), where I write about psychology and philosophy that have practical applications, and Solving Procrastination (https://solvingprocrastination.com/), where I write about what procrastination is, why we do it, and how to overcome it. 

As a Data Champion, I focus on the use of quantitative and computational methods for working with data (e.g., for data wrangling and statistical analyses). I also deal with associated topics, like maximizing the impact of research through open research and science communication.