Optimization is ubiquitous in engineering and computer science. A recent application is the defeat of the world Go champion by the artificial intelligence algorithms executed in the Google?s AlphaGo system. Other application examples include deep learning network training, large data set clustering, and many engineering design problems.

Electromagnetic fields in dielectric and lossy media, transmission lines, antennas and resonators treated with the concepts of duality, image theory, reciprocity, integral equations and other techniques. Boundary and initial value problems solved for several frequently encountered symmetries.

Elementary probability theory including random variables, p.d.f., c.d.f., generating functions, law of large numbers. Elementary stochastic process theory including covariance and power spectral density. Markov processes and applications.

Elementary probability theory including random variables, p.d.f., c.d.f., generating functions, law of large numbers. Elementary stochastic process theory including covariance and power spectral density. Markov processes and applications.

Machine learning is employed in an increasingly wide range of applications. This course will cover two sides about machine learning. On one side, we will talk about recent systems research in machine learning, such as efficient training and inference, distributed and parallel learning systems, and debugging and profiling of ML applications. On the other side, we will discuss research in using machine learning for systems, e.g., identifying performance, reliability, and scalability issues.

Modern techniques for synthesis and verification of digital systems. Topics in synthesis cover high-level synthesis, decision diagrams, combinational and sequential logic optimization. Topics in verification include symbolic techniques, equivalence checking, satisfiability, FSM traversal and state reachability analysis. Open to graduate students and senior undergraduate students only. Prerequisites: undergraduate courses in digital logic design and hardware organization.

This course introduces the students to the design of embedded systems with a focus in unprecedented cyber-physical systems and internet of things applications. It takes a holistic approach to design end-to-end systems by addressing challenges at the hardware, software, and network layers of the stack. Special attention is paid to design trustworthy systems for applications running on commodity platforms and operating systems

This course provides an introduction to fundamental concepts in network security and data privacy. Network security topics covered include principles of cryptography, secure protocols such as TLS, and firewalls. We will also cover privacy topics including Web privacy and privacy-preserving data analytics such as differential privacy, homomorphic encryption, secure multiparty computation. Homework assignments involve programming, written tasks, and a final project.

Introduction to fundamental biological neuron models, algorithms and techniques for learning spatio-temporal patterns, and software frameworks for implementing spike-based computation. Understanding of the hardware foundation for bio-inspired computation from transistors and emerging devices to neuromorphic circuits and systems. Investigation of low-power, low-latency applications of neuromorphic engineering in machine vision, robotics, autonomous vehicles, etc.

This class covers the fundamental of the nanoelectronics discipline ranging from nanophysics, to nano structures and nanodevices. It provides first an overview of the fundamental physical principles required for understanding the electronic properties of matter at the nanoscale. From the basic description of quantum dots, wires and wells, we will review the main electrical property differences between atoms, molecules and nanostructures including Carbon nanotubes and Nanoribbons.