# Simon Fraser UniversityEngaging the World

## Department of Mathematics

### Summer 2018 Project Descriptions

Below are brief descriptions of research projects in mathematics from faculty members who wish to supervise qualified undergraduate students.

Unless otherwise specified, each project is available to one student only.

#### Please be sure to refer to the appropriate page for full details on how to apply -   NSERC USRA Instructions  or  VPR USRA Instructions

Supervisor: Drs Nathan Ilten & Mattia Talpo

PROJECT: Is the toric boundary Cohen-Macaulay?

A toric variety is an algebraic variety XΣ obtained from a combinatorial object Σ, a collection of compatible polyhedral cones in a vector space R n . These objects are widely studied in algebraic geometry, especially because several geometric aspects about these spaces can be “explicitly computed” via the combinatorial properties of Σ. Every such variety contains (C ∗ ) n as an open subset, and the complement ∆Σ = XΣ \ (C ∗ ) n is called the toric boundary. Cohen-Macaulayness is a property of algebraic varieties that relaxes smoothness. The intuitive idea is that every point of the variety can be described by using (dim X)-many “nice” equations. The Cohen-Macaulay property is important for a number of reasons: for example, it has nice implications for computations in local cohomology and duality theory, and several classes of useful singular varieties are Cohen-Macaulay. Even though very few of them are smooth, every toric variety is Cohen-Macaulay. The goal of this project is to understand whether the toric boundary is also always CohenMacaulay. This is important for the study of ideally smooth logarithmic schemes, that are some kind of spaces locally modelled on the boundary of a toric variety. The student working on this project will become familiar with several ideas from commutative algebra and algebraic geometry. The computer program Macaulay2 (or a similar computer algebra program) will be useful to experiment with the problem, before trying to approach it from a theoretical point of view.

Requirements: Applicants should have background knowledge in abstract algebra (e.g. at least MATH 340, MATH 440/441 preferable). If you are interested in applying, please contact Dr. Ilten at nilten@sfu.ca.

Supervisor: Dr Nathan Ilten

Email: nilten@sfu.ca

PROJECT: Tangent cones in truncated power series rings

Given an ideal I ⊂ C[x1, . . . , xn], its tangent cone is the ideal generated by inf for all f ∈ I, where inf is the lowest degree part of f. Seen geometrically, the tangent cone cuts out limits of secant lines between 0 and p, as p approaches 0 in the variety V (I) defined by the ideal I — hence the name. There are essentially two algorithms (due to Mora and Lazard) which can be used to calculated the tangent cone of an ideal. They can be seen as relatives of Buchberger’s algorithm, which calculates a Gr¨obner basis for an ideal. The key difference is that the “term order” underlying the tangent cone is not well-ordered. The goal of this project is to adapt the known algorithms to quickly calculate the tangent cone up to a fixed degree d for ideals of the form I + hx1, . . . , xni d . This has important applications in deformation theory and the lifting of syzygies. In addition to theoretical work, the student will be expected to implement their algorithm in the computer algebra system Macaulay2. In carrying out this project, the student will be exposed to a variety of ideas from algebraic geometry and commutative algebra..

Supervisor: Dr Nathan Ilten

Email: nilten@sfu.ca

PROJECT: Algorithms for Cayley structures

A Cayley structure on a finite set A ⊂ Z n is a linear map π : Z n → Z k such that π(A) is a linearly independent set. Caley structures on A are highly relevant for properties of a certain geometric object XA called the toric variety associated to A. In particular, linear subspaces of XA are determined by Cayley structures of A, as is the so-called dual defect. The goal of this project is to develop algorithms to study Cayley structures for a given subset A ⊂ Z n , in particular, to develop an algorithm classifying Cayley structures of A up to equivalence. This will have important applications for explicit computations involving dual defects and linear subspaces of toric varieties. In addition to theoretical work, the student will be expected to implement their algorithm in the computer algebra system Macaulay2. In carrying out this project, the student will be exposed to a variety of ideas from algebraic and discrete geometry

Supervisor: Dr Jonathan Jedwab

Email: jed@sfu.ca

PROJECT: Combinatorial Problems Arising in Digital Communications

The project will involve the study of combinatorial problems arising in digital communications. See http://people.math.sfu.ca/~jed/research.html for background on the general area of research, and http://people.math.sfu.ca/~jed/students.html for examples of previous USRA projects.

