Basic fundamentals related to nanoscale phenomena will be first introduced, followed by seminar-like lectures covering some general nanotechnology/nanoscience areas and latest development.

This course introduces concepts, techniques, and algorithms from artificial intelligence and machine learning, such as classification, regression, support vector machines, decision trees, and neural networks, as well as deep neural networks. The application of these techniques to solve practical problems is a focus of this course. Project assignments are used to reinforce concepts learned in lectures.

Not available at this time

Theory and techniques used in the design of modern microwave and millimeter wave active circuits. Emphasis on amplifier and oscillator circuits using devices such as FETs, HEMTs, HBTs and optoelectronic devices. Modern reference material used as much as possible.

To explore concepts related to the design, analysis, and construction of microwave systems. This course will discuss the fundamental tradeoffs governing system design: the hardware components and technologies that comprise working systems, the models used for characterizing the transmission and reception of signals, the physics of wave propagation and interaction, and estimation theory which seeks to separate signals from sources of error and guide algorithms for extracting information from received signals.

This course will introduce students to the area of green computing. The course will cover emerging problems associated with the rapid growth of energy consumption in modern computing infrastructures, i.e. data centers, and discuss new research focused on mitigating these problems. The course will also cover ways to leverage computation, networking, and sensing to improve the energy-efficiency of the electric grid, e.g. by automatically regulating energy consumption in buildings, homes, etc.

Fundamental concepts and systems aspects of computer networks. Topics include a review of the layered Internet architecture and encompass router design, lookup and classification algorithms, scheduling algorithms, congestion control, wireless protocols, and network security. The goal of the course is to explore the key technical and research questions in computer networks as well as to convey the necessary analytical, simulation, and measurement techniques.

A graduate version of E&C-Eng 570 which includes additional readings from research papers on embedded operating systems concepts,

A graduate course in computer algorithms. Includes algorithms for sorting, optimization, scheduling, and data management; graph algorithms; random algorithms; NP-completeness. Prerequisites include close familiarity with data structures, probability theory, and linear algebra.

A graduate course in computer algorithms. Includes algorithms for sorting, optimization, scheduling, and data management; graph algorithms; random algorithms; NP-completeness. Prerequisites include close familiarity with data structures, probability theory, and linear algebra.