Mathematics and Data Research Group

Our Research

The modern world is awash with data.  Our ability to store, process, interpret and analyze data arising in real-world applications relies on foundational mathematics and mathematical tools.  The interaction between mathematics and data is a fruitful and active area of research. Not only does the analysis of data lead to new mathematical questions and problems, novel mathematical techniques - arising often from seemingly unconnected areas - lead to new and powerful tools in data science.

SFU has an active research group in contemporary applied mathematics and data science, encompassing a wide range of expertise and specialities.  Our mathematics research includes: foundations of data science, discrete mathematics, optimization, mathematical modelling, compressed sensing and sparse recovery, neural networks and deep learning. We work on applications in computational biology, epidemiology, evolution and genomics, imaging, cognitive science, and develop data science methods for computational science and engineering.

Our group has collaborations with the Departments of Statistics, Biology, Molecular Biology and Biochemistry, Linguistics, Psychology, the School of Computing Science, and with SFU’s Faculty of Health Sciences. We also collaborate with other researchers throughout the lower mainland, Canada and internationally.

The Department of Mathematics offers a selection of graduate and undergraduate courses in this area.  See below for a list.



Ben Adcock

Numerical Analysis, Applied and Computational Harmonic Analysis, Compressed Sensing

Leonid Chindelevitch

Bioinformatics, Computational Biology and Computational Epidemiology of Infectious Diseases

Cedric Chauve

Computational Genomics and Paleogenomics

Caroline Colijn

Canada 150 Research Chair.  "Modern" or maybe "statistical" applied math; applying discrete math and combinatorics, as well as stochastic processes, probability and biomathematics.

Zhaosong Lu

Operations Research

Marni Mishna

Analytic and Enumerative Combinatorics

Tamon Stephen

Operations Research

Paul Tupper

Stochastic Differential Equations, Cognitive Science

Postdoctoral Fellows and Visitors

  • Priscila Biller (Caroline Colijn)
  • Simone Brugiapaglia (Ben Adcock & Paul Tupper)
  • Nick Dexter (Ben Adcock)
  • Pedro Feijao (Cedric Chauve & Leonid Chindelevitch)
  • Pengyu Liu (Caroline Colijn)
  • Jessica Stockdale (Caroline Colijn)

PhD Students

  • Juan Cardenas
  • Maryam Hayati
  • Fatih Karaoglanoglu
  • Aniket Mane
  • Nicola Mulberry
  • Baraa Orabi
  • Nafiseh Sedaghat
  • Yi Sui
  • Kurnia Susvitasari
  • Alice Yue
  • Hooman Zabeti

MSc Students

  • Julius Booth
  • Conor Doyle
  • Jie Jian
  • Mohsen Katebi
  • Matthew King-Roskamp
  • Jianwei Li
  • Reza Miraskarshahi
  • Thomas Retzloff
  • Fatemeh Shafie
  • Alex Sweeten
  • Qinghong Xu

Recent Research


S. Brugiapaglia & B. Adcock. Robustness to unknown error in sparse regularization, IEEE Transactions on Information Theory 64(10):6638-6661, 2018. []

Baraa Orabi, Emre Erhan, Brian McConeghy, Stanislav V Volik, Stephane Le Bihan, Robert Bell, Colin C Collins, Cedric Chauve & Faraz Hach. Alignment-free Clustering of UMI Tagged DNA Molecules, Bioinformatics, in press, 2018. []

Pedro Feijao, H. Yao, Dan Fornika, Jennifer Gardy, Will Hsiao, Cedric Chauve & Leonid Chindelevitch. MentaLiST - A fast MLST caller for large MLST schemes, Microbial Genomics 2018 4. []

B. Adcock, A. C. Hansen, C. Poon & B. Roman. Breaking the coherence barrier: a new theory for compressed sensing, Forum of Mathematics, Sigma 5, 2017. []

S. Melczer & M. Mishna. Enumerating lattice paths with symmetries through multivariate diagonals Algorithmica, 75(4): 782-811, 2016.

P. Tupper, J. Jian, K. Leung & Y. Wang. Game Theoretic Models of Clear versus Plain Speech, Proceedings of the 30th Annual Meeting of the Cognitive Science Society. 1133-1138, 2018. []

G. Jenkins & P. Tupper. A Dynamic Neural Gradient Model of Two-Item and Intermediate Transposition, Neural Computation 30(7):1961-1982, 2018. []

D. Bryant, A. Nies & P. Tupper. A Universal Separable Diversity, Analysis and Geometry of Metric Spaces. 5:138-151, 2017. []


B. Adcock. Compressed sensing and high-dimensional approximation: progress and challenges, iTWIST 2018, Marseille, France, November 2018.

M. Mishna. The Analytic Combinatorics of Lattice Paths Analysis of Algorithms plenary talk, Princeton, May 2017.

T. Stephen. Two pairs of Boolean functions in biology, Fields Institute Industrial Optimization Seminar, December 2016. []

P. Tupper. Fitting a Stochastic Model to Eye Movement Time Series in a Categorization Task, Distributed Data for Dynamics and Manifolds, BIRS Oaxaca, Sept 5th, 2017.

Recent Courses

Fall 2018:

MATH 895 – Reading Course: Machine Learning
APMA 920 – Numerical Linear Algebra
MATH 495/795 – Methods and Models for Data
CMPT 405/705 - Design and Analysis of Algorithms

Summer 2018:

MATH 496/796 – Mathematics of Data Science
MATH 894 – Reading Course: Compressed Sensing, Structure and Imaging

Spring 2018:

MATH 895 – Reading Course: Applications of Convex Optimization in Machine Learning

Fall 2017:

MACM 409/MATH 709 – Numerical Linear Algebra and Optimization

2016/2017 or Earlier:

MATH 895 – Reading Course: An Introduction to Compressed Sensing
MATH 821 - Combinatorics


Affiliated Groups

If you would like to receive email updates from our group, please contact Ben Adcock


If you are a current SFU Mathematics Postdoctoral Fellow or Graduate Student in the Mathematics and Data Research Group,
and your name is missing from the above lists, please send an email to Casey Bell.