The Cheriton School of Computer Science offers enrichment opportunities to exceptional students through the Undergraduate Research Assistantships (URA) program to earn $600 a term for part-time research with a faculty member. Starting from F18, the School will top up $400 for students who meet the URA eligibility requirements and are accepted by the CS URA program. The money is paid once near the end of the term.
Application deadlines per term
- Winter applications — Last day of the month in January
- Spring 2020 applications — Friday, June 12th
- Fall applications — Last day of the month in September
We will not accept any applications once deadline has passed
Eligibility
Any student who has completed their second year in the Faculty of Mathematics with a cumulative average (CAV) of at least 80% is eligible. Preference is given to students enrolled in Computer Science major plans. A student can only do one URA per term. A student cannot be on a co-op term while doing a URA.
Note
- International Students need an SIN # to work in Canada
Please refer to the Student Success Office for this information:
https://uwaterloo.ca/international-students/social-insurance-number
- Engineering Students need to follow Engineering URA procedures
For an Engineering student to do a URA with a CS professor, please follow the application procedures explained on the Engineering URA web page: https://uwaterloo.ca/engineering/ura
How do I apply?
- Check eligibility stated above. Make sure you are eligible before taking the following steps.
- Find a project, send a resume and recent grade report to the professor, request an appointment, and reach a verbal agreement on the details of the project.
- Complete the Registration form - Once you complete the Registration form you will be directed to a webpage with the URA Application Form
- Complete the URA Application Form (PDF)
- Email the form to Shoshannah Holdom, sholdom@uwaterloo.ca. The Spring 2020 deadline is Friday, June 12th.
The URA Application Form must be fully completed and submitted to receive your URA payment.
A faculty member must sign and fill out the account number on the URA Application Form.
URA supervisors
Faculty members post openings each term so interested and eligible students can apply. Students can also contact faculty members to see if they are interested in supervising a URA.
The following faculty members have confirmed that they are willing to be contacted by potential URAs.
URA research projects
Title: Flescqa - scalable lightweight analysis of large software systems
Flescqa is a project that aims to provide scalable lightweight analysis of large software systems. Information about software are extracted from different artifacts (e.g., source code, build code, models) and are stored in a Neo4J graph database for further processing. Neo4J’s native query language, Cypher, supports interesting queries over software data, and is generally easy to use and generally scales well over this data. However, there are some queries (e.g., path-dependent queries) that require care to avoid performance blow-up in cases where the query requires an infeasibly large number of intermediate paths.
We are looking for a URA who is interested in advanced databases, query languages, declarative languages, and software analysis to explore query patterns or a general methodology for executing arbitrary path-dependent graph queries at scale. Advanced work would include performance analysis or implementing this methodology as a Cypher-based evaluator for a declarative language (e.g Datalog).
If you are interested, please email Jo Atlee at jmatlee@uwaterloo.ca
Title: Database operators over encrypted data
Databases are often entrusted with sensitive information. Hence, one would like to encrypt the data before storing it in the database. However, standard encryption techniques prevent the data from being processed by the DBMS and data must be decrypted temporarily by the DBMS opening additional attack surfaces. In this project we aim to investigate how import database operators, such as join, group by, select, etc. can be implemented without decrypting the data. He will use novel encryption techniques and design and implement special operators for them in an open-source DBMS.
If you are interested in this URA, please email Florian Kerschbaum (florian.kerschbaum@uwaterloo.ca) with an unofficial transcript and a resume.
Title: C∀ (Cforall) Programming Language and Runtime System
The C∀ project is an open-source project extending ISO C with modern safety and productivity features, while still ensuring backwards compatibility with C and its programmers. C∀ is designed to have an orthogonal feature-set based closely on the C programming paradigm (non-object-oriented) and these features can be added incrementally to an existing C code-base allowing programmers to learn C∀ on an as-needed basis. In many ways, C∀ is to C as Scala is to Java, providing a research vehicle for new typing and control-flow capabilities on top of a highly popular programming language allowing immediate dissemination. There are many small development and evaluation activities within the C∀ project suitable for URAs. A URA candidate should be interested in programming languages and associated runtime internals with experience in C/C++ programming.
