Program of Study
Christian Cunningham
Advisor: Bo Sun
Contextualizing My Program
Latent Representation of Breast Cancer Morphologies
Latent Representation of Breast Cancer Morphologies
Adversarial Autoencoder Network
Latent Representation of Breast Cancer Morphologies
Latent Representation of Breast Cancer Morphologies
Latent Representation of Breast Cancer Morphologies
Core Courses
9 Core Courses - All Finished
Class Notes can be found at: https://christiancunningham.xyz/wiki/
Or at a shorter URL: https://ccrl.dev/wiki/
Additional Courses
(MTH524/5) Dynamical Systems I & II - Completed
524 introduces the theory of dynamical systems, focusing on concepts like:
525 explores advanced topics such as:
(VMB631) Mathematical Modeling of Biological Systems
Currently taking
Mathematical Modeling in Biological Systems is useful for developing quantitative models of cancer cell behavior. This course emphasizes:
(PH581) AMO Physics, Modern Optics
While seemingly broader, the study of Atoms, Molecules, and Optics is relevant as it covers key concepts like Maxwell’s equations in matter and material dispersion. These principles are crucial for techniques such as optical imaging and spectroscopy, which are used to observe and analyze cancer cell structures and dynamics at microscopic scales.
(PH591) Biological Physics
Biological Physics is essential for understanding the basic physics principles that govern the kinetics and dynamics of molecular and cellular processes in cancer cells. This class provides foundational knowledge necessary for exploring how physical forces and molecular interactions drive changes in cell morphology and behavior
Additional Physics Department Requirements
Pedagogy Training
DEI Training
RCR Training
Blanket Courses
Masters Ph.D.
Alternative Courses
(PH564) Scientific Computing II
Mathematical, numerical, and conceptual elements forming foundations of scientific computing:
(CS513) Applied Machine Learning
Explores Machine Learning Basics and Applications such as Classifications.
(PH582) Optical Electronic Systems
Similarly with the AMO Modern Optics, this course would better equip me to understand how our data collection systems function
(CS546) Networks In Computational Biology
Emphasizes computational and applied mathematical methods for modeling and analyzing biological networks. Covers various network centralities, topological measures, clustering algorithms, probabilistic annotation models and inference methods. Introduces those concepts in the context of protein interaction, gene regulatory, and metabolic networks. Uses graph frameworks, data frames (and related data structures for data science), and programming in Python or R
(CS553) Scientific Visualization
Applies 3D computer graphics methods to visually understand scientific and engineering data. Methods include hyperbolic projections; mapping scalar values to color spaces; data visualization using range sliders; scalar visualization (point clouds, cutting planes, contour plots, isosurfaces); vector visualization (arrow clouds, particle advection, streamlines); terrain visualization; Delauney triangulation; and volume visualization
(PH515) Computer Interfacing and Instrumentation
Our microscopes use computer interfacings, making this a potentially useful breadth course for my program