DEPT. OF COMPUTER SCIENCE
Research Areas
Faculty-student engagement through research is a hallmark of learning at Wake Forest, and especially in Computer Science. We encourage both undergraduate and graduate students to discuss research possibilities with faculty members. Although some of the possibilities are listed here, we encourage students to bring ideas to the discussion table.
Computational Biophysics
Overview
Our interdisciplinary research group interests encompass biophysics and computer science. As such, our group is represented with current and previous undergraduate and graduate students with physics, computer science, chemistry, biology, and mathematics backgrounds.
We are interested in the theoretical and computational studies and methods development of biomoleular coarse-grained and atomistic molecular dynamics (MD) simulations in collaboration with experimental groups. The inherent computational complexity of MD simulations, however, require cutting-edge high performance computing to perform simulations of biologically relevant sized systems for timescales that correspond to biological functions. One of our primary focuses is to develop MD simulation approaches using graphics processing units (GPUs), which are ideally suited for parallel algorithms such as those in MD simulations.
Computational Modeling of Signaling Networks
Participants
Alexander, Allen, Cho, Furdui, John, l. Miller, Muday, Norris, Pease, Poole, Salsbury, Turkett, Zhang
Questions
This group brings together biologists and biochemists who generate original data sets and computer scientists and mathematicians who build models to find patterns and relationships in those data sets. The current foci of this group are a) computational modeling of networks of molecules that facilitate communication within and between cells and b) motif analysis of DNA and proteins sequences. All work involves both the design of algorithms for and application of algorithms to biological data sets.
Computer Evolution as a Moving Target Defense
Overview
Survival of the fittest and natural selection are concepts that can describe how species have developed better defenses (among other things) over time… so why not use these ideas for computer security?
This project aims to develop a new Moving Target (MT) defense strategy using a unique application of Genetic Algorithms (GAs) to manage computer configurations. The objective is to create an evolutionary-inspired system that proactively finds secure computer configuration postures while maintaining a desired level of diversity. The approach will increase the complexity and cost for attackers while reducing the exposure of vulnerabilities and increasing system resiliency.
Disability Research
Major Directions
We are interested in developing highly affordable modern technology that can bridge the interaction gap between computers and people with physical and cognitive disabilities. Our goal is to exploit recent advancements in machine learning, computer vision, and human computer interaction to use the person’s body as the interface between the computer and the brain, thereby reducing or eliminating the limiting effects of conventional interface devices.
High Performance Scientific Computing
Computational Resources
GreenFlash™ GPU-Optimized Desktop Cluster
Desktop Nodes (20)
- Intel Core i7-960 3.20 GHz
- Intel X58 Motherboard with SLI
- 12 GB DDR3 RAM
- (2) NVIDIA 480 GTX GPUs
- Cool Master NVIDIA Edition ATX Mid-Tower Case
- 1 TB Hard Drives
NVIDIA 480GTX GPUs donated by NVIDIA, Inc. [link]
Built by Wake Forest University Computer Science Dept. student volunteers. Instruction on how to build your own coming soon.
DEAC Supercomputing Cluster
- Nodes / Processors : 238 nodes / 1904 cores
- Infiniband cores: 672 cores
- Gigabit Ethernet cores: 1232 cores
- Memory: 11.4TB
- Storage: 100TB
- ** 5 NVIDIA S2070 GPUs Nodes **
Imaging Research
Major Directions
We are interested in developing imaging algorithms capable of rapidly and accurately characterizing features of interest, while dealing with blurry, noisy, and incomplete data. The main goal is to improve performance by exploiting the rich information available from multi-modal data as well as prior knowledge about the task at hand.
Recent Research Projects include:
- Statistical Image Analysis and Applications to 3D imaging for Improved SSA
- Analytics of Acoustic Emissions Data
- Methods for Fusion and Compression of LiDAR and Hyperspectral Data
- Chellenging Ocular Image Recognition
Interdisciplinary Studies in Computer Science, Sound, and Music
Associated Faculty
Dr. Jennifer Burg
Description
Digitized sound and music are rich areas for creative computer generation, analysis, and manipulation. From frequencies to the mathematics of music theory, sound and music can be represented numerically and processed algorithmically. This is an interdisciplinary field that creates a bridge between digital signal processing and music theory.
Work in this area in the Computer Science Department includes:
- development of curriculum material in digital sound and music (textbook)
- development of a fully online interdisciplinary course introducing digital sound and music from a mathematic and algorithmic perspective (example Tutorial)
- student research projects in algorithmic music composition (counterpoint and jazz)
- courses and student research projects comparing the music of different faiths and cultures (First-Year Seminar and summer research projects)
Mobile App Development
Mobile devices are a part of our daily life. They are everywhere, from simple music players to sophisticated GPS devices. We are an interdisciplinary group composed of undergraduate students who are interested in the developing software for this emerging technology. Our members come from a variety of academic disciplines: Computer Science, Business, Mathematics, Economics and Physics just to name a few. There is only one requirement: willingness to learn about mobile applications, and have fun developing the apps, from Freshman to Seniors all are welcome.
Network Security Group
Associated Faculty:
Errin Fulp, Professor
Research interests are primarily in security and computer networks. Current research projects include the following:
- Nature-Inspired Design
- Network Security Group
- Failure prediction and management
Parallel Computing & Numerical Algorithms
Associated Faculty:
Grey Ballard, PhD
Research includes:
- High Performance Tensor Computations
- High Performance Low Rank Approximation for Scalable Data Analytics
- Practical Fast Matrix Multiplication
- Communication-Avoiding Algorithms
Parallel & Distributed Computation Education
Today, many computing environments consist of multiple computing elements including multi-core machines, GPU and clusters. The learning computing architectures for most undergraduate computer science students are primarily treated as single CPUs – there is a large disconnect. There is much interest in teaching parallel and distributed computing to undergraduates, as evidenced in the ACM/IEEE Joint Curriculum recommendations (Curriculum 2014) and the IEEE/NSF recommendations. Two pedagogical strategies are being studied by the computer science communities: introduction of a sophomore level course on parallel and distributed computing, and integration of PDC topics into existing computer science core topics. Professors Thomas and John are actively involved in the development and dissemination of small parallel and distributed computing modules that fit appropriately into core computer science courses. In 2014 Wake Forest was recognized as an early adopter in this effort.
Modules that have been developed so are are:
Quantum Computing
The Quantum Computing Working Group (QCWG) is a collection of faculty with an interest in quantum computing. The focus of group activities is the design, fabrication and web implementation of the Wake Quantum Chip. Utilizing the cleanroom and fabrication facilities at the Center for Nanotechnology and Molecular Materials at Wake, a new type of topological entanglement QuBit is being implemented into a multi-Qubit chip. The goal is to allow general, off-campus users to interface with the chip through a wake-designed web portal and programming environment.
