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Ontario Tech acknowledges the lands and people of the Mississaugas of Scugog Island First Nation.

We are thankful to be welcome on these lands in friendship. The lands we are situated on are covered by the Williams Treaties and are the traditional territory of the Mississaugas, a branch of the greater Anishinaabeg Nation, including Algonquin, Ojibway, Odawa and Pottawatomi. These lands remain home to many Indigenous nations and peoples.

We acknowledge this land out of respect for the Indigenous nations who have cared for Turtle Island, also called North America, from before the arrival of settler peoples until this day. Most importantly, we acknowledge that the history of these lands has been tainted by poor treatment and a lack of friendship with the First Nations who call them home.

This history is something we are all affected by because we are all treaty people in Canada. We all have a shared history to reflect on, and each of us is affected by this history in different ways. Our past defines our present, but if we move forward as friends and allies, then it does not have to define our future.

Learn more about Indigenous Education and Cultural Services

FSC project summaries

Supervisors
Nisha Agarwal Robert Bailey Yuri Bolshan Dario Bonetta Jeremy Bradbury
Jane Breen Christopher Collins Jean-Paul Desaulniers Brad Easton Mehran Ebrahimi
Sean Forrester Mark Green Andrea Kirkwood Helene LeBlanc Gregory Lewis
Joseph MacMillan Fedor Naumkin Theresa Stotesbury Janice Strap Jaroslaw Szlichta
Liliana Trevani Lennaert van Veen Olena Zenkina

 

Supervisor name:  Andrea Kirkwood, PhD
Project title:  Assessment of septic system pollution in the Kawartha Lakes

Summary of research project:  A major knowledge gap in our understanding of water quality drivers in the Kawartha Lakes is the role of residential septic systems. Most lakefront cottages/homes in the Kawartha Lakes are serviced by on-site septic treatment systems, yet we have no idea how well these systems are performing. In order to determine the extent to which septic systems contribute to water quality degradation in the Kawartha Lakes, an undergraduate student will conduct a septic-system tracer study. The student will collect water samples across the Kawartha Lakes in the nearshore zone and measure markers of septic contamination including fecal and pharmaceutical targets.

Student responsibilities/tasks:  The undergraduate student will work in collaboration with a graduate student to collect water samples from nearshore sites in the Kawartha Lakes. The undergraduate student will process water samples in the Kirkwood and Simmons labs at Ontario Tech, and will perform biological and chemical analyses, including analytical chemistry (Liquid Chromatography Mass Spectrometry) and quantitative polymerase chain reaction (qPCR) methods.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

The ideal student candidate will be going into their 4th year of study in Sept. 2021. Students from Biological Science (any specialization) or Chemical Biology are best suited for this research project, particularly those with familiarity/experience in molecular biology and/or analytical chemistry techniques.

Having a drivers license is an asset.

Expected training/skills to be received by the student:

The student will:

  • Gain skills in water quality analyses including colorimetric assays for measuring nutrients and chlorophyll.
  • Learn how to use advanced equipment for water quality monitoring, including polymerase chain reaction (PCR) and Liquid Chromatography-Mass spectrometry (LC-MS).
  • Learn advanced data analysis techniques including multivariate statistics and bioinformatics.
  • Gain effective science communication skills by contributing to a community-focused website that provides research updates, as well as give research presentations to community partners (Kawartha Conservation, Lake Associations).

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Brad Easton, PhD
Project title:  Catalyst/support interactions for novel fuel cell catalysts

Summary of research project:  This research project will involve the synthesis and characterization of Pt nanoparticle catalysts deposited on novel conductive metal oxide supports. Specifically, the student will prepare Pt nanoparticles deposited on novel conductive metal oxide supports of various composition and shape. Your project will seek to gain insights into the electrocatalytic activity and how the catalyst/support interactions impact activity and stability.

