University of Washington

MSc. Applied and Computational Mathematics

Specialization:

Numerical Linear Algebra, Scientific Machine Learning.

University of Toronto

Honours B.Sc. in Mathematics & Applied Statistics

Relevant Coursework:

Mathematics: Algebraic Numbers (Independent Study under Prof. Marina Tvalvazade), General Topology, Real Analysis, Complex Variables,Partial Differential Equations, Number Theory, Mathematical Cryptography, Discrete Mathematics, Classical Geometries, Vector Calculus, Linear Algerbra, Groups and Symmetries, Mathematical Proofs.

Statistics: Probability and Statistics, Surveys and Sampling, Computational Statistics, Methods of Data Analysis, Experimental Design.

Computer Science:Machine Learning, Data Structures and Analysis, Software Design, Object-Oriented Programming, Algorithm Design & Complexity, Introduction to Computer Science

Fields Institute of Mathematics

Graduate Trainee

Studied Mathematical Neuroscience with focus on mathematical models of neurons and networks (Hodgkin-Huxley, Wilson-Cowan, Neural Fields) under Prof. John Griffiths

Skills

Programming Languages: Python, R, Java, SQL, Javascript

Developer Tools: Git/Github, Linux/Unix, Google Colab, Jupyter Notebook

Technologies/Framework: Latex, Markdown, Numpy, Pandas, Android Studio, JUnit, Agile/SCRUM, HTML/CSS, XML, Quarto

Technical Skills: Data Analysis, Bootstrapping, Jacknife Machine Learning, Statistical Modeling, Algorithm Design, Scientific Computing