Skip to main content

Faculty of Science project summaries

Supervisors

Nisha Rani Agarwal Greg Crawford Helene LeBlanc Janice Strap
Dario Bonetta Brad Easton Gregory Lewis Jaroslaw Szlichta
Jeremy Bradbury Mehran Ebrahimi Syed Qadri Liliana Trevani
Jane Breen Julia Green-Johnson Faisal Qureshi Lennaert van Veen
Christopher Collins Andrea Kirkwood Theresa Stotesbury Olena Zenkina

 

Summaries

Supervisor name: Nisha Rani 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:

  • Understand the different optical elements and tools required to build pump-probe spectroscopy.
  • 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.
  • 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, skills):

The student must be enrolled in the physics or electronics engineering program and should have completed at least two years of the program with an average GPA of 3.0 or greater.

 


Supervisor name: Dario Bonetta, PhD

Project title: Vibrational spectroscopy of cannabinoids

Summary of research project:  Analogous to how each person can be identified by their unique fingerprint, in materials science, every molecule can be identified by its exceptional signature which is in the form of vibrations. To detect these molecular vibrations, we require unprecedented techniques known as vibrational spectroscopy (VS) which encompasses two complementary techniques, that is, Raman and Infra-red (IR) spectroscopy, thus non-invasively chemical sensing the molecule which offers a great advantage over other invasive and labelling techniques. Thus, we will employ VS for detection of natural and synthetic cannabinoids for their use as breathalyzers. 

Student responsibilities/tasks:

  • Understand the physics behind Raman and Infra-red (IR) spectroscopy.
  • Acquire Raman and IR spectra of different cannabinoids like THC, CBD, CBZ and so on.
  • Assign Raman and IR spectral peaks to different functional groups.
  • Perform multivariate statistical analysis like Principal Component Analysis and k-means or hierarchical clustering on the datasets obtained.

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

The student must be enrolled in one of the undergraduate science programs and must have at least completed three years with a minimum GPA of 3.0. 

  


Supervisor name: Jeremy Bradbury, PhD

Project title: Using Machine Learning to Localize Bugs in Software

Summary of research project: Identifying and fixing buggy sections of code can be a challenging task. The aim of this summer research project is to assist software developers by producing a new machine learning (ML) tool for localizing bugs in source code. The project will investigate the benefits of different machine learning algorithms and will utilize open source data (e.g., GitHub). As long as the project involves the use of machine learning and focuses on improving software development it can be adapted to the interests of the student. If you would like more information, please email jeremy.bradbury@ontariotechu.ca.

Student responsibilities/tasks:

The student hired will spend one month learning about ML algorithms and researching existing bug localization tools. During this time the student will read research papers and books as well as try out different ML toolkits and libraries. A further three months will be spent creating a new bug localization tool using ML. The tool will be trained and evaluated on open source project data hosted on GitHub. Upon completion of the project the student will have gained experience with ML algorithms, open source software, bug localization techniques as well as software testing and debugging.

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

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

  


Supervisor name: Jane Breen, PhD

Project title: Connectivity measures of graphs and complex networks

Summary of research project:  Complex networks can be used to visualize a wide range of datasets (e.g., social networks, protein interaction networks, power grids, and the World Wide Web). There are a variety of ways to measure how well-connected these networks are. In this project we will compare a range of these metrics with a relatively new metric known as Kemeny's constant, which is related to a random walk process on the graph. Kemeny's constant can be used as a global measure of connectivity and as a determiner of clustering behaviour in our network.

Student responsibilities/tasks:

The student will be expected to undertake independent study of concepts in graph theory and linear algebra. The student will apply their understanding of these concepts to make and test conjectures regarding networks with certain properties and how those are reflected by the values of Kemeny's constant and other metrics. The student will generate data using Python or SageMath in order to test these conjectures for small networks, and will work on proving these conjectures, modifying the problem as needed

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

The student should have completed Math 2055U - Advanced Linear Algebra and Applications. Programming skills are desirable but not required.

 


Supervisor name: Christopher Collins, PhD

Project title: Hover and Tilt for Pen Interaction

Summary of research project:  Digital pens offer opportunities to achieve functions not possible with physical pens. Digital screens can sense the presence of the pen above the screen and pen tilt and movement can be tracked with onboard sensors. Existing software does not take appropriate advantage with these capabilities. This project will investigate the design space of memorable, error-preventing pen hover and tilt interaction for document annotation, creative applications, and productivity software. For example, a hover gesture could be used to change the pen mode from ink to highlighter, or the tilt of the pen could activate a selection mode.

