Online Master of Science in Electrical and Computer Engineering

Learn from Experts with Diverse Backgrounds

Kenneth Barner, Ph.D.

Professor and Chairman, Department of Electrical and Computer Engineering
Prof. Barner’s research interests focus on signal and image processing as well as human-computer interaction. The processing of signals is fundamental to applications in a broad array of disciplines, from biology and medicine, to communications and imaging. Traditional signal processing methods employ linear techniques. However, real-world systems and signals often exhibit nonlinearities. Moreover, linear methods break down in the presence of harsh environments, such as those characterized by impulsive, or heavy tailed distributions. Thus current work focuses on developing, analyzing, and employing nonlinear signal processing methods that are robust to harsh environments and are able to exploit system nonlinearities. Traditional signal processing methods also rely on Nyquist approaches to sampling and processing signals. This approach breaks down, or is inefficient, in real world cases, particularly those with multispectral components. Moreover, traditional approaches fail to exploit the sparsity inherent in many problems. Thus current work also leads and capitalizes on the growing body of sparsity-based approaches to sampling, processing, and detecting signals of interest. Sparsity and nonlinearity have been exploited in multiple theoretical and application focused projects, including the processing of biomedical signals, such as EEG and ECG signals and tomographic images for compression, feature extraction, and enhancement. Imaging, including stereo imaging, has been utilized for facial recognition, gait recognition, and human computer interface strategies, including those on mobile platforms.

Stephan Bohacek, Ph.D.

Associate Professor
Prof. Bohacek currently focuses on the two related areas of enterprise computing and networking. In both areas, he investigates the modeling, analysis, and prediction of performance. In enterprise computing, Prof. Bohacek researches the performance of different applications that run on private and public clouds, focusing on understanding the trade-off between cost and performance for different types of services where performance is measured as response time or durability of data. This work considers different approaches for collecting measurements from servers and various applications, such as database, web serving, web application, and big data applications such as Hadoop. In networking, Prof. Bohacek studies mobile ad hoc wireless networks (MANETs). These networks are composed of many wireless devices, such as mobile phones, laptops, vehicle-based radios, and stationary radios (such as radios mounted on lampposts). Mobility of the radios and interference between multiple radios introduce many complications when designing protocols and understanding radio performance. Besides MANETs, Prof. Bohacek is interested in wireline networking, such as transport (e.g., TCP) and routing. In all of these research topics, he utilizes, in equal parts, theory (e.g., optimization and probability) and hands-on development (e.g., kernel hacking). Prof. Bohacek’s work is based on data collected from a wide range of companies and live experiments. His research papers usually include a significant amount of mathematical analysis.

Charles Boncelet, Ph.D.

Professor and Associate Chair for Undergraduate Studies
Prof. Boncelet’s research interests lie in the areas of image and signal processing, data compression, information security, and steganography and steganalysis.

Boncelet has worked for two decades on signal and image processing and has invented several new filtering methods. This work has led to better noise filtering and edge enhancement, among other advantages. He has worked in data compression, especially image compression, for almost as long. Major results include new quadtree image compression methods, block arithmetic coding, new algorithms for the compression of binary images (i.e., facsimile images), and new implementations of context tree weighting.

Recently, Boncelet has been working in the area of information security, especially message authentication and data hiding. He has invented a series of message authentication codes that tolerate a small number of errors, yet still provide security. In data hiding, he is a co-inventor of a wavelet based watermarking method and a spread spectrum based steganographic method.

Current work also include steganalysis, the search for hidden messages in multimedia data (e.g., images and videos, as might be found on the internet) and development of watermarking for multi-level security. In addition, Boncelet has a general interest in prediction algorithms, such as for the stock market and sporting events.

Boncelet regularly consults as an expert witness on patent litigation in the areas of signal and image processing, data compression, information security, and wireless communications.

Boncelet holds a joint appointment in Computer and Information Sciences. He is a member of the IEEE, SIAM, ASEE, and Delaware Academy of Science. He is a past president of the University of Delaware Faculty Senate.

Chase Cotton, Ph.D.

Chase Cotton (Ph.D. EE, UD, 1984; BS ME, UT Austin, 1975) is a successful researcher, carrier executive, product manager, consultant, and educator for the technologies used in Internet and data services in the carrier environment for over 30 years.

Beginning in the mid-80’s Dr. Cotton’s communications research in Bellcore’s Applied Research Area involved creating new algorithms and methods in bridging, multicast, many forms of packet-based applications including voice & video, traffic monitoring, transport protocols, custom VLSI for communications (protocol engines and Content Addressable Memories), and Gigabit networking. In the mid-90’s as the commercial Internet began to blossom, he transitioned to assist carriers worldwide as they started their Internet businesses including Internet Service Providers (ISPs), hosting and web services, and the first large scale commercial deployment of Digital Subscriber Line (DSL) for consumer broadband services. In 2000, Dr. Cotton assumed research, planning, and engineering for Sprint’s global Tier 1 Internet provider, SprintLink, expanding and evolving the network significantly during his 8 year tenure. At Sprint his activities include leading a team that enabled infrastructure for the first large scale collection and analysis of Tier 1 backbone traffic, and twice set the Internet 2 Land Speed World Record on a commercial production network.