Student responsibilities/tasks:  

  • Preparation of spherical Pt nanoparticles.
  • Deposition onto novel support materials.
  • Catalysts will be tested electrochemically using cyclic voltammetry and electrochemical impedance spectroscopy.
  • These catalysts will also be characterized by a variety of techniques including X-ray diffraction, scanning electron microscopy and BET measurements.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

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

Expected training/skills to be received by the student:

  • Materials synthesis
  • Materials characterization
  • Electrochemical measurements
  • Analytical chemistry
  • Surface chemistry

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Christopher Collins, PhD
Project title:  Novel Selection Mechanisms for Pen-Enabled Computing

Summary of research project:  In this project we will be investigating the use of digital pens for selecting objects in applications. Pen selection often uses a 'lasso' approach to select all the objects enclosed inside an area. This project will investigate new methods such as a 'crossing' approach to select objects the pen touches, a 'pressure' approach to select objects underneath other objects by pressing harder with the pen and a 'selective' lasso approach, which selects only objects of the same type as the first one selected. This will allow efficient manipulation of objects in computer-aided design (CAD) applications, without requiring the user to switch between pen and other tools such as mouse/keyboard.

Student responsibilities/tasks:  Student will be responsible for conducting a thorough literature review of existing pen-based selection methods. The student will create new web-based designs of pen-based selection mechanisms (pen hardware will be provided). The student will assist the supervisor in designing a remote-deployment (web-based) study to test the new methods with real participants, under REB approval. The student will attend weekly lab meetings.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

  • Completed at least second year computer science (third year preferred).
  • Web development skills - familiar with Javascript/HTML/CSS.
  • Independent and excited to experiment with new ideas.

Expected training/skills to be received by the student:

  • Human-computer interaction theory and practice.
  • Empirical methods in computer science (experiment design).
  • Web development.
  • Interaction design.
  • Skills for reading, presenting, and discussing research papers.

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Dario Bonetta, PhD
Project title:  Genetic and genomic characterization of Cannabis sativa strains

Summary of research project:  The project will aim to better characterize the genotypic and phenotypic characteristics of Cannabis strains. We will be making use of gene panels to assess genotypic variation. The genetic information will be related to phenotypic properties such as growth characteristics and chemical profiles of terpenoids.

Student responsibilities/tasks:  The student will be responsible for growing, selecting and processing Cannabis plants. This will include performing crosses, extracting DNA, performing chemical analysis, conducting bioinformatic analysis of gene panel data.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

Knowledge in molecular biology techniques, a solid foundation in genetics, knowledge of bioinformatics is not required but a plus. Previous lab experience also a plus.

Expected training/skills to be received by the student:

  • Molecular biology techniques, bioinformatics.
  • Processing and analysis of chemical composition.
  • Horticultural techniques.
  • Genetics.

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Fedor Naumkin, PhD
Project title:  Field-assisted reactivity in highly polar supramolecular systems

Summary of research project:  Discovery of new molecular compounds and processes represent the very essence of frontier chemistry research.

The project will computationally investigate stability and properties of unique molecular complexes with extraordinary polarity, and involve molecular shape transformation and chemical bond pattern alteration in their internal electric field, which can be experimentally identified and monitored via observable spectra variations. 

Such systems are relevant for multiple applications based on light-matter interactions, intermolecular (self-)assembly, novel type of field-facilitated chemical reactions, molecular-level electronic devices and energy storage.

Student responsibilities/tasks:  

  • Explore and employ Computational Chemistry software (under guidance of supervisor), prepare input files, run calculations on available high-performance computing facilities (accessed via laptop).
  • Analyze and visualize results using molecular-graphics software (to learn as well), discuss them with supervisor.
  • Complete a literature search, prepare a report upon the project completion, prepare and deliver presentations at the Science Research Day, etc.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

  • MATH 1020 and PHYS 1020 completed with grade B- or higher.
  • CHEM 2010 completed with grade B- or higher.
  • Good command of Windows and Office (Word, Powerpoint, Excel).
  • Good skills in basic Geometry (positions and distances in xyz coordinates, etc.).

