Numerical Analysis, Applied and Computational Harmonic Analysis, Compressed Sensing
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Mathematics and Data Research Group
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.
- Juan Cardenas
- Maryam Hayati
- Fatih Karaoglanoglu
- Aniket Mane
- Nicola Mulberry
- Baraa Orabi
- Nafiseh Sedaghat
- Yi Sui
- Kurnia Susvitasari
- Alice Yue
- Hooman Zabeti
- Julius Booth
- Conor Doyle
- Jie Jian
- Mohsen Katebi
- Matthew King-Roskamp
- Jianwei Li
- Reza Miraskarshahi
- Thomas Retzloff
- Fatemeh Shafie
- Alex Sweeten
- Qinghong Xu
S. Brugiapaglia & B. Adcock. Robustness to unknown error in sparse regularization, IEEE Transactions on Information Theory 64(10):6638-6661, 2018. [https://ieeexplore.ieee.org/abstract/document/8242684/]
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. [https://doi.org/10.1093/bioinformatics/bty888]
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. [ https://dx.doi.org/10.1099/mgen.0.000146]
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. [https://www.cambridge.org/core/journals/forum-of-mathematics-sigma/article/breaking-the-coherence-barrier-a-new-theory-for-compressed-sensing/455E5F506912B78E9647CD7A7488530B]
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. [https://mindmodeling.org/cogsci2018/papers/0224/0224.pdf]
G. Jenkins & P. Tupper. A Dynamic Neural Gradient Model of Two-Item and Intermediate Transposition, Neural Computation 30(7):1961-1982, 2018. [https://www.mitpressjournals.org/doi/abs/10.1162/neco_a_01093]
D. Bryant, A. Nies & P. Tupper. A Universal Separable Diversity, Analysis and Geometry of Metric Spaces. 5:138-151, 2017. [https://www.degruyter.com/downloadpdf/j/agms.2017.5.issue-1/agms-2017-0008/agms-2017-0008.pdf]
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. [http://www.fields.utoronto.ca/activities/16-17/optimization-seminar]
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.
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
MATH 496/796 – Mathematics of Data Science
MATH 894 – Reading Course: Compressed Sensing, Structure and Imaging
MATH 895 – Reading Course: Applications of Convex Optimization in Machine Learning
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
- Foundations of Computational Mathematics (FoCM 2020) June 2020
- Minicourse on High-dimensional data in Uncertainty Quantification of PDEs