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
Associated Faculty
Dr. Sam Cho
Major Directions
Our interdisciplinary research group interests encompass computer science and biophysics. As such, our group is represented with current and previous undergraduate and graduate students with computer science, physics, 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 require cutting-edge high performance computing to perform simulations of biologically relevant systems over timescales that match 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.
We also integrate machine learning (ML) and artificial intelligence (AI) with MD simulations to develop generative models of biological folding states that are challenging to capture by MD simulations alone. We combine data-driven and physics-based approaches to better understand the mechanics underlying protein folding and misfiling, using large-scale MD simulations and modern AI methods.
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
Dr. Sam Cho, Prof. Cody Stevens
GreenFlash™ GPU-Optimized Desktop Cluster
Thanks to a generous initial donation from NVIDIA in 20111, the Computer Science Department created a special computer student called the GreenFlash (TM) GPU-Optimized Desktop Cluster, a group of powerful desktop computer designed to work together and leverage powerful graphics processing units (GPUs). This system is regularly updated by the Computer Science Department and used by students for both classes and research.
Students can log into the system from anywhere and write-cutting-edge computer programs using CUDA, the main programming language used to run software on GPUs. They learn how to use CUDA and create high-performance programs in special courses offered every couple of years (CSC 347/647).
When the cluster is not being used for classes, it is used by faculty and their undergraduate and graduate research students.
Desktop Nodes (5)
- Intel Core i9-9900K 3.60GHz CPU
- GIGABYTE Z390 AORUS PRO Motherboard
- 32 GB DDR4 RAM
- (2) NVIDIA RTX 2080 Ti GPUs
- (8) NVIDIA GTX 1080 Ti GPUs
- 2 TB Local Storage
- Cooler Master NVIDIA Edition ATX Mid-Tower Case
DEAC Supercomputing Cluster
The DEAC Cluster is a centralized service provided by Information Systems. Baseline usage of the DEAC Cluster for research and instructional activities is provided at no additional cost to faculty, students, and staff within any department that receive support by Information System. These departments include the School of Law, Divinity, Business, Graduate School of Arts and Sciences, and WFU Undergraduate College. The DEAC Cluster is supported by The HPC Team, who are available to provide training, troubleshooting, data management, software installation, server configuration, and hardware maintenance.
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
Associated Faculty
Dr. Pete Santago
Major Directions
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.