If you are interested, please email Peter Buhr at: pabuhr@uwaterloo.ca
Title: Efficient computation of generators of special invariant rings
Since Hilbert's seminal works on invariant theory, which laid foundational results for modern commutative algebra and algebraic geometry, and Mumford's seminal work on geometric invariant theory, much progress has been made on the computational aspects of invariant theory. Despite all of the progress made in this past century, many open questions still remain on the efficient computation of a generating set (or of a separating set) of invariant polynomials. The goal of this project is to make progress in efficiently computing a generating set of invariant polynomials for special group actions, which have profound applications in fundamental theoretical problems in computer science.
The ideal URA should have a solid command of linear algebra (equivalent of MATH 245, or MATH 235), with a solid command of basic abstract algebra (such as the material from PMATH 347) and some programming experience being a plus.
Programming will be done using Macaulay (no prior experience with this language required).
Interested students should contact Rafael Oliveira: rafael@uwaterloo.ca
Title: Computational complexity of polynomial identities
Polynomial identities in associative algebras have been a major object of study, as they are a natural generalization of commutative polynomial rings. While the basic structural theory of such rings is somewhat understood, the computational aspects of polynomial identities is still rudimentary. The goal of this project is to study the computational complexity of known polynomial identities and provide, through the computational lens, a systematic study and possibly new polynomial identities based on ideas from algebraic complexity theory. Another goal of this project is to use the better understanding of polynomial identities to solve open problems in non-commutative algebraic complexity theory.
The ideal URA should have a solid command of linear algebra (equivalent of MATH 245, or MATH 235), with a solid command of basic abstract algebra (such as the material from PMATH 347), combinatorics, and be fairly comfortable writing rigorous proofs.
Programming experience is a plus as experiments are part of the project.
Interested students should contact Rafael Oliveira: rafael@uwaterloo.ca
Title: Expressive Human-Computer Interaction
The Exii research group led by Prof. Daniel Vogel does all kinds of exciting Human-Computer Interaction research on different input and interaction technologies. Everything from pen based computing, touchscreens, and mobile devices, to large displays, virtual reality, augmented reality, and spatial augmented reality. We typically build our prototypes using tools like 3D game engines (mostly Unity), web development, or the Processing language. Our projects often leverage a range of techniques and technologies like computer graphics (hit testing algorithms, transforms, rendering), applied computer vision (mostly OpenCV), applied statistics (usually in Jupyter Notebook with a mix of Python, Pandas, and R), 3D fabrication (3D printing, physical mockups), electronics prototyping (usually Arduino based), applied machine learning (e.g. deep learning, classifiers like SVM), and motion tracking (Vicon or AR markers). A single project typically uses only a subset of these, and we don’t expect URAs to necessarily have these skills already. This is a great opportunity to learn new techniques and technologies while you work on cutting edge Human-Computer Interaction research.
Interested students should view a list of current projects and submit an application:
https://goo.gl/forms/bjMkN0Rryjupry2V2
Title: Differentially Private Statistical Estimation
Differential privacy is the current gold standard for data privacy. Recent developments have resulted in theoretically principled algorithms for private statistical estimation in high-dimensional settings. The goal of this project is to develop these theoretical methods into realizable and practical algorithms, which are able to privately estimate parameters of high-dimensional distributions in both synthetic and real data settings.
The ideal URA will be very comfortable with probability and linear algebra, in addition to being confident in Python.
Interested students should email Gautam Kamath: gckamath@uwaterloo.ca.
Title: Intelligent Connectivity for the Internet-of-Things
Although interest in connected devices has surged in recent years, barriers still remain in realizing the dream of the Internet of Things (IoT). The main challenge in delivering the IoT systems stems from a huge diversity in their energy, scalability, and data-rate constraints. The ICON lab at the Cheriton School of Computer Science is conducting research to address these challenges. The student will do research on designing new connectivity and sensing systems for virtual reality and smart home applications.
We are looking for students with one or multiple of the following skills:
- Experience with C/C++ programming, and interests in designing systems
- Familiarity with Micro-Controller, Raspberry Pi or FPGA programming
- Hardware design (familiarity with PCB design would be a plus)
- Research experience in wireless networks and systems
Interested students should email Omid Abari: omid.abari@uwaterloo.ca.
