About Old Dominion University
Old Dominion University (ODU) is a public research university in Norfolk, Virginia, United States. Established in 1930 as the two-year Norfolk Division of the College of William & Mary, the school became an independent college in 1962 and attained university status in 1969. In 2023, it had an enrollment of 23,494 students, and its main campus covers 250 acres.
The university's name is derived from one of Virginia's state nicknames, "The Old Dominion", given by King Charles II of England in recognition of its loyalty to the crown during the English Civil War. ODU offers 175 undergraduate and graduate degree programs from seven colleges and three schools. It has a Carnegie Classification of "R1: Doctoral Universities – Very high research activity" with "Higher Access, Medium Earnings". Old Dominion has approximately 165,000 alumni in all 50 states and 67 countries.
About Department of Computer Science
The Department of Computer Science (CS) has faculty members involved in several areas of research including big data, bioinformatics, high-performance computing, medical image computing, digital libraries and web science, mobile and sensor networks and cybersecurity. This work is supported by external research funding from federal agencies such as NSF, NASA, NEH, NIH, DoD, IIPL, NIA, and others. Our Department ranks in the top 25% in terms of R&D expenditures among Computer Science departments nationally (National Science Foundation rankings based on total Research & Development expenditures).
About Faculty Advisors
Chunjiang Zhu, Assistant Professor of Computer Science
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.
Lusi Li, Assistant Professor of Computer Science
Dr. Li's research interest lies in Computer Vision and Machine Learning with a focus on developing scalable algorithms to learn robust representations from large-scale data and their applications in industrial processes, as well as intelligent communications. She has been working on various topics, including Multi-view/Multi-modal Learning (Deep Learning, Graph Learning, Pre-trained Models), Secure AI models (Adversarial Robustness), Intelligent Communications (Semantic Communication, Dynamic Spectrum Management), Transfer Learning (Domain Adaptation, Domain Generalization), Few-Shot/Zero-Shot Learning (Meta-Learning, Prompt Tuning, Incremental Learning), 2D/3D Image Processing (3D Object Detection, Person Re-identification, Medical Image Recognition, and Segmentation), Imbalanced Learning (Information Entropy, Generative Adversarial Networks, Variational Autoencoder), Industrial Applications (Fault Diagnosis, Reliability Assessment).
Mahmoud Nazzal, Assistant Professor of Computer Science
Dr. Nazzal's research focuses on the security, robustness, and applicability of Graph Neural Networks (GNNs) and Large Language Models (LLMs). Sample topics include Adversarial robustness and prompt optimization, Applications in secure and functional source code generation, Hardware design automation with LLMs and GNNs, Transportation system analytics and Internet security, Deepfake detection using multimodal and graph-based methods. His goal is to advance AI security technologies that enhance trust and reliability in the systems we depend on today and in the future. Overall, he has contributed to more than 20 conference papers, over 13 journal papers, 10 US and international patents and patent applications, and 1 book chapter, and has lectured computer engineering and related courses at NJIT and in universities in Turkey and the UAE.
Pratip Rana, Assistant Professor of Computer Science
Dr. Pratip's research interests include machine learning, computational biology, complex systems, and modeling and simulation. He has expertise in the biological network, biophysical modeling, and biological data analysis.
Jing Deng, IEEE Fellow, Associate Dean, College of Arts and Sciences, Bank of America Distinguished Professor, Department of Computer Science, UNC Greensboro
Dr. Deng studies wireless networks, network security, and online social networks. In prior work, Dr. Deng has investigated problems in wireless sensor networks, mobile ad hoc networks, and designed novel methods to achieve efficient information delivery and system security. Dr. Deng’s current work focuses on developing algorithms and techniques to achieve optimum information delivery and to detect fraudulent users and abnormal activities in wireless networks and online social networks.