Global Arc

This course will introduce the matrix form of quantum mechanics and discuss the concepts underlying the theory of quantum information. Some of the important algorithms will be discussed, as well as physical systems which have been suggested for quantum computing. Prerequisite: Linear algebra at the level of MAT 202, 204, 217, or the equivalent.

Provides an opportunity for a student to concentrate on a "state-of-the-art" project in electrical engineering. Topics may be selected from suggestions by faculty members or proposed by the student. The final choice must be approved by the faculty member.

Provides an opportunity for a student to concentrate on a "state-of-the-art" project in electrical engineering. Topics may be selected from suggestions by faculty members or proposed by the student. The final choice must be approved by the faculty member.

Start by analyzing biological systems to understand the origins of some of the signals that they present. Develop circuit models of these systems to determine what instrumentation circuits are required at the interface so that the signals can be reliably acquired. Study analog circuit topologies based on MOSFETs for low-noise instrumentation and processing of the signals. Study digital topologies based on MOSFETs for extensive computations on the biological signals. Analyze the trade-offs between the analog and digital topologies. Emphasis is on design and analysis using circuit simulators.

Fossil fuel combustion is the largest contributor to anthropogenic pollutant emissions. Thus the energy security and the impact on climate change require cleaner transportation fuels. In this course a broad perspective ranging from the recent development of green fuels, through active combustion control and sensing using in-situ laser spectroscopic techniques, to global environmental impacts will be discussed. The course will cover both fundamentals such as physics of fuel combustion or light-matter interactions in combustion diagnostics as well as will review pollutant chemistry, transport, and the global standards of emission regulations.

The course covers basic theories of modern machine learning: 1. statistical learning theory: generalization, uniform convergence, Rademacher complexity, VC theory, reproducing Hilbert kernel space and their applications on simple classification/regression models; 2. optimization theory: gradient descent, stochastic gradient descent and their convergence analyses for convex functions, nonconvex functions 3. deep learning theory: basic approximation, optimization and generalization results for deep neural networks; 4. reinforcement learning theory: MDP, Bellman equations, planning, and sample complexity results for value iteration/Q-learning.

The course is an introduction to the theoretical foundations of machine learning. A variety of classical and recent results in machine learning and statistical analysis including: Bayesian classification, regression, regularization, sparse regression, support vector machines, kernels, neural networks and gradient descent.

An introduction to the properties of solids. Theory of free electrons--classical and quantum. Crystal structure and methods of determination. Electron energy levels in a crystal: weak potential and tight-binding limits. Classification of solids--metals, semiconductors, and insulators. Types of bonding and cohesion in crystals. Lattice dynamics, phonon spectra, and thermal properties of harmonic crystals. Prerequisite: 342, or PHY 208 and 305, or permission of instructor.

Electronic structure of solids. Electron dynamics and transport. Semiconductors and impurity states. Surfaces and interfaces. Dielectric properties of insulators. Electron-electron, electron-phonon, and phonon-phonon interactions. Anharmonic effects in crystals. Magnetism. Superconductivity. Alloys. Three hours of lectures. Prerequisites: 441 or equivalent.

The physics and technology of solid-state electronic devices. Covers electronic structure of semiconductors, energy bands and doping, followed by discussion of carrier transport by drift and diffusion and recombination/generation. Detailed analysis of p-n junctions, bipolar transistors and field effect transistors. Survey of a wide range of devices, including photodetectors, solar cells, light-emitting diodes and semiconductor lasers, highlighting contemporary concepts such as thin film electronics and 2D semiconductors.