Title: Wrist-worn hand and finger gesture recognition study
The HCI lab at the Cheriton School of Computer Science is looking for a student interested in user study in hand and finger gesture interaction using wrist-worn sensors. The project requires excellent coding skills and some experience with hardware prototyping Additional experience with Arduino / Processing / User study is appreciated, but not necessary.
If you are interested, please email Keiko Katsuragawa at kkatsura@uwaterloo.ca.
Title: High-Performance Multi-Core Runtime Systems
The KOS project builds an experimental operating system kernel to investigate the intersection of design simplicity and performance in runtime systems. An important part of this study is carried out by building a nimble user-level threading runtime system for existing OS platforms, such as Linux and FreeBSD. Several development and evaluation activities are suitable for URAs in the context of this project, such as support for dynamic stack sizing, or fine-tuning the performance of synchronization operations. A URA candidate should be interested in system-level software and runtime internals, have experience with C/C++ programming, and be familiar with the POSIX system call interface.
If you are interested, please email Martin Karsten at: mkarsten@uwaterloo.ca.
Title: Cloud and Distributed Big Data Systems
If you are a senior undergraduate student interested in working with large scale distributed systems that involve managing big data and storage in the cloud to achieve scalability and deliver good performance, please e-mail Khuzaima Daudjee at: kdaudjee@uwaterloo.ca
Title: Context-aware Threat Detection on Android
Smartphones come with many sensors, such as cameras, microphones, or accelerometers. The information sensed by these sensors could be used to detect a threat to the smartphone owner, their phone, and/or sensitive data available on the phone. For example, the phone's camera may be able to detect a shoulder-surfing attack. The goal of this project is to implement such a threat detection mechanism on Android.
Android development experience is required. CS 350 would be useful.
Interested students should contact Urs Hengartner: urs.hengartner@uwaterloo.ca
Title: Enhancing a Main-Memory DBMS
We are looking for up to three students to fill URA positions to work on a main-memory database management system. The system is mainly coded in common lisp, but also has components written in C, Bison, Flex and csh. It is a "compiling" DBMS that has a simple extensible runtime and operates by translating SQL-like data manipulation requests directly to C code that interfaces with this runtime. The underlying data model is a generalization of the relational data model in which, among other things, arbitrary inheritance hierarchies may be declared.
During the course of the project, students will increase their familiarity with a range of topics, among which: semantic data modelling, query optimization, A* search, transaction compilation, legacy data integration, rule-based translation and source code synthesis.
Ideal applicants will have a solid background in algorithms and data structures, and a particular interest in program translation. Experience with programming in a functional style would also be an asset.
Interested students should contact Grant Weddell: gweddell@cs.uwaterloo.ca
Title: Interactive visualization techniques for data science
This URA project will be related to the design, development, and evaluation of interactive visualizations that support the general data science workflow: from exploratory data analysis, then to model development and interpretation, and finally to insight communication and storytelling. An idea candidate should be comfortable in web app development (e.g., JavaScript, NodeJS, JQuery, D3, Boostrap) and data analytics (e.g., Jupyter Notebook, Python, numpy, scipy, pandas, tensorflow, pytorch).
Interested students should view a list of current and past projects on Prof. Jian Zhao’s webpage: http://www.jeffjianzhao.com and submit an application: https://bit.ly/2tphwWK
Title: Semantic Mining of Mathematical Text
Extracting semantic information from natural language text is recognized as an important problem in artificial intelligence, and there are many developed techniques. The present project is to extract semantic information from mathematical text. It is the long-term goal of the “International Mathematical Knowledge Trust” to capture the mathematical knowledge represented in the complete research literature of mathematics. There are several levels of semantic information, from general keyword spotting all the way to formalizing the mathematical statements in a language such as CoQ or Lean. The present project will perform n-gram analysis on the abstracts of mathematical article reviews provided by ZB-Math.
Interested students should contact Stephen Watt: smwatt@uwaterloo.ca
Title: Deep Learning for Handwriting Feature Recognition
One successful method for recognizing handwritten characters is based on representing them as parametric curves (x(s), y(s)) where x(s) and y(s) give the x and y positions of the pen as a function of arclength. Expressing x(s) and y(s) as truncated Legendre series or Legendre-Sobolev series has proven quite successful for recognition. These series use orthogonal polynomials as the basis, rather than monomials s^k. Recognition methods using proximity of coefficient vectors has proven quite successful. The present project is to explore how accuracy can be improved by using coefficient vectors to train neural networks for recognition.