Expected training/skills to be received by the student:

  • Hands-on experience in high-level molecular modelling using modern professional quantum-chemistry software.
  • Practical skills in the prospective area of computational nanoscience, adding a competitive edge for future work placement. 

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Gregory Lewis, PhD
Project title:  Nonlinear Dynamics in the Atmosphere and Climate

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 how they induce transitions in the basic wind and temperature patterns. The models of interest are nonlinear differential equations, but are simple enough to allow for analysis using numerical approximation techniques.

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 atmosphere in order to study transitions in the basic temperature and wind patterns of the atmosphere.  Specifically, the student will work on his/her own project, which will be chosen to fit with the particular interests of the student.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

Must have completed at least two years of a mathematics or physics undergraduate degree or minor.

Expected training/skills to be received by the student:

Development of:

  • Analytical and problem solving skills.
  • Programming skills.
  • Independent study skills.

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Helene LeBlanc, PhD
Project title:  Forensic Entomology: olfactory and behavioural assays

Summary of research project:  As a cadaver decomposes, volatile organic compounds (VOCs) are released attracting various species of blowflies. Analysis of the volatiles will be conducted using the specialised EAG device, which utilises the sensitive olfactory system of the fly to measure electrical impulses, merged with a GC-MS to conduct coupled GC-EAG experiments. This technique allows for the quick isolation of active compounds within a complex sample of volatiles. Bioassay experiments using a wind-tunnel will be conducted to confirm the attraction of the blowflies to the active compounds. Research students will explore various techniques used in forensic entomology.

Student responsibilities/tasks:  The student will analyse VOC samples using gas chromatography & mass spectrometer (GC-MS) coupled with an electroantenogram (EAG) device and conduct bioassay experiments. Training will be provided. The student will also be responsible for maintaining fly colonies used for the EAG experiments and bioassay experiments. The student will be part of a research team, while still conducting an independent research project.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

Must have minimum B average. and entering 3rd or 4th year of undergraduate studies.

Most techniques will be learned in the lab; however, some qualities are more desirable:

  • Excellent manual dexterity.
  • Good understanding of chemistry.
  • Effective problem solving skills.
  • Good work ethic.
  • Basic understanding of entomology and decomposition.
  • Intro Physiology course.

Expected training/skills to be received by the student:

  • Gas chromatography & mass spectrometer (GC-MS)
  • Electroantennogram (EAG)
  • Behavioural assays using hybrid y-tube and wind tunnel

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Jane Breen, PhD
Project title:  Graph centrality measures and disease spread in a network

Summary of research project:  By considering a network of individuals and their connections with one another, we can model the spread of disease in a community as a stochastic process on a graph. Strategies for controlling the spread of disease may revolve around how 'central' an individual is in the community, but there is a wide range of ways to measure centrality in a graph. The research objectives for this project are:

  1. to simulate a variety of infection models on a network, and
  2. to compare and contrast the effectiveness of a variety of centrality metrics in each setting.

Student responsibilities/tasks:  The student will need to:

  1. Undertake some background reading to familiarize themselves with the concepts involved;
  2. Generate a variety of models for the contact network for a community;
  3. Run computer simulations for disease spread on these networks; and
  4. Compare and contrast appropriate centrality metrics and their effectiveness in control strategies.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

  • Minimum 3.0 GPA.
  • Some programming experience is preferred.
  • Discrete Math (MATH2080U) and Advanced Linear Algebra (2055U) preferred.

Expected training/skills to be received by the student:

  • Mathematical independence
  • Comfort with problem-solving
  • Developing new ideas
  • Testing conjectures
  • Scientific writing skills, both formal (writing reports) and informal (writing blog posts, etc.)

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Janice Strap, PhD
Project title:  Characterization of proteins that regulate bacterial cellulose biosynthesis

Summary of research project:  Komagataeibacter species synthesize cellulose, an extracellular biopolymer important for host-microbe interactions. While the addition of exogenous phytohormones affects the physicochemical properties of cellulose, the contribution of host-derived signals to the regulation of cellulose biosynthesis is not yet known. This project will investigate the role of two-component regulatory systems in response to host-derived signals and the regulation of cellulose biosynthesis through extensive genetic, biochemical and proteomic analyses.

