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Faculty of Science Project Summaries

Supervisors  

Mehran Ebrahimi | Nisha Agarwal |

 

Supervisor name: Mehran Ebrahimi

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 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:

  • 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.

Length of award: 14 Weeks

Location of award: Hybrid

Available Award: NSERC USRA

 

Supervisor name: Nisha Agarwal

Project title: Quantum Treatment of Tip Enhanced Raman Scattering Modes

Summary of research project: Tip-Enhanced Raman Scattering combines scanning probe microscopy with surface-enhanced Raman scattering to analyze chemical processes at the nanoscale. A laser focused on a sharp, metallic probe tip generates a strong localized electromagnetic field (a "hotspot"). When this hotspot is brought close to a sample, it significantly enhances the Raman signal, allowing for chemical imaging with resolution down to angstroms. Our goal is to analyze the field of a laser beam around a metal-coated tip and study the associated skin effect and surface plasmons. This dynamics of a simple molecule in this field will be studied as an open quantum system to quantify the effect of thermal and quantum noise.

Student responsibilities/tasks:

  • The Student analyzes molecular structures and vibrational symmetries, builds a simplified Tip‑Enhanced Raman Scattering (TERS) lossy‑cavity model, and simulates polarization‑ and frequency‑dependent Raman contrast.

Student qualifications required:

  • Completed courses in Quantum Mechanics (QM) and Electromagnetism (EM).
  • Experience with Density Functional Theory (DFT) or molecular modeling.
  • Basic coding skills (Python or MATLAB).
  • Strong analytical and visualization skills.
  • Minimum A- average in physics courses.

Expected training/skills to be received by the Student:

  • Training in quantum‑chemistry modeling (normal modes, symmetry, Raman tensors) and visualization of molecular vibrations.
  • Development of a lossy‑cavity theoretical model to understand near‑field enhancement and polarization‑dependent Raman contrast.
  • Skills in data analysis, comparing theoretical predictions with experimental TERS measurements to interpret vibrational behavior at the nanoscale.

Length of award: 14 Weeks

Location of award: In-Person

Available Award: NSERC USRA