Requirements: Students should have completed MACM 201 (Discrete Mathematics II), and preferably have some programming experience. However, the most important attributes are enthusiasm, persistence, and a willingness to learn new skills.

Supervisor: Dr Cedric Chauve

Email: cedric. chauve@sfu.ca

PROJECT: Improving the Efficiency of Third Generation Sequencing Algorithm Through Pseudoalignment

Third Generation Sequencing (TGS) technologies (Oxford Nanopore, Pacific Biosciences) generate long reads (which is great for downstream applications that are quite noisy (>10% error rate, which is a problem for downstream applications). As a consequence, the problem of correcting TGS data using short but accurate Illumina reads (error rate < 1%) is an important bioinformatics problem. This approach is called hybrid correction, and is the only accurate approach for data sets characterized by a low to moderate depth of coverage of long reads.

In a recent paper (https://doi.org/10.1093/bioinformatics/btw463) we proposed the combinatorial algorithm CoLoRMap for hybrid correction based on a combination of graph-theoretical methods (shortest paths in a graph) and local-assembly of short reads. This method is very accurate but the initial phase of mapping the short reads onto the long reads is time consuming and impacts the applicability of the method on desktop computers.

Recently, the approach of pseudoalignment has been shown to speed-up enormously applications requiring the mapping of short-reads (see the blog post http://nextgenseek.com/2015/05/kallisto-paper-summary-near-optimal-rna-seq-quantification/). But so far, pseudoalignment has been used only with accurate short reads, and not with noisy long reads.

The first goal of this project is to modify CoLoRMap to replace the time-consuming short-reads mapping phase by pseudoalignment. In parallel, the intern will be integrated to a group of researchers (from Computer Science at SFU and the Vancouver Prostate Centre) working on the use of Oxford Nanopore TGS sequencing data in cancer genomics.

Requirements: good background in discrete mathematics, excellent coding skills in C++, a decent background in probabilities is a plus.

Supervisors: Drs Ben Adcock & Simone Brugiapaglia

PROJECT: Deep Learning Techniques for Inverse Problems in Imaging

In the last few years, deep neural networks have outperformed state-of-the-art methods in challenging computer-vision tasks such as classification and segmentation. More recently, they have also shown promising results when applied to inverse problems in computer imaging such as deblurring, denoising, super-resolution, and the efficient reconstruction of medical images from linear measurements (e.g., Magnetic Resonance Imaging and Computer Tomography).

Instead of fixing a statistical prior (such as the sparsity of the image) to regularize the inverse problem, these new approaches seek to learn the reconstruction algorithm itself by taking advantage of a preliminary learning phase where the neural network is optimized based on a large dataset of training examples.

In this project, we will explore these new techniques and compare them with classical tools for inverse regularization, with the aim of understanding how to fully exploit their potential for inverse problems in computer imaging.

Requirements: Candidates should have a strong mathematical background.  Analysis, linear algebra, numerical analysis and Matlab experience are essential.  Previous knowledge of optimization and image processing are beneficial, but not strictly necessary.

Supervisor: Drs Ben Adcock & Simone Brugiapaglia

PROJECT:  Stochastic collocation methods for uncertainty quantification of physical models

Uncertainty is ubiquitous in science and engineering.  Uncertainty quantification is the process of deriving quantitative characterizations of uncertainties in both computational models and real-world applications.  One of the most common problems in UQ focuses on determining the parameter dependence of solutions of parametrized systems of PDEs.  Although this can be recast as the problem of approximating a high-dimensional function from sampled data, a major challenge lies with the fact that the amount of data is often small, since gathering data is time-consuming.  Efficient uncertainty quantification requires techniques from not only approximation theory, but also numerical analysis, statistics, and sparse recovery.

This project will investigate a recent class of techniques for high-dimensional approximation based on sparse approximation.  Its focus will firstly be on the improvement of these techniques, through, for instance, the design of novel adaptive sampling procedures, and the development of learning procedures to reduce the dimensionality.  It will also consider their application to parametric PDEs.

Requirements: Analysis, numerical analysis, linear algebra and Matlab experience are essential.  Optimization is beneficial but not mandatory.

Supervisor: Dr Michael Monagan

Email: mmonagan@cecm.sfu.ca

PROJECT: Counting and generating irreducible polynomials and solving polynomial diophantine equations

We are looking for a student, either a mathematics major or a computing major who likes algebra and programming, to work on one or both of the following projects.