Student responsibilities/tasks:

In this project the student will be responsible for reviewing existing digital pen research, in particular as it relates to hover and tilt of the pen. Techniques will be adapted and extended to the target scenarios. Specific steps will include: brainstorming about potential interaction designs, developing a software prototyping tool which can be dynamically adapted to experiment with interaction techniques; designing preliminary human-subjects evaluations of the system; writing a summary report; documenting all source code. The selected student will also attend weekly laboratory meetings and participate in research discussions.

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

Students ideally will have completed at least second year computer science or software engineering. Completion of third year is preferred. Software development will be in a high-level language (e.g., Python, Javascript) but the specific language is flexible. Specific interface design and implementation experience would be desirable but no experience needed.

  


Supervisor name: Greg Crawford, PhD

Project title: Using Satellite Data to Support Marine Environmental Monitoring, Conservation and Research in a Developing Country

Summary of research project:   Coiba National Park, Panama, is a critical habitat (~Galapagos) and a UN World Heritage Site, but as yet poorly understood. Dr. Crawford has begun work with colleagues there (coiba.pa.gov) to develop an ocean monitoring buoy to address this. An amazing opportunity exists to enhance the monitoring program using satellite-based data (e.g., ocean temperature). Project impacts include:

  • Insights on El Nino influence in the region.
  • Building the scientific and environmental conservation capacity for this developing country.
  • Establishing the value of satellite data there. Results will likely be presented at a conference in October 2020.

Student responsibilities/tasks:

  • Review available data sets and help develop recommendations for those sets on which to focus. Key sets include sea surface temperature (SST), surface winds, and ocean colour (e.g., chlorophyll concentration). Other data to consider includes sea surface altimetry (insights to ocean currents) and sea surface salinity (SSS). Recommendations will be based on time and space resolution, data reliability (e.g., patchiness), and accuracy and availability (past and future).
  • Acquire/archive recommended data.
  • Assess changes in SST (and other variables) in the region over the past decade; look for El Nino influences.
  • Additional tasks, depending on student's interests.

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

  • Third year standing in physics, math or computer science (data science specialization).
  • Programming experience in Python, Matlab or similar.

   


Supervisor name: Brad Easton, PhD

Project title: Nanostructured Pt electrocatalysts deposited on novel metal oxide supports

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. The project will seek to gain insights into the catalytic activity and electrochemical stability of these materials towards fuel cell relevant reactions.

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 Brunauer, Emmett and Teller (BET) measurements.

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

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

 


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, skills):

The student is required to have a good understanding of calculus and linear algebra. In addition, good programming skills in Matlab or Python is required. Experience working with medical imaging data, image processing background, machine learning, and numerical optimization is desirable but not required. Students with a background in computer science, applied and industrial mathematics, physics, or a related field are encouraged to apply.

 


Supervisor name: Julia Green-Johnson, PhD

Project title: Measuring the immune impact of gut microbial products and dietary interventions

Summary of research project: Gut microbes can influence the immune system through direct interaction and indirectly through secreted products and metabolites. These interactions are further influenced by dietary substrates, such as oligosaccharides and dairy products. This focus of this project will be to examine the impact of selected gut microbes, metabolites and dietary substrates on immune activity using cell culture and tissue cytokine based analyses.

Student responsibilities/tasks:

Responsibilities include planning and conducting experiments, analyzing and reporting results, keeping regular laboratory records, conducting scientific literature searches and preparation of summaries, and standard laboratory maintenance duties as assigned. Laboratory techniques include cell and bacterial culture and enumeration, media and reagent preparation and cytokine measurement using enzyme immunosorbent assays and related measures of immune impact. Completion of biosafety and WHMIS training, participation in lab meetings and preparation of written reports will be required.

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

Completion of BIOL 2030U, BIOL 2060U, BIOL 3032, and BIOL 4041 with a minimum B+ grade is required, and completion of BIOL 4031, 4060 and/or 4070 is an asset. Candidates should have laboratory skills in cell biology, aseptic technique and microbiology prior to undertaking the research project.

 


Supervisor name: Andrea Kirkwood, PhD

Project title: Intraspecific variation in zooplankton tolerance to road salt

Summary of research project:   Zooplankton are important members of aquatic food webs, serving as a critical energy link between phytoplankton and fish. With increasing inputs of road salt (NaCl) into freshwater ecosystems, zooplankton communities may be adversely effected. This project aims to collect and culture the zooplankton species Daphnia pulex-pulicaria from different lakes across Ontario to assess intraspecific variation in chloride tolerance. Findings from this research will improve our understanding of zooplankton adaptation and response to legacy road salt pollution in lakes.

