Modern computer laboratory facilities are essential in preparing students for professional positions in the world of computer science and software engineering practice and research. CIS department facilities include the Computer Projects Lab, the Game and Multimedia Environments (GAME) Lab, the Database and Multimedia Systems Lab, the Research Laboratory for Sustainable Systems (RLSS), the Security and Forensics Research Lab (SAFE), the Vehicular Networking Systems Research Lab, the Virtual Engineering Laboratory (VEL), and the Wearable Sensing and Signal Processing Laboratory (WSSP).
Computer Projects Lab
Database and Multimedia Systems Laboratory (DMS) (Drs. Qiang Zhu, William Grosky, and Brahim Medjahed)
The Database Research Group in the Department of Computer and Information Science at the University of Michigan - Dearborn has strong faculty members who are active in the database area. They conduct innovative research in database management systems and related areas. Their research projects have been funded by various federal and industrial sponsors including the U.S. National Science Foundation (NSF), the U.S. National Institutes of Health (NIH), Michigan Life Science Corridor, IBM Corporation, and Ford Motor Company. The faculty members in the group have published research results in refereed quality journals and conference proceedings including ACM Transactions on Database Systems, ACM Transactions on Information Systems, IEEE Transactions on Data and Knowledge Engineering, IEEE Transactions on Multimedia, the VLDB Journal, Information Systems, Data and Knowledge Engineering, Distributed and Parallel Databases, Pattern Recognition, International Conference on Very Large Data Bases (VLDB), IEEE International Conference on Data Engineering (ICDE), and IEEE International Conference on Multimedia Computing and Systems (ICMCS). They actively participate in professional activities including serving on the editorial boards for various technical journals and magazines (e.g., IEEE Multimedia, Multimedia Tools and Applications, International Journal of Semantic Computing) and serving as program/organizing committee members/chairs for numerious international conferences (e.g., ICDE, DEXA, WISE, WAIM, ACM Multimedia, etc). Learn more.
The Learning and Uncertainty in Intelligent Systems Lab seeks to
advance our fundamental knowledge about both the theory and
practice of artificial intelligence (AI) and machine learning (ML) as
they relate to (1) the design of intelligent systems for important
problems of current practical interest and broad significance; (2)
the future of these disciplines; and (3) the flexible implementation
and effective use of intelligent systems in a wide range of realworld
domains, from the technological, to the social and
The emphasis falls mostly on the design and analysis of
computational tools, such as frameworks, models, and algorithms
that can handle the inherent uncertainty of complex systems and
can also learn from huge collections of data on their behavior. The
particular focus is on large, real-world systems whose complex
global behavior is the result of simple local interactions among
embedded entities forming the system (e.g., technological, social,
financial, biological, political, and economic networks).
Search-Based Software Engineering Laboratory (SBSE) (Drs. Marouane Kessentini, William Grosky, and Bruce Maxim)
This laboratory is a software development practice, which focuses on couching software engineering problems as optimization problems and utilizing meta-heuristic techniques to discover near-optimal
solutions to those problems.
We conduct research related to the application of computational search techniques to a wide variety of engineering problems, including requirements management, software testing, and capability management. SBSE offers a productive and proven approach to software engineering through automated discovery of near-optimal solutions to problems, and has proven itself to be effective on a wide variety of problems.
The SBSE lab has strong collaborations with industry, and includes a total of seven PhD students and several master and undergraduate students. The members of the SBSE lab are very active in the software engineering community with a strong publications record in top software engineering journals and conferences such as TOSEM, TSE, and EMSE. The lab has also organized several software engineering conferences such as GECCO, SSBSE, and NasBASE. Learn more.
This laboratory conducts research in the areas of computer and network security, digital forensics, and applied cryptography. It aims at tackling real-world security challenges and at the same time provides security education for society. Learn more.
This is an NSF-sponsored research laboratory.
Due to the increasing energy consumption by computer systems and the thermal threat to computer systems, environmentally sustainable computing has received significant attention in industry and research. The goal of sustainable computing is to efficiently and effectively manage system resource to render the computing sustainable with minimal impact on the environment. The underlying systems are diverse, ranging from embedded systems to high-performance chip-multiprocessors to server clusters in data centers. Many applications on these systems demand timing requirements of their applications. One of our research goals is to study these timing-sensitive applications on diverse sustainable computing platforms.
On the other hand, many systems are highly vulnerable to faults or attacks, which can compromise the system performance, corrupt important data, or expose private information. Another major research goal is to design approaches to render systems more sustainable, secure, and trustworthy. Learn more.
With emerging standards such as dedicated short-range communication (DSRC) designated for vehicle-to-vehicle communications and roadside-to-vehicle communications, cars will soon be able to communicate with each other. This enables a new class of communications applications that can support future transportation systems and needs. Located in Motown, we are working to rapidly adapt these technologies for the transportation industry. Several faculty members at the University of Michigan-Dearborn have teamed together to establish the Vehicular Networking Systems Research Laboratory. The goal of this laboratory is to provide a dedicated environment for interdisciplinary experimental research in wireless networking and mobile computing in order to develop expertise in both the theoretical and applied aspects of wireless networking and mobile computing within the context of automotive applications. Learn more.
The Virtual Engineering Laboratory is an NSF-sponsored research lab that is equipped with state-of-the-art research facilities, including a high-energy and high-accuracy X-ray computer tomography system, a high-accuracy laser scanning system, a measurement microscope, a surface roughness profiler, a coordinate measurement machine, a portable robotic arm, an autostereo and geowall display, and high-end workstations. The research at VEL focuses on innovative computational methodologies for solving both fundamental and applied problems in engineering. Our work follows the principle of being unique, creative and explorative with a focus on critical unsolved problems in precision measurement, nondestructive evaluation, computational material science, and computer-aided design, modeling and simulation. Learn more.
Wearable health technology is drawing significant attention for good reasons. The pervasive nature of such systems providing ubiquitous access to information will transform the way people interact with each other and their environment. The resulting information extracted from these systems will enable emerging applications in healthcare, wellness, emergency response, fitness monitoring, elderly care support, long-term preventive chronic care, assistive care, smart environments, sports, gaming, and entertainment which create many new research opportunities and transform researches from various disciplines.
Despite the ground-breaking potentials, there are a number of interesting challenges in order to design and develop wearable medical embedded systems. Due to limited available resources in wearable processing architectures, power-efficiency is demanded to allow unobtrusive and long-term operation of the hardware. Also, the data-intensive nature of continuous health monitoring requires efficient signal processing and data analytics algorithms for real-time, scalable, reliable, accurate, and secure extraction of relevant information from an overwhelmingly large amount of data. Therefore, extensive research in their design, development, and assessment is necessary. Learn more.