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Summer Undergraduate Research Assistants

Summer Undergraduate Research projects for summer 2019 are posted below. Please make sure you meet the eligibility before applying!

NSERC Undergraduate Summer Research Awards (USRA), and Work Learn Undergraduate Research Awards (WLURA) give promising undergraduate students the opportunity to spend a summer working on a project with a Statistics professor. These awards represent a chance to gain valuable experience in many aspects of research, including data analysis and coding.  You can enhance your resume for employment or for graduate school application, and find out what sort of career in statistics you want to pursue. 

Position Details:

Summer Undergraduate Research positions run for a minimum of 16 weeks, with the possibility of extension at the discretion of the supervisor. The positions are full time, so you must be willing to be on-campus every weekday for the duration of the summer, unless otherwise stated by your supervisor. This means that you will not likely be able to take any courses on top of the position. Please do not apply if you are unable to commit to these time requirements. Award holders are required to submit a short report on their activities at the end of the award term.

Value of Award:

The stipend for these awards is a minimum of $6,000.00.

Aboriginal USRA Positions:

We strongly encourage Aboriginal students to apply for these opportunities. Please indicate your Aboriginal status in your application.


NSERC USRAs are available to Canadian citizens and permanent residents only.

WLURAs are available to international students only.

To be eligible for either of these awards you must:

  • be registered (at the time of application) in a bachelor’s degree in the term immediately before holding the award
  • International students MUST hold a valid study permit, and be registered as a UBC student. This means that International Students who graduate in May are NOT eligible for this award. If you are unsure of your eligibility, please contact the USRA coordinator AND an International Student Advisor BEFORE you apply
  • Have a cumulative GPA of B- (68%) or greater
  • At the time of the award, have completed all course requirement of at least the first year of university
  • NOT have started a graduate program in science or engineering


  • You may still apply for the program if you already hold a bachelor’s degree, and are enrolled in a second bachelor’s degree
  • You may only hold one USRA per fiscal year (April 1-March 31)
  • You may hold a maximum of three USRAs during your university career
  • If you are a graduating student, you may hold the award in the term immediately following the completion of your degree requirements, regardless of your graduation date. The exception to this is International Students who do not hold a Post-Graduate Work Permit (see above)
  • You do NOT have to be a Statistics major to apply, but please note that the positions are competitive, and priority will be given to students with a background in Statistics
  • Individual supervisors may require you to have taken certain courses, or have a specific skill set

You are NOT eligible if:

  • You are enrolled in an undergraduate professional degree program in the health sciences (e.g., MD, DDS, BScN, BScPharm)
  • You hold higher degrees in sciences or engineering
  • You are an international student who has graduated (or will graduate in May), and you do not hold a Post-Graduate Work Permit

Detailed information on awards and eligibility can be found on the Faculty of Science USRA webpage.

Application Procedures:

  • Check out this years list of projects, and decide which projects you are interested in, making sure that you meet the required skill set/prerequisites that are mentioned. Competition for these positions is very keen, so it’s a good idea to apply as soon as possible.
  • Additional projects MAY be added, so check back periodically if none of the projects currently posted are applicable to you
  • Remember that you're not restricted to the projects on the list. If you have an idea for a project, you are encouraged to contact a faculty member with your idea to see if they are interested in supervising you.
  • Email your application package to the project supervisors with whom you are interested in working. Your package should include a copy of your unofficial transcripts, along with a cover letter explaining what interests you about their research, and what you think you can contribute to the project. Make sure you submit your application as soon as possible, as these positions go fast.
  • Project supervisors will contact students with whom they are interested in working, and may request an interview.
  • Should you be chosen to work as a summer research assistant, you will be asked by the research supervisor to fill out an NSERC online application form. Instructions will be given to you at that time.
  • Don't procrastinate. We receive a limited quota of positions, and they are filled very quickly!

Projects for 2019:

1. Deep Learning

Determining the DNA sequences that give rise to various biological mechanisms has major clinical implications. We will approach this challenge as a prediction task by building deep learning (DL) models to predict e.g. chromatin accessibility from DNA sequences. Working with the IMMGEN consortium, we have direct access to one of the most comprehensive ATACseq dataset, which comprises chromatin accessibility information of close to a hundred cell types. Building an accurate multi-response DL model to jointly predict chromatin accessibility of various cell types will be a major part of this project. Also, to facilitate interpretability, which is critical for clinical translation, the student will be developing methods to extract cell-type specific DNA motifs from the DL model.