Student responsibilities/tasks:

The student will work in the lab and field to collect and process samples. Tasks will include:

  • Field gear preparation.
  • Travel to lakes by car; sampling lakes by boat.
  • Lab preparation (glassware, acid washing, reagent/media prep, etc.).
  • Processing water samples (filtration, spectrophotometry, etc.).
  • Culture Daphnia.
  • Run bioassays with Daphnia.
  • Data input/analysis.

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

Third or fourth year students registered in environmental biology, environmental toxicology, or life sciences specializations will have taken the relevant courses suitable for this position.

Ideally, the student candidate would be prepared for field work that involves boating (i.e., comfortable on water and able to swim), and working in inclement weather. A drivers and/or boating license is an asset.

 


Supervisor name: Helene LeBlanc, PhD

Project title: Isolation and identification of active decomposition volatiles for carrion insects

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 specialized electroantennogram (EAG) device, which utilizes 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 analyze VOC samples using gas chromatography and mass spectrometer (GC-MS) coupled with an EAG device. 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, skills):

The student must have:

  • At least a B average and be entering their third or fourth year of undergraduate studies.
  • Completed courses should include CHEM 2130U - Analytical Chemistry for Biosciences or CHEM 2030U -Analytical Chemistry and BIOL 2010U - Introductory Physiology.
  • A basic understanding of entomology and vertebrate decomposition is a bonus.

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

  • Excellent manual dexterity in order to work with fragile equipment.
  • A good understanding of chemistry.
  • Effective problem solving skills.
  • Good work ethic.

 


Supervisor name: Gregory Lewis, PhD

Project title: Nonlinear Dynamics and 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 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 their own project, which will be chosen to fit with the particular interests of the student.

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

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

 


Supervisor name: Faisal Qureshi, PhD

Project title: Deep learning methods for activity recognition

Summary of research project:  The goal of this project is to explore deep learning theory and tools for action and activity recognition in single camera videos. Specifically, we will study computational attention models for action and activity localization and recognition. Here, localization refers to identifying the temporal extent of an action, and recognition refers to classifying an action into one of many classes. We will use standard benchmarks to compare the performance of our system against other state-of-the-art methods.

Student responsibilities/tasks:  

Familiarize yourself with action datasets, such as HMDB51 and UCF101. Perform a brief literature review (action and activity recognition using deep learning). Learn how to write video data loaders using deep learning frameworks for action/activity recognition. Learn how to use pre-trained deep networks. Learn how to modify, as needed, pre-trained deep networks. Learn how to deploy and train deep networks for GPU server class machines. Prepare a technical report. Prepare a presentation. Prepare a GitHub code repository. Take part in weekly meetings and group meetings. 

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

  •          Third year computer science or software engineering student.
  •          Second year linear algebra, statistics, and calculus.
  •          Some programming experience in a high-level language, such as Python or MATLAB (Python preferred).
  •          Experience with Python libraries, NumPy and SciPy, and deep learning frameworks, PyTorch, a plus.
  •          Motivation, and a stated interest in research and graduate studies.
  •          Interest in computer vision.

 


Supervisor name: Theresa Stotesbury, PhD

Project title: Identifying key chemical signatures of biological film degradation

Summary of research project:  In forensic science and national defense scenarios, 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. We can use spectroscopy (Raman, IR, mass spectrometry) to develop accurate and reliable techniques to age a sample. In this project students will use a combination of spectroscopy and statistics to identify key chemical signatures found on the surface of degrading bloodstains as well as those emitted into the headspace.

Student responsibilities/tasks:

This position is geared towards supporting research in a forensic chemistry. This student will work alongside a graduate student and learn critical skills in wet-bench and analytical chemistry.

Duties will include: 

  • Operating technical equipment including, but not limited to GC-MS, Raman, UV-Vis, and SEM.
  • Assisting with developing novel bloodstain sampling methods for experimentation.
  • Conducting original experimental design, data acquisition and interpretation.
  • Solution preparation, fluid property measurements, and wet bench chemistry.
  • Following appropriate laboratory health and safety guidelines.
  • Disseminating results in research meetings.

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

Those competent in wet-bench chemistry and have an excellent understanding of analytical chemistry, physics and statistics are strongly encouraged to apply.

A successful candidate should exhibit the following desirable skills:

  • Task-oriented with strong time management.
  • Effective problem-solver and highly organized.
  • Ability to work independently and as a team member.
  • Strong scientific literacy (reading and writing).
  • Creative and innovative.

Students entering their third or fourth year of their science degree would be preferable. Completion of CHEM 2140U - Analytical Chemistry for Biosciences or CHEM 2030U - Analytical Chemistry is not required but helpful.

