Search the:

Apply Donate Crisis Centre

Faculty of Science Project Summaries

Supervisors

Supervisor name: Andrea Kirkwood

Project title: The Role of Salinity in Stormwater Pond Community Metabolism

Summary of research project: Stormwater management ponds (SWMP) are a growing part of urbanizing landscapes, yet their ecological function remains poorly known. One aspect of ecological function is community metabolism (photosynthesis and respiration). The undergrad student would conduct a field and lab study of SWMP in Durham Region that investigates the role of salinity in community metabolism efficiency.

Student responsibilities/tasks:

The student would conduct field work (alongside a graduate student) to collect water and biological samples, as well as deploy sensors that track dissolved oxygen and conductivity.
The student would process samples in the Ontario Tech aquatic ecology lab to measure nutrients, algae, bacteria, organic matter, and chloride.
Applicants may wish to consider doing an undergraduate thesis in their 4th year based on this project.

Student qualifications required:

This project is geared for students in the Biological Science program with a minimum GPA of 3.0.
Ability to work outside in inclement weather is an asset, as well as having some laboratory experience.

Expected training/skills to be received by the Student:

Field sampling techniques including working with sensors and field probes, water and biological sampling.
Laboratory techniques such as measuring water quality parameters and conducting bioassays.
Learning about experimental design and why it is important for study validity.
Data management including how to properly input, curate, and analyze data.
Working in a team environment, including communication skills and team work.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Annie En-Shiun Lee

Project title: Educational Learning Application for Low-Resource Languages

Summary of research project: FlipCard is a mobile-compatible web application designed to enhance the learning experience for low-resource language learners. For these languages, few online resources or dedicated language learning apps are available. FlipCard addresses this gap by utilizing large language models (LLMs) specifically trained on these languages. This is combined with a user-friendly interface that supports the learning experience.

Student responsibilities/tasks:

Students will attend weekly meetings to discuss progress and next steps.
Students will contribute novel and valuable suggestions that impact the research.
They will analyze experimental results, offering both quantitative and qualitative interpretations.
Students will assist in writing and proofreading research papers.
They will respond to reviewers' comments during the rebuttal phase.

Student qualifications required:

Front-End: React, Expo, CSS, Object-oriented programming.
Back-End: Firebase, database schema design.
Tools: Git, Vercel.
Relevant traits: Self-starter, Team player and communicator, Multilingual background, Psychology/Human-computer interaction background.

Expected training/skills to be received by the Student:

Gain advanced skills in Human-Computer Interaction (HCI), focusing on creating user-friendly interfaces for screens of different sizes.
Develop proficiency in front- and back-end development technologies, as well as an understanding of machine learning and large language models.
Acquire hands-on experience in conducting comprehensive user research studies, including data collection, analysis, and applying findings to design.
Learn data analysis and statistical techniques, essential for interpreting user study results and making data-driven design decisions.
Learn how to write, proofread, refine research papers, and respond to reviewers. The goal would be to write-up a paper as a team at the end of the project.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Annie En-Shiun Lee

Project title: URIEL++: Language Database and Vector for Multilinguality and Diversity

Summary of research project: URIEL+ is an enhancement of the URIEL knowledge base for multilingual natural language processing (NLP), providing vector representations and distance measures for 8,171 languages. URIEL+ expands typological feature coverage to 2,898 languages. Experiments show improved performance on various downstream NLP tasks with URIEL+ language features.
With URIEL++, we are addressing remaining gaps in URIEL. This includes integrating script data from ScriptSource for 8,290 languages, using parent languages to fill dialect data, and creating an interface to help researchers select relevant features. Our goal is to make URIEL++ a versatile resource for NLP, linguistics, and sociolinguistics research.

Student responsibilities/tasks:

Students will attend weekly meetings to discuss progress and next steps.
Students will contribute novel and valuable suggestions that impact the research.
They will analyze experimental results, offering both quantitative and qualitative interpretations.
Students will assist in writing and proofreading research papers.
They will respond to reviewers' comments during the rebuttal phase.

