**Contact Info**

Department of Applied Mathematics

University of Waterloo

Waterloo, Ontario

Canada N2L 3G1

Phone: 519-888-4567, ext. 32700

Fax: 519-746-4319

PDF files require Adobe Acrobat Reader

Monday, May 6, 2019 — 2:30 PM EDT

MC 6460

Brydon Eastman | Applied Math, University of Waterloo

Machine Learning Techniques in Mathematical Oncology

Cell signaling in early tumour development is crucial to the geometry of the invading solid tumour. This complex process is impacted by numerous factors in the cellular micro-environment including the presence of cancerous stem cells, methods of cell signaling, and nutrient fields. All these factors influence the proliferation and invasive potential of a cancerous tumour. We propose, and investigate, a selection of models that utilise partial differential equations, stochastic processes, and machine learning in hybrid techniques. The processes proposed seek to use machine learning in the context of artificial neural networks to identify cancer stem cells via image flow cytometery, to act as decision making units in cellular automata simulations of cancer stem cells, and to perform sensitivity and bifurcation analysis on bio-systems in mathematical oncology.

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**Contact Info**

Department of Applied Mathematics

University of Waterloo

Waterloo, Ontario

Canada N2L 3G1

Phone: 519-888-4567, ext. 32700

Fax: 519-746-4319

PDF files require Adobe Acrobat Reader

University of Waterloo

University of Waterloo

43.471468

-80.544205

200 University Avenue West

Waterloo,
ON,
Canada
N2L 3G1