1: We would like to count the number of and find algorithms for generating irreducible polynomials of certain forms. For example, for a given degree d, how many irreducible trinomials are there of the form x^d + a x + b over a finite field of size q? If we need one such polynomial, what's the best way to get one?

2: The main step to factoring a multivariate polynomial uses Hensel lifting which must solve several polynomial diophantine equations. These are equations of the form

sigma1 A1 + sigma2 A2 + ... + sigman An = C

where the A1, A2, ..., An and C are given polynomials and we want to solve for the simgas.  We are developing algorithms based on polynomial evaluation and interpolation which can be parallelized.

Requirements: Students should have taken a first course in either algebra or number theory (MATH 340 or MATH 342 at SFU) and have some programming experience  in either C or C++ or Python. We will use Maple and C and Cilk C (a parallel version of C)  for experiments.  No parallel programming knowledge is needed.

SUPERVISORS: Drs Sandy Rutherford and JF Williams

EMAIL: arruther@sfu.ca  and jfwillia@sfu.ca

PROJECT:  Mathematical Modelling of HIV Testing, Treatment, and Care

This project will combine mathematical modelling of the HIV epidemic in Vancouver with operations research analysis of HIV testing, engagement in care, and retention in care. The models are defined as systems of ordinary differential equations.  Data for the project has been provided by Vancouver Coastal Health and the BC Centre for Excellence in HIV/AIDS. Key questions to be addressed are:

1.      What are the optimal allocations of resources across treatment programs to minimize HIV incidence, morbidity, and mortality?

2.      What are the optimal allocations of public health resources between treatment and testing programs to minimize HIV incidence, morbidity, and mortality?

These questions can be framed as “black box” optimization problems, in which the objective function is obtained by numerically solving the ordinary equation models. However, these optimization problems are computationally challenging, because the objective function is nonconvex and high-dimensional. The focus of this research will be on developing parallelized code for solving these problems using metaheuristic optimization methods. The computations will be done on Compute Canada’s high-performance computing cluster at SFU.

Supervisor: Dr Paul Tupper

Email: pft3@sfu.ca

PROJECT:  Gromov-Hausdorff Distance for Metric Structures

The Gromov-Hausdorff distance is a way of defining how close two metric spaces are to being isomorphic. It gives a complete separable metric on the space of all compact metric spaces. This
project will investigate generalizing the Gromov-Hausdorff distance to classes of metric structures. Roughly, metric structures are metric spaces with extra structure on them, for example, normed spaces or inner product spaces.  This work will build on a metric on numerated structures defined by ben Yaacov.

Requirements: Analysis, Metric Spaces.

Supervisor: Dr Paul Tupper

Email: pft3@sfu.ca

PROJECT:  A diversity view on clustering algorithms

Clustering is a common used procedure in which a collection of objects is broken up into "natural" groupings. For example, given a finite collection of points in a metric space, you assign points to one of two clusters such that points within clusters tend to be closer together than points in different clusters. Clustering in metric spaces is a well-established field. In this project the student will study clustering in the context of diversities, a generalization of metric spaces where a positive number is assigned to every subset of points, and not just pairs as in a metric space.

Requirements: Discrete math and/or optimization.

Supervisor: Dr Paul Tupper

Email: pft3@sfu.ca

PROJECT:  Tight Spans for Metric Structures

Given a metric space, its tight span is the set of all minimal one-point extensions of the space. The tight span can be equipped with its own metric, and viewed as its own metric space, so that the original metric space is naturally embedded in its tight span. The theory of tight spans arose in phylogenetics but has become of interest in its own right. In this project the student will work to see how much of the theory of tight spans can be generalized to metric
structures, which are basically metric spaces with extra structure (one example being normed spaces). We see already how this generalization works in some cases, but we'd like to push it further.

Requirements: Analysis, model theory.

Supervisor: Dr Imin Chen

Email: ichen@sfu.ca

PROJECT:  The LWE (learning with errors) problem

The LWE (learning with errors) problem involves finding a vector s, given that s lies on a list of hyperplanes known only up to some error. The objective of this project is to study and give an independent exposition of a result of Regev [1] which shows that if there exists an efficient algorithm that solves LWE, then there exist an efficient quantum algorithm that approximates the decision version of the shortest vector problem.  This hardness result, and later variants such as RLWE, are often used to motivate approaches to post-quantum cryptography and homomorphic encryption.

