Ben Adcock

Ben Adcock is a Professor of Mathematics at Simon Fraser University (SFU), Department of Mathematics, Faculty of Science. He holds a PhD from the University of Cambridge (2010) and has held postdoctoral fellowships at SFU and Purdue University. His research focuses on mathematical data science, numerical analysis, approximation theory, compressed sensing, and machine learning applications in scientific computing.

Education: PhD in Mathematics (2010, University of Cambridge), MMath (2006), BA (2005). Prior roles include Assistant Professor at Purdue University (2012–2014) before returning to SFU as faculty. Awards include the CAIMS/PIMS Early Career Award (2017), Sloan Fellowship (2015), and Leslie Fox Prize (2011).

Research interests span sparse regularization techniques, compressed sensing theory, deep neural networks for scientific computing, and high-dimensional function approximation. Recent work emphasizes optimal sampling strategies, stability in deep learning for image reconstruction, and Hilbert-valued function approximation.

Publications reflect contributions to compressive imaging, function approximation, and algorithmic stability. Notable books include Sparse Polynomial Approximation of High-Dimensional Functions (SIAM, 2022) and Compressive Imaging: Structure, Sampling, Learning (CUP, 2021). Supervised students include Juan M. Cardenas (2021 SIAM CSE poster award winner) and Yi Sui. He leads editorial roles at SIAM journals and co-organized the 2020 Foundations of Computational Mathematics conference.