 


Supervisor name: Janice Strap, PhD

Project title: Identification and 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:

Planning and carrying out experiments, documentation of laboratory activities, literature searches, data analysis, writing reports, and standard laboratory maintenance. 

Techniques include: bacterial culture, sterilization, sample preparation, mutant generation and characterization, organic extractions, sample clean- up, analytical biochemical assays, gel electrophoresis, TLC, column chromatography, PCR and qRT-PCR. 

Students will be required to complete biosafety and 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, skills):

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

 


Supervisor name: Jaroslaw Szlichta, PhD

Project title: Automatic Database Management Systems Knobs-Tuning

Summary of research project:  Tuning database management system knobs (configuration settings) such as size of buffer-pool or sort-heap with respect to the target application’s query workload and processing resources of the server, is critical for nowadays (cloud-based) applications that are complex, high-volume and high-throughput. Tuning database systems manually is a hard problem as modern database management systems, such IBM Db2 have hundreds of settings and many settings are dependent. We will develop large-scale deep learning models to predict the most desirable settings and that learn from past configurations to recommend more accurate ones in the future.

Student responsibilities/tasks:

Student tasks will include: 

  • Review related work.
  • Design machine learning driven approach to predict optimal configuration settings.
  • Implement the solution over IBM Db2 engine.
  • Conduct comprehensive experimental evaluation over synthetic and real datasets.
  • Write a research paper to be submitted to one of the top-tier conferences in data science, such as VLDB, ACM SIGMOD, IEEE ICDE and EDBT.  

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

Student should complete the following courses before starting the project: Scientific Data Analysis (CSCI 2000U) and Database Systems and Concepts (CSCI 3030U). Completing (or being enrolled) in Big Data Analytics (CSCI 4030) is recommended. Student should also have strong programming skills and understanding of design algorithms. The required GPA is ≥ B, however, the recommended one is ≥ A-.  

 


Supervisor name: Liliana Trevani, PhD

Project title: Exploring non-precious metal electrocatalyst materials for a sustainable future

Summary of research project:  The project is expected to expand ongoing research work on the synthesis and characterization of heteroatom-doped mesoporous carbons and non-precious metal catalyst materials. The main idea behind this initiative is to find novel materials that can boost the oxygen reduction reaction (ORR) kinetics by a 4-electrons mechanism for systems like metal-air batteries and fuel cells without the need for precious metal catalysts like Pt. Other applications in the fields of sustainable energy and environmental remediation would also be considered.

Student responsibilities/tasks:

The student will synthesize carbon materials using different experimental approaches. The main goal of this part of the project is to control the concentration and dispersion of dopants/metals on carbon. After the characterization of the products (surface chemistry and textural properties), electrochemical methods will be used to evaluate the catalytic activity and stability of materials under different experimental conditions (i.e. acid and alkaline media). The formation of chemical intermediates in the oxygen reduction reaction will be studied using a rotating ring disc electrode system.

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

Students are to have completed second year Chemistry and have a minimum overall grade of B. Other skills include:

  • Critical thinking.
  • Enthusiastic about learning new experimental methods and the evaluation of scientific results.
  • Able to work in a collaborative environment.
  • Good communication skills.

 


Supervisor name: Lennaert van Veen, PhD

Project title: The flip-flopping brain

Summary of research project:   An ongoing debate in neuroscience concerns slow oscillations in electrical signals generated by the human brain. One opinion is that these oscillations have the signature of "critical noise", a phenomenon also found in earth quakes and flickering neon lights. If true, this provides a way for the brain to generate large responses to small inputs quickly. An opposing view is that this signature is a consequence of measurement error only. In this project, we will use a hierarchy of EEG models to investigate the conjecture that critical noise is present in the brain if it has access to multiple stable states, i.e., is allowed to "flip-flop".

Student responsibilities/tasks:

The student working on this project will study a hierarchy of models of the human EEG, ranging from scalar stochastic differential equations to a system of coupled, nonlinear differential equations. The analysis will involve both theoretical and numerical work. All programming will be done in Python/Numpy.

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

  • Some familiarity with the following topics is expected: ordinary differential equations, Fourier transforms, and elementary statistics.
  • Required: basic calculus and linear algebra, Python programming.
  • Bonus: software project management, dynamical systems, computational science.

 


Supervisor name: Olena Zenkina, PhD

Project title: "Smart" Molecularly Defined Materials: Electrochromic (EC) films for energy storage.

Summary of research project:  Electrochromic (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, 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. Students who advance this research will learn inorganic and organic synthesis, methods for materials preparation and characterization and main strategies for studying and optimization of metal-ligand interactions on the surface support.

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

Preferably a third or fourth year chemistry student. Research experience will be an asset but not the main requirement.