Interested students please contact Dr. Mostafavi: saram [at] and provide a cover letter and unofficial transcripts

2. Manifold Regression

While the effects of disease on brain are gradually being revealed via large scale neuroimaing studies, the underlying biological basis to these brain alterations are largely unknown. In collaboration with the RUSH Medical Center, we are analyzing the associations between genomic and brain attributes over one of the largest datasets that comprise both genomic and neuroimaging data collected from ~2,000 individuals. Genomic attributes include gene expression, DNA methylation, and histone acetylation, and the primary neuroimaging attribute of interest is brain connectivity. To associate genomic attributes to brain connectivity, one needs to respect the manifold structure of connectivity/covariance matrices, which we will achieve through developing new manifold regression models. Also, recent neuroimaging studies suggest that disease-related effects might be encapsulated in the temporal dynamics of brain connectivity. We will thus build high dimensional Hidden Markov Models to capture this aspect of brain connectivity, and extend manifold regression to this setting.

Interested students please contact Dr. Mostafavi: saram [at] and provide a cover letter and unofficial transcripts

3. Optimization Methods in Bayesian Inference 

Optimization methods are becoming increasingly popular in Bayesian computation and data science, especially in the setting of large-scale data analysis. Particularly useful are "automated" methods, i.e., those designed to be accessible to practitioners without in-depth knowledge of statistics or machine learning. Our lab is working on a wide variety of automated optimization-based Bayesian methodologies, from data compression to model learning. In this project, the student will design a cutting-edge implementation of one of these methods in a popular probabilistic programming framework (e.g. Stan, TensorFlow Probability, Edward), and conduct the first comprehensive comparison study to related methods on real data analysis problems.  

A good candidate for this project will have strong coding skills (experience in Python/numpy/scipy and perhaps a willingness to learn C++), as well as familiarity with optimization methods, probability, and statistics.  Bonus points for experience with probabilistic programming frameworks.

Interested students should contact Dr. Trevor Campbell at trevor [at] and provide a cover letter and unofficial transcript.

4. Probabilistic Programming Languages

Probabilistic programming languages (PPL) allows rapid prototyping of complex models and is a current hot topic in computational statistics and Bayesian data science. Our lab is developing a PPL based on new developments in the field of non-reversible Monte Carlo methods. The student could get involved in a variety of aspects of this projects ranging from designing inference engines, to creating probabilistic models and doing data analysis.

I am looking for students with particularly strong programming skills. Previous exposure to JVM languages such as Java, Scala or Xtend highly recommended.

Interested candidates should contact Dr. Alexandre Bouchard-Côté at bouchard [at], and provide a cover letter and unofficial transcripts. 


Contact the Statistics USRA coordinator at gradinfo [at] (subject: Undergraduate%20Research%20Assistant%20opportunities)

Profiles of some previous Summer Undergraduate Research Assistants:

Angad Kalra, a Combined Major in Math and Computer Science, worked with Prof. Sara Mostafavi in the summer of 2017. Angad first performed literature survey on the problem of open chromatin region classification. He then examined the available tools for deep learning and learned one of them, namely TensorFlow. Using TensorFlow, he implemented and tested a network architecture analogous to the state-of-art for a similar problem. Finally, he presented how to apply TensorFlow for deep learning to the lab. In addition to this project, he also helped with implementing an algorithm for solving a linear mixed model in associating gene-by-environment interactions to gene expression, which he also presented to the lab.

Joanna Zhao, graduated with a BSc. in Statistics, and is now a graduate student in the Department of Statistics and Actuarial Science at SFU. Joanna worked with Jennifer Bryan. “My part in the project was to systematically work through“Creating more effective graphs” by Naomi Robbins and produce all the plots using the ggplot2 package in R language. The goal was to create a convenient and simple tool that can provide users with clear instructions on how to create each and every graph using ggplot2. Ultimately, a web application was put together to present an organized visual collection of all the graphs and the corresponding code. I have learned how to make a web application framework for R and improved by knowledge and usage of R functions to organize and manipulate data. I have also learned how to use knitr and Rmarkdown to generate documents/reports from R, and how to write a Makefile to compile source code."