Graduate (List)

This course covers topics of interest in Electrical and Computer Engineering at the graduate level. The course content is specifically designed by the instructor.

In this advanced research course, we study the various aspects that enable mobile ubiquitous computing; network architectures, cloud computing, on-device machine learning, wearables, applications, security, privacy, and interaction methods. We will read and present seminal and state-of-the-art papers on each topic. Students will also design and demonstrate class projects in mobile applications or IoT services.

Mobile computing and wireless networking has become an essential part of our daily lives. In this advanced, research oriented course, we study the various research aspects that enable intelligent mobile computing; wireless connectivity, network architectures, protocols, systems, services, applications, security, privacy, and even machine learning. Students will design class projects that develop interesting mobile applications or IoT services. EE323 and EE202 are strongly encouraged, but are not required prerequisites. Proficiency in programming is expected.

This course deals with security issues related to hardware computer architecture and microarchitecture. Specifically, students will learn the fundamental knowledge of recent microarchitectural vulnerabilities and explore various attacks and defenses on CPU, memory, and interconnect architectures.

Distributed computing systems have become pervasive. From clusters to internet-worked computers, to mobile machines, distributed systems are being used to support a wide variety of applications. This course introduces key concepts and techniques underlying the design and engineering of distributed computing systems. The following are the objectives of this course:
– In-depth understanding of core concepts of distributed computing.
– Construction of applications and supporting system components by doing project work.

This course covers advanced research topics in Big data – AI Integration, which is at the intersection of data management, machine learning, and systems. Each week, students will take turns presenting recent papers in each topic in a seminar format or submit short reviews. Students will also write surveys on recent papers of interest and implement state-of-the-art methods based on them.

This course covers advanced research topics in computer networking and cloud computing. The course is designed to cover various topics in the broad areas of computer systems, networking, cloud and mobile computing, including issues such as wide-area networking, congestion control, data center networking, software-defined networking, network functions virtualization, distributed systems, systems for machine learning, and data intensive computing.

The focus of this course is to understand the mathematical foundations of this methodology in light of the convergence, degree of suboptimality, computational complexity and sample efficiency of different algorithms.

This course is the advanced course dealing with methods for correcting and detecting errors in data and covers finite field theory, cyclic code, BCH code, Reed-Solomon code, convolutional code, trellis-coded modulation, turbo code, LDPC code, space-time code, and adaptive coding. (Prerequisite: EE522, EE528)

The purpose of this course is to provide the fundamental background behind detection and estimation theories based on likelihood functions as well as on Bayesian principles. Topics to be covered are decision theory, hypothesis testing, performance analysis, detection and estimation from waveform observation, linear and nonlinear parameter estimations. (Prerequisite: EE528 recommended)