Student qualifications required:

Required Technical skills: Human-computer interaction and interface design; Python and JavaScript; NumPy, Pandas and Git; Machine learning and statistical data analysis
Relevant traits: Communication and teamwork skills; Strong writing skills; Linguistics interest or background
CSCI 4050U - Machine Learning, Theory and Application

Expected training/skills to be received by the Student:

Gain advanced skills in Human-Computer Interaction (HCI) and data visualization, focusing on creating user-friendly interfaces for research.
Develop proficiency in machine learning, especially programming language for Python and the packages like TensorFlow, Keras. Also become proficient on running compute on GPU and SLURM on Canada Compute Resources
Large Language Models (LLMs): Specialized training in understanding and working with Large Language Models, such as GPT-3.5, to leverage natural language processing capabilities for assessment tool development.
Learn data analysis and statistical techniques essential for interpreting results and making data-driven design decisions.
Research Methodology and Data Analysis: Comprehensive training in research methodologies, including data collection, analysis, and interpretation, to equip the student with the necessary skills for robust empirical research in the field of machine learning and NLP.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Brad Easton

Project title: Advanced materials for electrochemical energy storage and conversion

Summary of research project: The development of doped metal-oxide-based materials supports represent a unique platform for electrochemical energy conversion and storage applications. This project will be involve the synthesis and characterization of various nanostructured metal-oxide that are doped with different metals in order to modify their electronic properties and morphology. Detailed physical characterization will be performed to relate electrochemical performance with structure/composition.

Student responsibilities/tasks:

The student will work in collaboration with other team members on the synthesis and characterization of the materials.
Materials characterization methods will include SEM, XPS, XRD, TGA, BET, and UV-Vis spectroscopy.
In addition, the student will be responsible for electrochemical testing and evaluation of the materials.

Student qualifications required:

Chemistry majors.
Successful completion of CHEM 3540U, and CHEM 3040 is considered an asset.

Expected training/skills to be received by the Student:

Nanomaterials synthesis.
Electrochemical measurements.
Clean energy.
Analytical chemistry.
Surface chemistry.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Fedor Naumkin

Project title: Intermolecular complexes: properties and processes - computational modeling

Summary of research project: Intermolecular complexes can have unique characteristics, and intra-complex reactions can form new molecules efficiently due to electric fields of ionic constituents.
Objectives can involve:

Prediction of structure and properties of uncommon molecular systems;
Modeling of their structural dynamics and property variations under external stimuli (electric dis/charge, mechanical pressure);
Simulation of species' IR spectra to enable their experimental identification and to track processes.
Such systems have multiple practical applications including efficient light detection and utilization of solar energy, reactions mediated by electric fields, molecular electronic devices and machines.

Student responsibilities/tasks:

Learn and use a computational chemistry software (under guidance of supervisor), prepare input files, run multiple calculations on available high-performance computing facilities (accessed remotely via student's laptop).
Analyze and visualize results, in particular by using a molecular-graphics software (to be learned), regularly discuss them with supervisor.
Do a literature search, prepare and deliver presentations (at the Research Day, etc.).

Student qualifications required:

Preferred: completed CHEM 2010 (Structure & Bonding) course with at least B- grade.
Proficiency in main MS Office applications (Word, Powerpoint, Excel).
Good calculus and geometry skills, good 3D imagination being a plus.
Interest in a computational research is a bonus.
Familiarity with work in a Linux environment is an extra bonus.

Expected training/skills to be received by the Student:

Direct experience in modern molecular modelling using state-of-the-art professional quantum-chemistry software at high-performance computing facilities.
Development of practical skills in a highly prospective area of computational nanochemistry, which adds a competitive edge for future employment.
Exercising the abilities to think independently, to efficiently check ideas via reliable calculations, to predict new species and/or processes to guide experiments.
Acquiring expertise in molecular systems design, quantitative analysis of their structure and properties, their relationships and evolution under external factors.

Length of award: 14 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Greg Lewis

Project title: Nonlinear Dynamics of the Atmosphere

Summary of research project: The Earth's atmosphere is a very complex nonlinear system that is influenced by many factors. However, the basic wind and temperature patterns, which are directly linked to changes in the climate, are primarily determined by the heating from the Sun and the rotation of the Earth. In this project, mathematical models that highlight these primary factors are studied to learn more about transitions in the observed flows. The models of interest are nonlinear partial differential equations, but simplifications to the system are made to allow for analysis using numerical approximation techniques. Bifurcation analysis techniques are used to determine and classify the transitions in the flows.

Student responsibilities/tasks:

In this Research Project, the Student will contribute to a research program that applies nonlinear dynamics techniques to simplified mathematical models of the climate and atmosphere in order to study transitions in the basic temperature and wind patterns.
Specifically, the Student, under close and direct supervision of the supervisor, will work independently on their own project.

