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Program of Study

Christian Cunningham

Advisor: Bo Sun

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Contextualizing My Program

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Latent Representation of Breast Cancer Morphologies

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Latent Representation of Breast Cancer Morphologies

Adversarial Autoencoder Network

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Latent Representation of Breast Cancer Morphologies

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Latent Representation of Breast Cancer Morphologies

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Latent Representation of Breast Cancer Morphologies

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Core Courses

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9 Core Courses - All Finished

  • Quantum Mechanics (I, II, III)
  • Mathematical Physics
  • Electricity and Magnetism (I, II)
  • Dynamics Single and Multiple Particles
  • Statistical Mechanics (I, II)

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Class Notes can be found at: https://christiancunningham.xyz/wiki/

Or at a shorter URL: https://ccrl.dev/wiki/

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Additional Courses

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(MTH524/5) Dynamical Systems I & II - Completed

524 introduces the theory of dynamical systems, focusing on concepts like:

  • Discrete Dynamical Systems
  • Transitivity
  • Topological Ergodicity
  • Invariant/ Periodic Points
    • Crucial for identifying effective potentials

525 explores advanced topics such as:

  • Measure Preservation
  • Measure-based Ergodicity
  • Limit behaviors
  • Embeddings
  • Conjugacies
  • Mappings
    • Crucial for understanding embeddings of higher dimensional processes such as random walks to lower dimensions

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(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:

  • Analyzing stochastic processes
  • Focused on tools to analyze bulk behaviors
    • Phase Diagrams, SEIR modeling, etc.

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(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.

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(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

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Additional Physics Department Requirements

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Pedagogy Training

DEI Training

RCR Training

  • TA Seminar (PH607-3)
  • MTH-607 Building Diverse, Inclusive, Respectful and Welcoming Mathematics Communities
  • Research Seminar (PH607-4) + CITI Trainings
  • I have also taken GRAD520

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Blanket Courses

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Masters Ph.D.

  • [5/5] Non-thesis Research Credits
  • [3/3] Research Credits
  • [0/1] DEI Seminar Credit
  • [0/51] Thesis Credits
  • [10/15] Research Credits

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Alternative Courses

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(PH564) Scientific Computing II

Mathematical, numerical, and conceptual elements forming foundations of scientific computing:

  • Computer hardware
  • Algorithms
  • Precision
  • Efficiency
  • Verification
  • Numerical analysis
  • Algorithm scaling
  • Profiling
  • Tuning

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(CS513) Applied Machine Learning

Explores Machine Learning Basics and Applications such as Classifications.

  • K-means
  • Linear + Non-linear classifications
  • Sentiment Analysis

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(PH582) Optical Electronic Systems

Similarly with the AMO Modern Optics, this course would better equip me to understand how our data collection systems function

  • Photodetectors
  • Laser theory
  • Laser systems

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(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

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(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

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(PH515) Computer Interfacing and Instrumentation

Our microscopes use computer interfacings, making this a potentially useful breadth course for my program