Interested students should contact Stephen Watt: smwatt@uwaterloo.ca
Title: Machine Learning of Handwriting using Chebyshev-Sobolev Approximation
One successful method for recognizing handwritten characters is based on representing them as parametric curves (x(s), y(s)) where x(s) and y(s) give the x and y positions of the pen as a function of arclength. So far, expressing x(s) and y(s) as truncated Legendre series or Legendre-Sobolev series has proven quite successful. These series use orthogonal polynomials as the basis, rather than monomials s^k. This project will be to explore whether recognition can be improved using the Chebyshev and Chebyshev-Sobolev polynomials as basis functions.
Interested students should contact Stephen Watt: smwatt@uwaterloo.ca
Title: Algorithms for Symbolic Polynomials
Computer algebra systems, such as Maple, are very good at performing algebraic operation on polynomials with terms of fixed degree, but are much weaker when exponents involve variables. A new class of algorithms work with so-called “symbolic polynomials”, which are polynomial-like objects where the exponents are themselves integer valued polynomials, e.g. x^(n^2+3n) – 5y^(2n-1). The present project will develop or enhance an existing Maple package for algorithms on symbolic polynomials, including polynomial greatest common divisors and factorizations. Experience with Maple is required.
Interested students should contact Stephen Watt: smwatt@uwaterloo.ca
Title: Handwriting Neatening
Just as face averaging can be used to produce faces that are perceived as attractive, character averaging can be used to produce attractive characters written in a user’s own distinctive style. Representing handwritten characters as a particular type of feature vector allows this averaging to be well-defined and efficient. This can allow handwriting samples to be adjusted for uniformity of size, slant, pressure, etc. The present project will involve developing a tool to mark up character samples. Experience with Typescript and Node.js will be an asset to the URA candidate, and experience with calligraphy will be a plus.
Interested students should contact Stephen Watt: smwatt@uwaterloo.ca
Title: Suggestion Systems for Handwriting Recognition
On-line handwriting recognition with tablet or phone has the advantage that it is not necessary for the system to present a unique recognition result. It is possible to present a set of best guesses and have the user accept the top result or make a correction. This means that a user’s acceptance and correction history can be used to continuously train and individualize the recognition. This project is divided in two parts: the first is to explore methods to evolve the support set of symbol samples as character suggestions are accepted and rejected. The second is to understand to what degree handwriting “styles” can be used to predict preferred forms of symbols, i.e if a writer draws one character in a certain way, can this be used to predict how they will write other characters. Experience with Typescript and Node.js will be an asset to the URA candidate.
Interested students should contact Stephen Watt: smwatt@uwaterloo.ca
Title: NAN-Boxing for Read-Free Javascript Code
Programming language interpreters, such as those for Javascript, typically allocate data objects in memory augmented with header words that describe the object type and size. This means that type testing, array bounds checks and other operations require following a pointer and reading memory. This is much slower than register-to-register operations. One technique that is used to improve performance is so-called “NAN-boxing” which encodes pointers in the “not-a-number” space of floating point numbers. This project explores the performance gains that can be made by encoding additional information with the pointer, including certain values and size information. A URA candidate should be familiar with at least one systems level programming language, such as C/C++, and working with program-managed memory.
Interested students should contact Stephen Watt: smwatt@uwaterloo.ca
Title: Learner Modelling for On-Line STEM Education
On-line education is undergoing a transformation in which the traditional “repeat until you get the quiz right” model is starting to be replaced with multiple pre-programmed paths with detours. This project will explore using machine learning techniques on quiz data to identify classes of learners with correlated learning needs. This can allow individualized coaching by the on-line instruction system, and steer students toward explanations given in terms they have shown they understand. A URA candidate should have experience with, or be willing to learn about, R or python and SVMs.
Interested students should contact Stephen Watt: smwatt@uwaterloo.ca
For more information, please contact:
Shoshannah Holdom
Office: MC 4014
Phone: (519) 888-4567 ext. 36826
Email: sholdom@uwaterloo.ca
