Research
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.
Associated Faculty
Drs. Sarra Alqahtani, Paúl Pauca & Fan Yang
Major Directions
- Satellite and UAV Image Segmentation for Ecological Monitoring
- Change Detection and Prediction of Human Impact (e.g., Mining)
- Multi-Source Data Fusion and Model Generalization
- Time-Series and Spatial Analysis of Environmental Regions
- Scalable Machine Learning and Open-Source Tools for Conservation Scientists
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.
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.
Associated Faculty
Dr. Paúl Pauca
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.
Associated Faculty
Prof. Cody Stevens
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 **
Associated Faculty
Dr. Paúl Pauca
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
- Challenging Ocular Image Recognition
Associated Faculty
Dr. Sarra Alqahtani
Major Directions
- Safe and Robust Reinforcement Learning (RL)
- Multi-Agent Reinforcement Learning (MARL) under Uncertainty
- Interpretable and Explainable AI Systems
- Formal Verification and Risk-Aware Decision Making
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.
Freshmen to seniors are welcome.
Associated Faculty
Dr. Errin Fulp
Major Directions
- Security and Computer Networks
- Nature-Inspired Design
- Network Security Group
- Failure prediction and management
Associated Faculty
Dr. Grey Ballard
Major Directions
- High Performance Tensor Computations
- High Performance Low Rank Approximation for Scalable Data Analytics
- Practical Fast Matrix Multiplication
- Communication-Avoiding Algorithms
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.