My research area is in the realm of computational methods for data analytics. I use spectral graph theory, computational harmonic analysis, applied probability, and machine learning to design algorithms for extracting information from complex datasets.
APPM 3170 - Discrete Applied Mathematics
Primary Instructor
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Fall 2019
Introduces students to ideas and techniques from discrete mathematics that are widely used in science and engineering. Mathematical definitions and proofs are emphasized. Topics include formal logic notation, proof methods; set theory, relations; induction, well-ordering; algorithms, growth of functions and complexity; integer congruences; basic and advanced counting techniques, recurrences and elementary graph theory. Other selected topics may also be covered.
APPM 4580 - Statistical Learning
Primary Instructor
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Spring 2020
Consists of applications and methods of statistical learning. Reviews multiple linear regression and then covers classification, regularization, splines, tree-based methods, support vector machines, unsupervised learning and Gaussian process regression.
APPM 4720 - Open Topics in Applied Mathematics
Primary Instructor
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Fall 2019
Provides a vehicle for the development and presentation of new topics that may be incorporated into the core courses in applied mathematics. Department enforced prerequisite: variable, depending on the topic, see instructor. May be repeated up to 15 total credit hours. Same as APPM 5720.
APPM 5720 - Open Topics in Applied Mathematics
Primary Instructor
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Fall 2019
Provides a vehicle for the development and presentation of new topics that may be incorporated into the core courses in applied mathematics. Department enforced prerequisite: variable, depending on the topic, see instructor. May be repeated up to 6 total credit hours. Same as APPM 4720.
ECEN 2703 - Discrete Mathematics for Computer Engineers
Primary Instructor
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Fall 2019
Emphasizes elements of discrete mathematics appropriate for computer engineering. Topics: logic, proof techniques, algorithms, complexity, relations, and graph theory.
ECEN 4632 - Introduction to Digital Filtering
Primary Instructor
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Spring 2018 / Fall 2018 / Fall 2019
Covers both the analysis and design of FIR and IIR digital filters. Discusses implementations in both software and hardware. Emphasizes use of the FFT as an analysis tool. Includes examples in speech processing, noise canceling, and communications.
ECEN 5322 - Data and Network Science
Primary Instructor
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Spring 2019
The course covers the theory and design of algorithms that are used to model, analyze, and extract information from large scale datasets and networks. The course includes a project.
ECEN 5632 - Theory and Application of Digital Filtering
Primary Instructor
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Spring 2018 / Fall 2018 / Fall 2019
Digital signal processing and its applications are of interest to a wide variety of scientists and engineers. The course covers such topics as characterization of linear discrete-time circuits by unit pulse response, transfer functions, and difference equations, use of z-transforms and Fourier analysis, discrete Fourier transform and fast algorithms (FFT), design of finite and infinite impulse response filters, frequency transformations, study of optimized filters for deterministic signals.
STAT 5610 - Statistical Learning
Primary Instructor
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Spring 2020
Consists of applications and methods of statistical learning. Reviews multiple linear regression and then covers classification, regularization, splines, tree-based methods, support vector machines, unsupervised learning and Gaussian process regression. Same as STAT 4610.