Student responsibilities/tasks:  Techniques include: bacterial culture, sterilization, sample preparation, mutant generation, complementation and characterization, organic extractions, sample clean- up, analytical biochemical assays, gel electrophoresis, TLC, column chromatography, PCR & qRT-PCR. Students will be required to complete biosafety & WHMIS training. A written summary of all research activities is expected at the end of the training term.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

  • Applicants are expected to have successfully completed BIOL 3032, BIOL 3080 with a minimum grade of B+.
  • Preference will be given to students who have also successfully completed BIOL 3010, CHEM 2030 and CHEM 3830.
  • Applicants should be familiar with basic laboratory skills in biological science & chemistry.

Expected training/skills to be received by the student:

  • Experimental planning and organization.
  • Documentation of research activities.
  • Data analysis and interpretation.
  • Standard laboratory practices, maintenance and procedures.
  • Scientific communication (report writing and presentation).

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Jaroslaw Szlichta, PhD

Project title:  Guided Data Quality Rules Discovery via Machine Learning

Summary of research project:  Poor data quality is a serious issue for organizations causing operational inefficiencies. Data quality rules are used to identify errors. To ensure that the automatically discovered rules are desired, domain experts should be involved to verify their correctness. Our goal is to develop a guided rules discovery framework to blend user feedback (while minimizing participation). To rank rules, we will devise the utility measure to estimate the improvement in data quality. User feedback will be used to identify correctness to adaptively fine-tune the deep reinforcement learning model. The trained machine learning model can eventually supersede the task of deciding the correctness of rules.

Student responsibilities/tasks:  Students tasks will include:

  • review of related work in automatic discovery of data quality rules;
  • design machine learning driven approach to rank data quality rules;
  • implement the solution through a deep reinforcement model;
  • conduct comprehensive experimental evaluation over real-world datasets; and
  • write a research paper to be submitted to one of the top-tier conferences in data science, such as VLDB, ACM SIGMOD, IEEE ICDE or EDBT.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

Qualifications required for the student:

  • Complete the following courses before starting the project: Scientific Data Analysis (CSCI 2000U), and Database Systems and Concepts (CSCI 3030U) or Big Data Analytics (CSCI 4030).
  • Have strong programming and algorithmic skills.
  • The required GPA is >= B, however, the recommended one is >= A-.

 Expected training/skills to be received by the student:

  • Learning how to comprehensively review the related work.
  • Designing research framework to improve on the state-of-the art algorithms.
  • Implementing adaptive machine learning solutions.
  • Quantifying experimentally benefits of the proposed framework on real-world data.
  • Improving the communication and writing skills via weekly meetings/presentations and the conference paper submission.

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Jean-Paul Desaulniers, PhD
Project title:  Monitoring viral SARS-CoV-S from waste water from the Durham Region

Summary of research project:  This project proposes to monitor and identify the SARS-CoV-2 virus that causes COVID-19 from waste water. The student will identify and measure SARS-CoV-2 RNA and molecular biomarkers of health excreted by populations and use modelling to predict the special-temporal occurrence of emerging outbreaks in sewage wastewaters. Using a combination of quantitative real-time PCR, and proteomics, the student will work with our team (myself, Prof. Simmons, and Prof. Kirkwood). Our team will provide a tool for public health officials that delivers test results they need to implement appropriate short-term responses to COVID-19 community spread and long-term planning for disease prevention.

Student responsibilities/tasks:  

Using RNA extraction methods, the student will use qPCR-based methods for quantify the virus. The student must be studious and be qualified to perform aseptic procedures, including safe handling of biohazardous materials. Experience with laboratory equipment such as a high-speed centrifuge and working in a laminar flow-hood is an asset.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

  • Minimum grade: B+/A-.
  • Student in a biology program or the chemical biology program.
  • Completed 3rd year.
  • Driver's license an asset.

