Graduate (List)

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.

This course provides theory and practice for modeling and simulation of discrete event systems which include communication networks, manufacturing systems, and high-level computer systems. Topics include system taxonomy and discrete event systems (DES) characteristics; three entities in modeling and simulation; model representation and formalism construction; DEVS (Discrete Event systems Specification) formalism and DES modeling; simulation algorithm for DES; Petri Net modeling and analysis; statistics for modeling, simulation and analysis; model validation; output analysis and performance evaluation; advanced topics in DES modeling and simulation.

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.

Many of key technique now being applied in building services and service-based applications were developed in the areas of databases, distributed computing, and multiagent systems. These are generally established bodies of work that can be readily adapted for service composition. Lecture on service-oriented computing will cover the principles and practice of service-oriented computing. Especially, it introduces architecture, theories, techniques, standards, and infrastructure necessary for employing services.

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)