Teaching

MA 106 Linear Algebra: Spring 2019

• Under Prof. Neela Natraj and Prof. Krishnan S
• Mentored and tutored a division of over 50 first Year undergraduate students through weekly problem solving sessions
• Responsible for grading and related logistics

Course Content

Vectors in Rn , notion of linear independence and dependence, linear span of a set of vectors, vector subspaces of Rn , basis of a vector subspace. Systems of linear equations, matrices and Gauss elimination, row space,null space, and column space, rank of a matrix.Determinants and rank of a matrix in terms of determinants.Abstract vector spaces, linear transformations, matrix of a linear trans-formation, change of basis and similarity, rank-nullity theorem.Inner pro duct spaces, Gram-Schmidt pro cess, orthonormal bases, projections and least squares approximation.Eigenvalues and eigenvectors, characteristic polynomials, eigenvalues of special matrices ( orthogonal, unitary, hermitian, symmetric, skew-symmetric, normal). algebraic and geometric multiplicity, diagonalization by similarity transformations, spectral theorem for real symmetric matrices, application to quadratic forms.

MA 105 Calculus: Autumn 2019

• Under Prof. Sudhir Ghorpade
• Mentored and tutored a division of over 50 first Year undergraduate students through weekly problem solving sessions
• Responsible for doubt clearing, grading and related logistics

Course Content

Review of limits, continuity, differentiability. Mean value theorem, Taylor’s Theorem, Maxima and Minima. Riemann integrals, Fundamental theorem of Calculus, Improper integrals, applications to area, volume. Convergence of sequences and series, power series. Partial Derivatives, gradient and directional derivatives, chain rule, maxima and minima, Lagrange multipliers. Double and Triple integration, Jacobians and change of variables formula. Parametrization of curves and surfaces, vector Fields, line and surface integrals. Divergence and curl, Theorems of Green, Gauss, and Stokes.

EE 229 Signal Processing - I : Autumn 2020

• Under Prof. Animesh Kumar
• Responsible for grading and related logistics for the entire class of nearly 90 students
• Wrote scripts to automate aspects of the online grading and score sharing pipeline

Course Content

Continuous-time and discrete-time signals and systems, and their examples; Linear systems, linear time/shift invariant systems; Impulse response, convolution, and filtering; The Fourier transform; Fourier representations of continuous-time and discrete-time signals; Lowpass, bandpass, and high-pass systems; Stability and pole zero properties of linear shift invariant systems; Z-transform and Laplace transform; Sampling and reconstruction of bandlimited signals; Approximate reconstruction methods (zero-order hold); The discrete Fourier transform and the fast Fourier transform (FFT) algorithm; Implementation of discrete-time systems using FFT; Introduction to contemporary practice and examples.