Expected training/skills to be received by the student:

  • RNA extraction from wastewater: The student will learn how to take raw sewage, and extract the RNA in a proper environment.
  • qPCR training: The student will learn how to setup and perform quantitative real-time PCR on the SARS-CoV-2 gene from the extracted RNA.
  • Data analysis: The student is expected to learn how to process the data, and work with the team on correlating active COVID-19 cases with the data obtained from the qPCR experiments.

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Jeremy Bradbury, PhD
Project title:  Automating Software Development Using AI

Summary of research project:  Software systems continue to grow in complexity and become more pervasive in our society. The growth of software complexity is mirrored by a growing challenge to maintain software development quality. To address this challenge, we will use artificial intelligence (AI), to automate a software development activity or provide an automatic recommendation to assist software developers. The successful student will focus on addressing a challenging software development task using an AI technique such as machine learning, deep learning or metaheuristic search (e.g. genetic algorithms). Automatic bug repair, test generation and refactoring of source code are all possible applications.

Student responsibilities/tasks:  The student will spend 4 weeks learning about automated software development and AI methods. During this time the student will read research papers and book chapters as well as program AI examples. A further 12 weeks will be spent creating a new automated software tool with the goal of improving software development practices.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

Ideally the student should be registered in Computing Science, Software Engineering or a related field.

Expected training/skills to be received by the student:

  • Use of AI libraries (e.g., PyTorch)
  • Collection of software development data from GitHub
  • Understanding of automation opportunities in software development

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Joseph MacMillan, PhD
Project title:  Black Hole Growth in Dark Matter Halos

Summary of research project:  Dark matter halos surrounding galaxies host supermassive black holes (BH). The growth of a seed BH affects the density at the centre of the the halo; this has previoulsy been studied under the assumption of adiabatic growth. This research project aims to expand the understanding of the role of BH growth by using N-body simulations to model the halo and BH together. The main objective will be to improve numerical resolution at the centre of the halo by using a new particle-mass scheme to better understand the role of the BH in those central regions.

Student responsibilities/tasks:  Student responsibilities include:

  • Adapting previous work in this field (e.g. updating numerical code to Python);
  • Researching and developing a new particle-mass scheme for N-body initial conditions;
  • Running and analysing simulations; and
  • Comparing results with previous work.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

  • Completed PHY 2040U Mechanics II.
  • Astronomy background is preferred (e.g., taken PHY 3900U Astronomy II).
  • Some coding experience preferred.

Expected training/skills to be received by the student:

  • Basic research: literature search, literature review.
  • Training on modern astrophysics simulation software (e.g., Gadget-2).
  • Programming skills: creating analysis software, creating and using libraries.

Award available:  Ontario Tech STAR Award


Supervisor name:  Lennaert van Veen, PhD
Project title:  Coloured noise in networks

Summary of research project:  Many physical systems can be modelled with networks. Examples include actual networks, like the World Wide Web, as well as the earth's climate. Nodes of the network correspond to states of the system. Transitions from one state to another can be random or deterministic.

This project will investigate what kinds of dynamics are observed on networks, depending on the transition rules and the structure of the network. Starting with simple networks and explicit computations, we will work our way up to more complicated structures that may require simulations and statistics. Along the way, we will encounter such techniques and tools as Markov chains, spectral analysis and stochastic processes.

Student responsibilities/tasks:  

  • Study network dynamics with tools of linear algebra and dynamical systems.
  • Design networks to yield particular behaviour like regime transitions and variability on long time scales.
  • Program simple simulations in Python/NumPy to explore ideas.
  • Study literature on network dynamics, Markov chains and stochastic processes.
  • Keep careful records of numerical experiments and maintain a repository for the simulation and analysis software.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

Completed:

  • At least two courses in each of the following areas: linear algebra, scientific computing/data analysis.
  • At least one course on differential equations.

