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.

People

Faculty

Ben Adcock

Mathematical Data Science, Numerical Analysis, Approximation Theory, Computational Harmonic Analysis

Cedric Chauve

Computational Genomics and Paleogenomics

Caroline Colijn

Canada 150 Research Chair in Mathematics for Evolution, Infection and Public Health

Nadish de Silva

Ailene MacPherson

Jessica Stockdale

Infectious disease modelling, statistics

Tamon Stephen

Operations Research

Paul Tupper

Infectious Disease Modelling and Estimation, Cognitive Science, Metric Spaces, Phylogenetics

Postdoctoral Fellows & Visitors

Elisha Are
Amy Langdon

PhD Graduate Students

Niloufar Abhari
Juan Cardenas
Omid Gheysar Gharamaleki
Fatih Karaoglanoglu
Aniket Mane
Sebastian Moraga
Nicola Mulberry
Baraa Orabi
Yexuan Song
Kurnia Susvitasari
Alice Yue

MSc Graduate Students

Piyush Agarwal
Saimon Islam

Recent Research

Publications:

B. Adcock, S. Brugiapaglia and C. G. Webster
Sparse Polynomial Approximation of High-Dimensional Functions
SIAM, 2022
www.sparse-hd-book.com

B. Adcock and S. Brugiapaglia
Is Monte Carlo a bad sampling strategy for learning smooth functions in high dimensions?
arXiv 2208.09045, 2022

B. Adcock, J. M. Cardenas and N. Dexter
CAS4DL: Christoffel Adaptive Sampling for function approximation via Deep Learning
arXiv:2208.12190, 2022

B. Adcock and M. Neyra-Nesterenko
Stable, accurate and efficient deep neural networks for inverse problems with analysis-sparse models
arXiv:2203.00804, 2022

B. Adcock, J. M. Cardenas and N. Dexter
An adaptive sampling and domain learning strategy for multivariate function approximation on unknown domains
arXiv 2202.00144, 2022

J. Barnes, M. R. Blair, R. C. Walshe and P. F. Tupper
LAG-1: A dynamic, integrative model of learning, attention, and gaze
PLOS One 17(3):e0259511, 2022

S. Brugiapaglia, M. Liu and P. Tupper
Invariance, encodings, and generalization: learning identity effects with neural networks
Neural Computation 34(8):1756-1789, 2022

C. Colijn, D. J. D. Earn, J. Dushoff, N. H. Ogden, M. Li, N. Knox, G. Van Domselaar, K. Franklin, Kristyn, G. Jolly and S. P. Otto
Genomic surveillance of SARS-CoV-2
Preprint, 2022

G. Ebrahimi, B. Orabi, M. Robinson, C. Chauve, R. Flannigan and F. Hach
Fast and accurate matching of cellular barcodes across short-reads and long-reads of single-cell RNA-seq experiments
iScience 25(7):104530, 2022

M. Hayati, L. Chindelevitch, D. Aanensen and C. Colijn
Deep clustering of bacterial tree images
Philosophical Transactions of the Royal Society B 377(1861):20210231, 2022

F. Karaoglanoglu, C. Chauve and F. Hach
Genion, an accurate tool to detect gene fusion from long transcriptomics reads
BMC Genomics 23(1):129, 2022

P. Liu, P. Biller, M. Gould and C. Colijn
Analyzing Phylogenetic Trees with a Tree Lattice Coordinate System and a Graph Polynomial
Systematic Biology (in press), 2022

J. Sielemann, K. Sielemann, B. Brejová, T. Vinař and C. Chauve
plASgraph - using graph neural networks to detect plasmid contigs from an assembly graph
bioRxiv 2022.05.24.493339, 2022.

B. Sobkowiak, K. Kamelian, J. Zlosnik, J. Tyson, A. G. da Silva, L. Hoang, N. Prystajecky and C. Colijn
Cov2clusters: genomic clustering of SARS-CoV-2 sequences
medRxiv 2022.03.10.22272213, 2022

A. Yue, C. Chauve, M. Libbrecht and R. R. Brinkman
Automated identification of maximal differential cell populations in flow cytometry data
Cytometry A 101(2):177-184, 2022

P. Tupper, K. W. Leung, Y. Wang, A. Jongman and J. A. Sereno
The contrast between clear and plain speaking style for Mandarin tones
The Journal of the Acoustical Society of America 150(6):4464-4473, 2021

Talks:

C. Colijn
Genomic epidemiology in SARS-CoV-2: new tools and challenges
Artificial Intelligence for Pandemics (AI4PAN) seminar, The University of Queensland, June 2022

B. Adcock
Deep learning for scientific computing: Two stories on the gap between theory and practice
Alan Turing Institute workshop on Interpretability, Safety, and Security in AI, December 2021

C. Colijn
Modelling and policy in the COVID-10 pandemic
Biostatistics-Biomedical Informatics Big Data (B3D) Seminar series, Harvard University, May 2021

B. Adcock
Deep learning for scientific computing: (closing) the gap between theory and practice
The AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences, April 2021

B. Adcock
The troublesome kernel: instabilities in deep learning for inverse problems
One World Mathematics of INformation, Data, and Signals (OW-MINDS) Seminar, June 2020

C. Colijn
Genomic epidemiology with TransPhylo: methods, applications and limitations
Phyloseminar.org seminar, January 2020

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

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

Recent Theses

Y. Song
Account for sampling bias in ancestral state reconstruction
MSc thesis, 2022

E. Haghshenas
Computational methods for analysis of single molecule sequencing data
PhD thesis, 2020

M. King-Roskamp
Global guarantees from local knowledge: Stable and robust recovery of sparse in levels vectors
MSc thesis, 2020

Recent Courses

Fall 2022:

APMA 920 – Numerical Linear Algebra
MATH 895 - Infectious Disease Modelling in Populations and Within Hosts

Spring 2022:

MATH 775 - Mathematical Topics in Data Science

Fall 2021:

APMA 923 - Numerical Methods in Continuous Optimization
MATH 709 - Numerical Linear Algebra and Optimization

Summer 2021:

APMA 940 - Mathematics of Data Science

Fall 2020:

APMA 920 – Numerical Linear Algebra

Fall 2019: