Faculty Research Summaries
The Department of Computer Science at UNCG has a small but active faculty in a variety of research areas, with well-regarded work that has appeared in leading journals and conferences. The list below is organized faculty by expertise in broad, traditional areas of computer science, and the faculty profiles that follow give more detailed information. A printable brochure is also available.
Algorithms and Theory of Computing
Data Science and Machine Learning
Online Social Networks
Security and Cryptography
Primary Research Areas: Online Social Networks, Wireless Networks, and Network Security
Other Expertise: Information Assurance, Network Fairness
Brief Overview: Prof. Deng’s research is focused on efficient and secure algorithms for online social networks and wireless networks. There are mainly two issues in these networks, especially resource constrained ones: First, how can the information be efficiently delivered from source to destination? Secondly, how secure can such information delivery be when there are node failures, compromise, and captures? In recent research, Prof. Deng has worked on medium access control in wireless networks, information delivery in wireless sensor networks, key establishment in wireless networks, and secure routing in mobile networks.
Other recent projects of Prof. Deng have included zombie user detection and wireless rechargeable networks.
Research Areas: Databases, Data Warehousing, and Data Mining.
Brief Overview: In data warehousing, a large amount of data from different sources are extracted, cleansed, and integrated into a large storage area. There are a lot of research activities that involve this back-end of warehousing, and Prof. Fu’s research focuses on the front-end, investigating issues such as representation and efficient algorithms for complicated queries. Prof. Fu has successfully designed several efficient data cube algorithms.
Prof. Fu is investigating combining data mining and data cube, two important fields, resulting in an integrated information system that has more efficiency and capabilities. Initial results such as classification and clustering on data cubes are promising. Dr. Fu works on delivery networks, social networks, and recommendation algorithms. In particular, he is interested in the structure and dynamic features of social networks and in community clustering.
Primary Research Areas: Biomedical Image Analysis, Image Processing, Machine/Deep Learning for imaging data, Brain connectome
Other Expertise: Computational Neuroscience
Brief Overview: Dr. Kim’s research interests are mainly in developing cutting-edge image analysis methods for the interdisciplinary field between computer science and biomedicine. Imaging data has become the most powerful tool in biomedicine due to the advancement of high-resolution imaging technology and the increasing variety of imaging modalities. She aims to apply state-of-the-art computer science technologies, for example, machine/deep learning, pattern recognition, computer vision, visualization, and graph theory, to various clinical and preclinical research imaging data to study biomedical fundamentals from computer scientists’ view. Such techniques can be adapted to healthcare practices and biomedical research for automated image reading/quantification, computer-assisted diagnosis at an earlier time as well as predictive modeling for clinical outcome.
Primary Research Areas: Extended Reality Interfaces, Virtual Reality, Augmented Reality, Human-Centered Design, Human-Computer Interaction
Brief Overview: Dr. Kopper’s research centers around extended reality (XR) user experience, Virtual Reality (VR) simulation and applied XR research. Specifically, he works on improving the usability of virtual and augmented reality systems by designing novel interaction techniques, mitigating visually induced motion sickness and integrating tangible devices onto XR user interfaces. On virtual reality simulation, Dr. Kopper works on the design, VR prototyping and evaluation of next generation user interfaces for the assessment of technology that is not yet available in the market, particularly in the public safety domain. His research is also transdisciplinary and collaborative, where he investigates the employment of XR interfaces in areas such as health care, neuroscience, and the humanities.
Primary Research Areas: Information Retrieval, Natural Language Processing, Biomedical Informatics, Machine Learning
Brief Overview: Dr. Sun’ research interests span the topics of information retrieval, machine learning, and Natural Language Processing, with applications in clinical informatics to solve important healthcare problems. Dr. Sun’ overall research goal is to build user-centered intelligent information retrieval systems to leverage the vast amounts of data for improving healthcare for meaningful purposes. He developed novel text mining algorithms and heterogeneous information networks to understand query intents, online discussions and review opinions. He
applied information retrieval frameworks with enhanced user-system interaction on clinical study recommendations for prospective patients, clinical trial design optimization using electronic health records and medical evidence retrieval and synthesis. Working with clinicians from various hospitals including Rhode Island Hospital, Columbia University Irving Medical Center and University Hospitals Cleveland Medical Center, his systems have been evaluated and deployed.
Primary Research Areas: Big Data Analytics and Machine Learning, Big Data Privacy and Security, Computational Modeling, Bayesian Inference and Optimization, and Cognitive Computing.
Other Expertise: Image Analysis and Pattern Recognition, Image/Video Coding and Compression, Image/Video Watermarking and Image/Video Visual Quality Metric.
Brief Overview: Dr. Suthaharan’s research interests fall predominantly under the state-of-the-art themes of big data analytics and machine learning. In big data analytics research, he studies various data characteristics — data heterogeneity, complexity, scalability, and unpredictability — of big data for extracting knowledge to understand the data source that produced the big data. In machine learning research, Dr. Suthaharan studies advanced mathematical, statistical, and computational techniques to formulate efficient machine learning models and algorithms that can help accomplish big data analytics research. His research includes the selection and optimization of hyperparameters of machine learning models using Bayesian analysis to make machine learning highly usable in big data analytics in interdisciplinary settings. Dr. Suthaharan is also interested in exploring software engineering models and designs to support big data analytics and machine learning research. One of his current and major research areas is in ophthalmic data science and machine learning.
Stephen R. Tate
Primary Research Areas: Computer Security, Secure Software, and Cryptography
Other Expertise: Algorithms, Data Compression, and Theoretical Computer Science
Brief Overview: Dr. Tate studies computer security, with a particular emphasis on how to create software systems that provide strong security guarantees. In prior work, Dr. Tate has invented new fundamental cryptographic techniques, has produced new methods for reasoning about and proving security of cryptographic operations and protocols, and has looked at ways to enhance the security of systems by using secure hardware components such as Trusted Platform Modules. Dr. Tate’s current work focuses on developing tools and techniques to produce high-assurance software, including producing certified software and performing security-focused static analysis.
Primary Research Areas: Machine Learning, Stochastic Optimization, Deep Learning and Federated Learning
Other Expertise: Differential Privacy, Sparse Learning; Graph Convolutional Network (GCN)
Brief Overview: Dr. Tong’s research interests span the areas of stochastic optimizations, sparse learning, federated learning, and privacy-preserving machine learning. Dr. Tong mainly developed new machine learning algorithms, such as efficient sparse learning algorithms, parallel stochastic second-order algorithm, efficient Adam algorithms, and federated learning algorithms. Her goal is to develop efficient and privacy-preserving optimization algorithms for deep learning and federated learning, including communication-efficient distributed algorithms, decentralized algorithms, and federated algorithms.
Other recent projects have designed a new deep graph learning method to improve drug discovery & precision medicine; and propose a tensor-based model with quadratic inference function to analyze multidimensional data.
Primary Research Areas: Graph Algorithms and Machine learning
Other Expertise: Database Systems, AI for Drug Discovery, and Theoretical Computer Science
Brief Overview: Dr. Zhu has been using theory, principles, and methods in algorithm design,particularly graph algorithm design to solve problems in many other areas, for example, machine learning, drug discovery and development, and cyber-physical systems. Specifically, Dr. Zhu has worked on dynamic graph learning and optimization problems for machine learning and artificial intelligence, and designed graph structures such as spectral sparsifiers and graph spanners and systems to support various types of queries in graphs. For drug discovery, he has studied graph-based indexing algorithms for accelerating chemical similarity search, and designed benchmarking on the several advanced indexing algorithms. Earlier, he also designed healthcare cyber-physical systems, and data structures and algorithms in advanced memory chips.