A course in dynamical systems or an advanced course in scientific computing/numerical analysis would be a bonus.

Expected training/skills to be received by the student:

  • Gaining an understanding of network dynamics and Markov chains.
  • Writing Python/NumPy code for simulation and data analysis; maintaining a repository; visualizing data.
  • Conducting a literature review and presenting it both in writing and orally.

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Liliana Trevani, PhD
Project title:  Fabrication of nano-metal structures for plasmonic sensing of pharmaceutical drugs

Summary of research project:  The project is aimed to investigate the synthesis and characterization of supported metal nanomaterials in silica and carbon matrices for surface enhanced Raman scattering (SERS). The application of these nanostructures for the detection of low concentrations of target molecules (including pharmaceutical drugs) will be also investigated. The student will also gain experience in several analytical and physical/chemical characterization techniques.

Student responsibilities/tasks:  

The student will need to

  • Take safety training courses.
  • Perform synthetic work and learn new characterization techniques.
  • Participate in general laboratory activities (waste disposal, lab organization, etc.)
  • Meet regularly with the supervisors and participate in group meetings.
  • Prepare a final report with the guidance of the supervisors.
  • Deliver a final project presentation summarizing the main project results.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

Have completed the following with a Minimum grade: B

  • CHEM 2020U – Introduction to Organic Chemistry.
  • CHEM 2030U – Analytical Chemistry.
  • CHEM 2040U – Thermodynamics and Kinetics.

Expected training/skills to be received by the student:

  • Hands-on training on the synthesis and characterization of nanomaterials.
  • Training on UV-visible and Raman spectroscopy.
  • Training in new software tools for spectroscopic data collection and analysis.
  • Training in written and communication skills.
  • Critical data analysis.

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Mark Green, PhD
Project title:  Quantum Algorithms from Textbook to Applications

Summary of research project:  Two of the most popular quantum algorithms are Shor’s algorithm and Grover’s algorithm. These algorithms are described in all quantum computing textbooks along with toy problems as examples. Unfortunately, scaling these algorithms to larger examples and real-world problems can be quite difficult and sometimes these algorithms don’t behave as expected. The problems with scaling are not discussed in textbooks. The aim of this research project is to study these algorithms on larger problems to determine how they behave on closer to real-world problems. The main result of this research project will be a methodology for applying these algorithms to larger problems.

Student responsibilities/tasks:  The student will develop quantum computing programs using Python and Qiskit. They will study and summarize the research that has been done on Shor’s and Grover’s algorithms. They will develop examples that illustrate how these algorithms can be applied to interesting problems.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

  • The student must be familiar with Python programming and be comfortable with linear algebra and related mathematics.
  • The student should have a good grasp of program development and debugging.
  • Some knowledge of quantum computing will be useful, but isn’t necessary.

Expected training/skills to be received by the student:

The student will:

  • Develop skills in quantum computing.
  • Learn how to develop software for quantum computers.
  • Enhance their ability to solve problems and analyze results.
  • Become familiar with current research on quantum computing.

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Mehran Ebrahimi, PhD
Project title:  Machine Learning Techniques for Medical Image Processing

Summary of research project:  Several medical image processing techniques have been found to be useful for detection, diagnosis, and pre-surgical localization of tumours. The goal of this project is to extend and validate machine learning algorithms aimed at solving real-world inverse problems in the field of medical image processing. The research will be conducted at the Imaging Lab ( http://www.ImagingLab.ca ) in the Faculty of Science, Ontario Tech.

Student responsibilities/tasks:  The potential candidate will be responsible for utilizing and extending our current image processing, machine learning, and data visualization tools and algorithms in either Matlab or Python. The student will be engaged in literature review, mathematical modelling, programming, and validation of the results. In addition, the student is expected to produce scientific reports of the results in form of a poster and/or a conference paper.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

The student is required to have a good understanding of calculus and linear algebra. In addition, programming skills in Python or Matlab is required. Experience working with imaging data, machine learning, and optimization is desirable but not required. Students in Computer Science, Mathematics, Physics, or a related field are encouraged to apply.

Expected training/skills to be received by the student:

  • Image Pre-processing
  • Mathematical Modelling
  • Numerical Simulations
  • Data Analysis
  • Document/Report/Manuscript preparation

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Nisha Agarwal, PhD
Project title:  Development of pump-probe spectroscopy

Summary of research project:  Electronic transitions in different materials take place in picosecond (ps) and femtosecond (fs) time frames, also known as ultrafast processes. In order to understand and observe such fast transitions, high-energy and pulsed ps/fs lasers are required. Pump-Probe Spectroscopy (PPS) technique is used to observe these ultrafast processes. PPS essentially excites the electronic transitions in a material using a pump train and a delayed probe beam is used to measure the change in these transitions. Thus, novel photochromic polymer will be studied to understand their efficiency in switching cycles and polymer degradation over long periods of time.

Student responsibilities/tasks:  

  1. Understand the different optical elements and tools required to build pump-probe spectroscopy
  2. Design the optical bench for delay in pump and probe beam, make electronic connections with lock-in amplifier and display the output on an oscilloscope.
  3. Test the pump-probe technique with the provided photochromic polymer material and measure the electronic transitions.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

The student must be enrolled in the physics or electronics engineering or nuclear engineering program and should have at least completed 2 years of the program with an average GPA of 3 and above.

Expected training/skills to be received by the student:

  • The student will receive training in optical bench design.
  • They will receive health and safety training to work with Class 4 lasers.
  • They will be skilled in non-linear ultra fast spectroscopy.

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Olena Zenkina, PhD
Project title:  Molecularly Defined  Materials

Summary of research project:  EC materials are a class of “smart” materials that reversibly change their optical properties under an applied voltage. They are used in smart windows/mirrors, battery charge sensors, and electro-optic modulators. The project will target preparation of novel efficient EC materials by integration of conductive metal oxide nanosurfaces with a monolayer of the metal complex resulting in durable materials with high coloration efficiency. It will involve organic synthesis of ligands, preparation and characterization of metal complexes, creation of the nanostructured supports and integration of all components into smart devices.

Student responsibilities/tasks:  In addition to initial organic, organometallic and materials synthesis and characterization, the student will explore chemical and electrochemical switching properties, stability, conductivity and colour efficiency of these novel materials. The ultimate goal will be to create functional materials with high coloration efficiency, colour homogeneity, high contrast ratios, and controllable switching time.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

  • Preferably 3rd or 4th year chemistry student.
  • Research experience will be an asset.

Expected training/skills to be received by the student:

  • Inorganic synthesis
  • Organic synthesis
  • Materials preparation
  • Materials characterization
  • Performance testing using NMR, UV-Vis, Glovebox , 3D profilometry, ellipsometry, electrochemical techniques CV, EIS methodologies.
  • Manuscripts drafting/ report writing
  • Presentation skills
  • Scientific optimization of the materials, molecular units and supports

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Robert Bailey, PhD
Project title:  Characterizing Aquatic Ecosystems With Satellite Imagery

Summary of research project:  Multispectral satellite imagery has been collected for the last 50 years and is now freely available for research. This project will quantify spatial and temporal variation of these multi-spectral images in Trent-Severn lakes. Together with climate and other data, this will enable model-building to back-cast and then forecast the dynamics of plants and algae in these lakes.

Student responsibilities/tasks:  The student will learn to search for and download satellite images for the target areas and dates. They will then pre-process the images and prepare them for statistical analysis of spatial and temporal variation. Finally, they will execute the analysis.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

  • All required first year courses in any BSc program completed.
  • Skill using basic laptop/desktop apps (e.g. spreadsheets).
  • Openness to learn new software.

Expected training/skills to be received by the student:

  • Basic properties of Landsat satellite imagery and using the US Geological Survey (USGS) Earth Explorer tool for downloading images.
  • Basic pre-processing of images using QGIS software and associated plug-ins.
  • Quantify spatial and temporal variation in satellite images using R statistical software.
  • Communication skills for presenting the research.

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Theresa Stotesbury, PhD
Project title:  Rheological characterization of forensically relevant biological materials

Summary of research project:  In forensic investigations, biological materials are often collected and analyzed only for source identification (e.g. linking DNA to an individual). The how (mechanistic) and when (time since deposition) is often overlooked and can provide critical medico-legal information to these investigations. There is clear potential in using materials characterization to develop accurate and reliable techniques age and better characterize these biological samples. In this project, students will use rheology to identify key fluid properties of aging and degrading biological materials. In this project, biological samples include whole blood as well as DNA extracts from degrading bloodstains.

Student responsibilities/tasks:  

  • Operating technical equipment including, but not limited to: rotational rheometer, hematocrit centrifuge, SEM.
  • Developing novel rheological methods to analyze biofluids.
  • Conducting original experimental design, data acquisition and interpretation.
  • Solution preparation, fluid property measurements, wet-bench chemistry.
  • Following appropriate laboratory health & (bio)safety guidelines.
  • Disseminating results in research meetings.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

  • Those competent in forensic and analytical chemistry with an understanding of fluid dynamics, physics and statistics are strongly encouraged to apply (CHEM 2140/2300, PHY 3060).
  • Be an effective problem-solver and organized.
  • Work independently and as a team player.
  • Have strong scientific literacy (reading & writing).
  • Be enthusiastic and curious about science.

Expected training/skills to be received by the student:

  • Training on operating technical equipment including, but not limited to: rotational rheometer, hematocrit centrifuge, SEM.
  • Training on research design and developing novel experimental methods.
  • Refinement in scientific communication (reading, writing, speaking).

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Yuri Bolshan, PhD
Project title:  Novel reactions using ether as a reductant.

Summary of research project:  Recently, we discovered that diethyl ether acts as a hydride source. Common reducing agents including NBH4 and LiAlH4 are expensive. In contrast, diethyl ether is cheap and easy to handle. In my research laboratory you will investigate novel reactions, which use diethyl ether as a reductant.

Student responsibilities/tasks:  The student will learn how to set-up, follow and work-up chemical reactions. In addition, the student will be purifying synthetic intermediates using automated flash purification system. Finally, the student will characterize products using NMR.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

The student should:

  • Be a 2nd or a 3rd year student.
  • Be interested in conducting research, which means doing something nobody has ever done before.
  • Have good laboratory skills.

Expected training/skills to be received by the student:

  • Practical laboratory skills in real research environment.
  • Critical thinking: hypothesis and experiment design.
  • Communication skills.

Award available:  Ontario Tech STAR Award or NSERC USRA


Supervisor name:  Sean Forrester, PhD
Project title:  Characterization of the cys-loop receptor family from Dirolilaria immitis


Summary of research project: 
Dirofilaria immitis is a mosquito transmitted parasitic nematode that primarily infects dogs and is the causative agent of the debilitating disease called heartworm. For the past two decades the only preventative treatments for heartworm are drugs from the macrocyclic lactone family. However, there are now reports of resistance of this parasite to macrocyclic lactone drugs in certain regions. Therefore, there is a growing need for the development of new anthelmintics. The cys-loop family of receptors remains an attractive target for the development of future drugs. In this project the student will be isolating and characterizing cys-loop receptor genes from D. immitis.

Student responsibilities/tasks:

  • Cloning of cys-loop receptor genes.
  • Expression in Xenopus oocytes for functional examination.

Student qualifications required (e.g. courses completed, minimum grades, etc.):

  • Knowledge in molecular biology techniques.
  • Previous lab experience in a BSc program also a plus.

Expected training/skills to be received by the student:

  • Molecular biology techniques, bioinformatics.
  • 2-electode voltage clamp electrophysiology.
  • RNA synthesis.

Available Awards: Ontario Tech STAR Award or NSERC USRA