Student qualifications required:

3rd year standing in a Mathematics, Physics or Computer Science program.

Expected training/skills to be received by the Student:

Development of analytical and problem-solving skills: the Student will be required, with guidance, to formulate and solve research problems.
Development of communication skills: the Student will be required to communicate their findings with other members of the lab.
Development of programming skills. The project will involve programming.
Knowledge building. The Student will acquire knowledge in Mathematics and Physics, relevant to the research problem.

Length of award: 16 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Janice Strap

Project title: Exploring the Cellulose Circuit

Summary of research project: Komagataeibacter xylinus, a Gram-negative bacterium, produces cellulose as an extracellular biopolymer essential for biofilm formation and for mediating host-microbe interactions. Relatively little is known about the regulatory network governing cellulose biosynthesis. This project offers opportunity to decode the function of a key regulatory protein in this pathway. By leveraging advanced molecular biology, bioinformatics, and biochemical techniques, we will map this proteinâ€™s influence on gene expression and cellulose synthesis. This research could have significant implications for biotechnological applications, particularly in material science and microbial biotechnology.

Student responsibilities/tasks:

Students will conduct experiments and document research activities in a laboratory notebook.
Techniques used in this project include aseptic handling, bacterial culture, sterilization, sample preparation, mutagenesis, complementation, cloning, analytical biochemical assays, gel electrophoresis, PCR, DNA/RNA extraction, bioinformatics analyses.
Completion of biosafety and WHMIS training is required.

Student qualifications required:

Applicants are expected to have successfully completed BIOL 2060U, and BIOL 3080U with a minimum grade of B+.

Expected training/skills to be received by the Student:

Experimental design and molecular techniques.
Research documentation and scientific reporting.
Data analysis and interpretation.
Core laboratory techniques and lab management.
Collaborative research and professional skills.

Length of award: 14 Weeks

Location of award: In-Person

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Joseph MacMillan

Project title: Simulations of Early Galaxies

Summary of research project: New observations from the James Webb Space Telescope (JWST) suggest that our current models of galaxy formation in the early universe are incorrect. This project will explore the formation of galaxies using N-body simulation techniques and explore models including gas dynamics and supernova feedback. Guided by JWST observations, the project will (1) determine where current models are falling short, (2) simulate the formation and early lifetime of a galaxy, and (3) investigate new physics that might be necessary in the early universe.

Student responsibilities/tasks:

Literature search and research of current JWST observations and implications for early universe galaxies.
Explore N-body simulation software appropriate for galaxy formation, including gas dynamics.
Perform high particle number simulations of galaxy formation.
Learn the necessary tools for simulation visualization and analysis.

Student qualifications required:

Completed PHY 2040U Mechanics II and PHY 3900U Astronomy II.
Comfortable with Python coding.

Expected training/skills to be received by the Student:

Programming and coding skills (Python, C, GitHub, Linux toolchains).
Basic research skills (literature searches, reproduction of already-published results).
Data analysis (large datasets, visualization, plotting).

Length of award: 14 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR

Supervisor name: Ken Pu

Project title: LLM Driven Semantic Type System

Summary of research project: This project proposes a novel type system tailored for large language models (LLMs) in programming, introducing "semantic types" that extend traditional types with semantic annotations. The system includes semantic type inference, where annotations are generated for code blocks involving these enriched types, and semantic type checking to verify expression correctness based on semantic meanings. Extensive use of LLMs will be critical for generating and verifying semantic types, allowing for more contextually accurate and reliable code generation by AI.

Student responsibilities/tasks:

Perform literature survey, especially on programming languages and prompt engineering methods.
Generating dataset involving annotation of programs with semantic types.
Participate in original research on semantic type theory.
Participate in the development of tools for semantic type inference and semantic type checking.
Perform benchmark evaluation of the system.
Prepare manuscripts for publication

Student qualifications required:

A+ in one of the following courses: CSCI 3055U or CSCI 4050U.
Basic understanding of ML, AI and LLM.
Strong background in discrete mathematics, logic and algorithms.
Strong coding knowledge in Python and one other language.
Basic understanding of LaTeX.
Strong desire to conduct original research by solving hard problems.

Expected training/skills to be received by the Student:

LLM based workflow.
Learn type theory and program verification.
Learn building novel benchmarks to evaluate AI systems.

Length of award: 14 Weeks

Location of award: Hybrid

Available Award: NSERC USRA or Ontario Tech STAR