Lab Coordinator, Boston University, Mathematics and Statistics, 2023
Elementary treatment of probability densities, means, variances, correlation, independence, the central limit theorem, confidence intervals, and p-values. Students will be able to answer questions such as how can a pollster use a sample to predict the uncertainty of an election?
Teaching Assistant, Boston University, Mathematics and Statistics, 2022
Elementary treatment of probability densities, means, variances, correlation, independence, the central limit theorem, confidence intervals, and p-values. Students will be able to answer questions such as how can a pollster use a sample to predict the uncertainty of an election?
Instructor, Boston University, Mathematics and Statistics, 2022
A first course in calculus. Topics covered include: limits, derivatives and their applications, and integration.
Teaching Assistant, Boston University, Mathematics and Statistics, 2022
Elementary treatment of probability densities, means, variances, correlation, independence, the central limit theorem, confidence intervals, and p-values. Students will be able to answer questions such as how can a pollster use a sample to predict the uncertainty of an election?
Teaching Assistant, Boston University, Mathematics and Statistics, 2021
Vectors, lines, planes. Multiple integration, cylindrical and spherical coordinates. Partial derivatives, directional derivatives, scalar and vector fields, the gradient, potentials, approximation, multivariate minimization, Stokes’s and related theorems.
Teaching Assistant, Boston University, Computer Science, 2020
Representation, analysis, techniques, and principles for manipulation of basic combinatoric structures used in computer science. Rigorous reasoning is emphasized.
Instructor, Boston University, Mathematics and Statistics, 2020
A course introducing various topics of discrete mathematics. Topics included an introduction to proofs, basic number theory, principles of counting, and modular arithmetic.
Teaching Assistant, Boston University, Computer Science, 2020
Introduction to basic probabilistic concepts and methods used in computer science. Develops an understanding of the crucial role played by randomness in computing, both as a powerful tool and as a challenge to confront and analyze. Emphasis on rigorous reasoning, analysis, and algorithmic thinking.
Teaching Assistant, Boston University, Computer Science, 2019
Introduction to basic probabilistic concepts and methods used in computer science. Develops an understanding of the crucial role played by randomness in computing, both as a powerful tool and as a challenge to confront and analyze. Emphasis on rigorous reasoning, analysis, and algorithmic thinking.
Instructor, Boston University, Mathematics and Statistics, 2019
A course in elementary linear algebra. Topics covered include: matrix algebra, Gaussian elimination, determinants, vector spaces, bases, and eigenvalues.
Instructor, Boston University, Mathematics and Statistics, 2019
A course in elementary linear algebra. Topics covered include: matrix algebra, Gaussian elimination, determinants, vector spaces, bases, and eigenvalues.
Teaching Assistant, Boston University, Mathematics and Statistics, 2019
First-order linear and separable equations. Second-order equations and first-order systems. Linear equations and linearization. Numerical and qualitative analysis. Laplace transforms. Applications and modeling of real phenomena throughout.
Teaching Assistant, Boston University, Mathematics and Statistics, 2018
Numerical and graphical summaries of univariate and bivariate data. Basic probability, random variables, binomial distribution, normal distribution. One- sample statistical inference for normal means and binomial probabilities. Primarily for students in the social sciences with limited mathematics preparation