[1] On Lattices, Learning with Errors, Random Linear Codes, and Cryptography, Oded Regev, Journal of the ACM 56(6), article 34, 2009.

Supervisor: Dr Weiran Sun

Email: weirans@sfu.ca

PROJECT: To study the well-posedness of the famous Boltzmann equation with so-called non-cutoff kernels

Due to the singular nature of the interaction operator, one expects there will be regularizing effect for any positive time. Existing results have shown such regularization starting for initial data with sufficient smoothness. Our goal is to lower the assumption on the regularity of the initial data and pursue regularization of weak solutions. Any student interested in this project is required to have taken measure theory and functional analysis courses.

Supervisor: Dr Nils Bruin

Email: nbruin@sfu.ca

PROJECT:  Computational arithmetic geometry: Twists of the Burkhardt Quartic

Recent work [*] has exhibited a very explicit correspondence between a certain quartic threefold and sextic polynomials with very particular properties. It also found that the two classical models of this quartic do NOT define the same variety over Q. There is a very interesting connection with a so-called "field of definition obstruction" here that can be examined computationally.

Requirements: a reasonably algebra knowledge; preferably also algebraic geometry; willingness to use computational tools to explore mathematical problems. The exact research topic can changed depending on the interests and skills of the qualifying candidate.

[*] Nils Bruin, Brett Nasserden, Arithmetic aspects of the Burkhardt quartic threefold, ArXiv preprint arXiv:1705.09006, (2017)

Supervisor: Dr JF Williams

Email: jfwillia@sfu.ca

PROJECT:  Verified computation of spiral wave solutions to the Complex Ginsburg Landau equation

The Complex Ginsburg Landau equation is a partial differential equation which provides a qualitative description of phenomena including cardiac waves, liquid crystals, superconductivity and many others.

This project will use techniques from functional analysis and scientific computing to build tools with which to prove the existence of spiral wave solutions to the CGL equation.

Requirements: Familiarity with MATLAB, differential equations and some numerical analysis.

Supervisor: Dr David Muraki

Email: muraki@sfu.ca

PROJECT: Computation of Fluid Models for Atmospheric Science.

Independently motivated undergraduates in the third or fourth year of their degree are invited to join a research group that uses computational models to understand the fluid mechanics of the weather.  There are active projects that investigate a variety of atmospheric phenomena.

One current area of interest involves projects connected with the question, "What is the shape of a cloud"?  We have developed a new mathematical model for the motion of cloud edges --- one that has already confirmed the behaviour of the somewhat rare phenomenon known as a "holepunch" cloud (search for images!).

Requirements: Some background in differential equations is essential, as is proficiency in a computational environment such as Matlab.

Supervisor: Dr Nilima Nigam

Email: nigam@mathsfu.ca

PROJECT:  The role of architecture and regionalization of tissue in muscle mechanics

The study of movement in mammals has a long history, but somewhat surprisingly, one of the most popular mathematical models of muscle mechanics continues to be an 80-year-old description of a 1-D muscle fibre due to Hill. In this project, the student will be involved in an ongoing project to develop a fully 3-D mathematical model for muscle. Concretely, the student will learn about a new nonlinear elasticity model for muscle; design computational experiments, and run these on an existing mathematical software. We are particularly interested in understanding the role of muscle density and connective tissues in locomotion. We would also like to examine what happens during Parkinson's disease. The work will be joint with Professor James Wakeling in the BPK department, and a nice opportunity to explore mathematical applications in physiology.

Supervisor: Dr Nilima Nigam

Email: nigam@mathsfu.ca

PROJECT:  The conjugate gradient method on realistic high-performance computers

The conjugate gradient method is an elegant and efficient method for solving certain linear systems, and is considered the 'work-horse' method in modern numerical analysis. It is increasingly used to solve very large computational problems.

It is very easy to build large-scale computers using many inexpensive units. It is next-to-impossible to avoid losing reliability in this setting. Random faults such as bit-flipping, or outright failure of individuals components could happen.

The conjugate gradient method was designed and analyzed to work as a deterministic algorithm. What happens to it when computer-related faults described above are allowed in the algorithm? No one truly knows.

This project will be a theoretical and computational investigation of how fault-tolerant the conjugate gradient method is.

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