Since 2008, Dr. Cotton has been at the University of Delaware in the Department of Electrical and Computer Engineering, initially as a visiting scholar, and later as a Senior Scientist, Professor of Practice, and Director of Delaware’s Center for Information and Communications Sciences (CICS). His research interests include cybersecurity and high-availability software systems with funding drawn from the NSF, ARL, CERDEC, JPMorgan Chase, and other industrial sponsors. He currently is involved in the educational launch of a multi-faceted Cybersecurity initiative at UD where he is developing new security courses and degree programs including a minor and MS in Cybersecurity.

Dr. Cotton currently consults on communications and Internet architectures for many carriers and equipment vendors worldwide.

Hui Fang, Ph.D.

Assistant Professor
Prof. Fang’s research interests focus on text information management, especially in the areas of information retrieval (also known as search engine technology), text mining and bioinformatics. The main goal is to overcome the information overload problem and help users access and make use of the various kinds of text information, such as web pages, emails, instant messages, literature, blogs, etc. The information is being continuously produced everywhere in the world in every possible way. Such a huge amount of information overwhelms users, and poses significant challenges in text information management. Clearly, it becomes increasingly important to manage large amounts of information so that users can access useful information and discover interesting knowledge efficiently and effectively. Prof. Fang is studying a novel task aware information retrieval framework, which allows users to precisely formulate their queries in the context of information tasks and systematically exploit the task information in the retrieval decision. She is also interested in developing optimal retrieval functions and understanding what hinders the performance of existing retrieval models. Moreover, she is also actively collaborating with domain experts including journalists and biologists and developing effective techniques and systems that could help them discover interesting knowledge from domain-specific information.

Kenneth Jay Lutz, Ph.D.

Adjunct Professor
Prof. Lutz developed and is teaching a course on the Smart Grid at the University of Delaware. He has had decades of experience in energy, telecommunications, and public policy. For most of his career, Prof. Lutz was a Distinguished Member of the Technical Staff at Telcordia Technologies (formerly Bell Communications Research) and at Bell Telephone Laboratories before then. In 2009 Dr. Lutz was awarded an IEEE/AAAS Congressional Fellowship and worked for United States Senator Ron Wyden, where he was instrumental in writing federal legislation for renewable energy and energy efficiency. He then founded AMR Strategies LLC to help utilities modernize their grids using smart grid technologies, renewable energy sources, energy storage, and other technological improvements. For the past two summers he served as the faculty-member-in-residence for the Washington Internship for Students of Engineering (WISE) in Washington, DC.

Prof. Lutz’s research interests focus on the smart grid as a complex system of systems that integrate power systems, communications systems, and information technologies to create a modern electrical infrastructure to supply reliable power. New electric-grid technologies, including renewables, energy storage, distributed generation, microgrids, and demand-response, need to be incorporated into the electric grid seamlessly, requiring new grid sensors, new control elements, and secure communications. Some of the most important technological challenges include developing effective grid-control algorithms, processing huge amounts of data, and ensuring that the supply of electricity is secure. The implementation of a smart grid presents several other engineering challenges. Utilities need to find ways to build and operate smart grids and deal with major changes in the grid, such as two-way power flow, distributed generation, and electric vehicles. Another challenge is to remove regulatory barriers to the smart grid at both the state and federal levels. A successful smart grid deployment requires a holistic systems engineering approach that encompasses all aspects of technology, business, and public policy.

Michael Piovoso, Ph.D.

Dr. Piovoso is a Professor of Electrical Engineering with Penn State University School of Graduate Professional Studies in Malvern, PA. He has a PhD in Electrical Engineering from the University of Delaware. Prior to joining Penn State, Dr. Piovoso spent nearly 33 years in the DuPont Company Central Research Department. His work at DuPont was mainly in the areas of the application of multivariate methods to improved process understanding and control, neural networks, expert systems, process control and fault detection and identification. In 1999, Dr. Piovoso won the IEEE Control Systems Technology Award for his contributions to the application of multivariate statistics to process control. At Penn State, Dr. Piovoso has won teaching, research and service awards. Since joining Penn State, besides continued work in process systems, Dr. Piovoso has recently extended his research interest into system biology, in particular HIV. Dr. Piovoso have over 100 peer reviewed research papers and 8 patents. He is the subject editor in process systems for Chemical Engineering Research and Design, an Elsevier journal.