About
Evan Norris is a Ph.D. student in the Department of Physics at the University of Ottawa. His research primarily focuses on the solution of inverse problems using computational physics. his current work includes the development of a deep learning system for the prediction of scattered fields by arbitrarily shaped plasmonic nanoparticles and a computational microscopy framework for orientation imaging of collagen fibrils using second harmonic generation microscopy without interferometry. In the future his primary research directions include the application of deep learning algorithms for the solution of inverse problems and the further development of computational phase reconstruction systems.
Experience
Conducted research primarily in computational microscopy and deep learning. Development of tensorial ptychography algorithms, in the generation of numerical fibrillar collagen samples, development of numerical imaging diffraction toward the generation of second harmonic generation diffraction measurements, the development and testing of core ptychographic reconstruction algorithms, and development and testing angle retrieval algorithms. Development of machine learning models for inverse design in nanoplasmonics.
Primarily marked assignments and held office hours for student support in the following courses:
- PHY 3355: Statistical Thermodynamics (Winter 2023)
- PHY 4141/5341: Computational Physics II (Fall 2022)
- PHY 3355: Statistical Thermodynamics (Winter 2022)
- PHY 2390: Astronomy (Fall 2021)
- PHY 2325: Physics in Biology (Winter 2021)
- PHY 2311: Waves and Optics (Fall 2019)
Assisted in the development of computer software for the simulation of carbon nano-composites toward material conductivity research. Development of monte carlo optical scattering simulations for the study of optical transmission through aerosols and liquid suspensions.
Conducted atmospheric optics research, specifically in the development of optical scattering simulations using GPGPU programming