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 — Friday, October 9th
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 Fall 2020 deadline is Friday, October 9th.
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: Editable grammars for compression of symbolic music
A context free grammar’s hierarchical structure can also highlight internal structure inside the string. What if we have two strings that are in some sense structurally very similar: can a CFG that summarizes one string be adapted to a related string? Can we build a short CFG for one string and see how much it would need to change in order to summarize a different string?
In this project, we will use symbolic music representations as the strings to analyze, because this data has hierarchical structure. Our hypothesis is that the number of rule changes necessary to go from a grammar for a string x to a grammar for a string y is smaller when y is closely related to x (such as sharing a genre or composer).
Required skills:
- Must have taken CS 360 and 341; 462 would be helpful
- Rudiments of music
- Ability to code in Python.
Interested students should contact Dan Brown: dan.brown@uwaterloo.ca
Title: Front end for a digital greenhouse application
At Intelligent Connectivity lab (ICON Lab), we have developed a system that uses battery-free sensors to monitor the moisture level of plants in a greenhouse. Our sensors are already deployed in a greenhouse in Ontario. Our technology enables greenhouses to maintain suitable moisture levels not only to improve plant productivity and quality but also to save up to 25% of irrigation water. We are looking for a URA who can build a browser-based front end to this system that would allow a greenhouse manager to monitor the greenhouse in real-time and water the plants only when it is required.
Prerequisites: Familiarity with front-end design. Familiarity with Rasberry Pi and Azure IoT platform is a plus.
Interested students should contact Professor Omid Abari: omid.abari@uwaterloo.ca
Title: Machine Learning for IoT applications
At Intelligent Connectivity lab (ICON Lab), we are looking for URAs who are interested in working at the intersection of ML and IoT. Past research performed by ICON lab’s URAs have resulted publications in the best conferences, giving them opportunities to join grad schools in top universities.
Prerequisites: Experience with Python. Familiarity with Tenserflow, PyTorch or Keras is a plus.
Interested students should contact Professor Omid Abari: omid.abari@uwaterloo.ca
Title: What does WiFi networks reveal about our life?
At Intelligent Connectivity lab (ICON Lab), we are looking for URAs who are interested in doing research on WiFi networks. In this project, we are interested to learn what we can learn about our life style from the patterns of WiFi packets. Past research performed by ICON lab’s URAs have resulted publications in the best conferences, giving them opportunities to join grad schools in top schools.
Prerequisites: Experience with C and Python programming. Interests in designing systems. Familiarity with WiFi and wireless networks is a plus.
Interested students should contact Professor Omid Abari: omid.abari@uwaterloo.ca
Title: IoT networks for Digital Twins
A digital twin is a digital replica of any physical entity. To achieve digital twin vision, we require to enable internet connectivity to billions of things. At intelligent Connectivity lab (ICON Lab), we are developing new wireless technology to enable this vision. We looking for URAs who are interested in working with hardware and designing new type of hardware. Past research performed by ICON lab’s URAs have resulted publications in the best conferences, giving them opportunities to join grad schools in top schools.
Prerequisites: Experience with RF and PCB design. Familiarity with wireless systems is a plus.
Interested students should contact Professor Omid Abari: omid.abari@uwaterloo.ca
Title: Front end for a rainforest protection application
We have built a system that uses Earth observation data to detect forested land and creates certificates in the Algorand blockchain to prove the existence of a forest. This allows forest managers to get sponsorship for (parts of) their forest, and they can prove that the forests still existed, as of the time of the last survey, and that no one else is sponsoring the same forest section. I’m looking for a URA who can build a browser-based front end to this system that would allow a forest manager to mark a section of forest for a certificate, and for a corresponding certificate to be stored on a blockchain. Another front end would allow a potential sponsor to view the forest, and to verify that the forest still exists and has not been sponsored by anyone else.
Prerequisites: familiarity with front-end design using a framework such as Angular JS or React
Interested students should contact Srinivasen Keshav: keshav@uwaterloo.ca
Title: Estimating the lighting load of commercial buildings
Commercial buildings use quite a bit of energy for lighting. Assuming that a building is lit by compact fluorescent fixtures, we can estimate the lighting energy footprint of commercial buildings from satellite observations. I’m looking for a URA who would be interested in writing code using the Google Earth Engine (e.g. https://developers.google.com/earth-engine/datasets/catalog/AHN_AHN2_05M_INT) to identify commercial buildings, their footprint, and thus their lighting load.
Prerequisites: familiarity with Python
Interested students should contact Srinivasen Keshav: keshav@uwaterloo.ca
Title: Creating a set of curated datasets
In the past decade, the ISS4E group (http://iss4e.ca ) has acquired quite a bit of raw data, including data on electricity consumption, e-bike travel, images of snow on solar panels and occupancy measurements in offices. The task of this URA is to curate these datasets and release them public after anonymization. This could result in a paper in a journal such as Data in Brief (https://www.journals.elsevier.com/data-in-brief/policies-and-guidelines/what-data-are-suitable-for-data-in-brief )
Prerequisites: none
Interested students should contact Srinivasen Keshav: keshav@uwaterloo.ca
Title: Android UI/UX Engineering for an Image Analysis Application
Tasks include:
- Create a frontend UI for an Android camera application, intended to capture RGB+Depth images of trees.
- Ideally, the UI would allow users to:
- View continuous camera preview, click to capture images
- Collect and label multiple images of the same tree
- Tap on locations of note within the image preview, such as identifying the tree trunk; save information about these pixels with the image.
- Other features according to student time/interest, such as exporting data, obtaining image metadata, and additional work on the app backend.
Prerequisites: experience in Android app UI design and implementation.
Interested students should contact Srinivasen Keshav: keshav@uwaterloo.ca
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: Using a probabilistic programming language to model and solve machine learning tasks
Probabilistic programming languages (PPLs) are emerging as a powerful technology for model-based machine learning. This project pushes the boundaries of what is possible with probabilistic programming when combined with deep learning. In particular, students will use a PPL to express interesting machine learning problems, and use neural networks to approximate solutions to those problems.
We are looking for ambitious students with the following skills:
- Solid experience in deep learning and reinforcement learning
- Familiarity with deep learning frameworks
- Basic knowledge of probabilistic programming
- Ideally (though not required), familiarity with a probabilistic programming language such as Pyro
Interested students should email Yizhou Zhang: yizhou.zhang@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 applicants please follow directions at https://cs.uwaterloo.ca/~r5olivei/#undergrad-research
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 applicants please follow directions at https://cs.uwaterloo.ca/~r5olivei/#undergrad-research
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
