The study of how to program computers to exhibit intelligent behavior whether through problem solving, human computer interaction such as question answering, learning, solving optimization problems, or some combination of these. Areas of study include knowledge-based systems research, probabilistic reasoning and other forms of uncertainty handling, machine learning, neural networks and genetic algorithms. Common problems researched include those revolving around recognition/classification (such as speech recognition, visual comprehension, diagnosis, text mining) and planning/design.
Dr. Gary Newell studies problems involving Pattern Recognition with Uncertainty as well as exploring the boundaries of computation. For example, recent projects and publications have dealt with identifying Non-Recursively Enumerable Problems (i.e. highly undecidable problems), as well as Gestural Recognition using Probabilistic models. In particular, two recent publications have dealt with the extension of the INCA model which consistently surpasses existing recognition techniques in the area of pattern recognition with uncertainty. His recent research interests include examining the topic of Predictive Analytics as it might be applied to areas of Artificial Intelligence.
Dr. Kevin Kirby's dissertation and early research was in neural networks and genetic algorithms, with an emphasis on biologically realistic models. He spent a research summer at Wright Patterson Air Force base and, inspired by Alan Turing’s morphogenesis model and recurrent neural networks, developed what was later known as reservoir computation. He also created and taught workshops for Air Force scientists and engineers in the early 1990s on neural networks. He later branched out more broadly into natural computation, and published on quantum information in biological systems. He received the George Polya Award from the Mathematical Association of America for my exposition of neural associative memory in terms of the Dirac notation from quantum mechanics. Dr. Kirby's continuing long-term research interests include information geometry, Helmholtz machines, and deep learning, the latter now made more practical by GPU-accelerated computation. Â
Dr. Richard Fox works on knowledge-based systems research concentrating on problems of classification, abductive inference and routine planning/design. Among the projects that Dr. Fox has worked on with students are hand-written character recognition, identifying user behaviors in a Linux operating system, automated program code generation and automated music composition. Three recent projects with undergraduate students have been music recognition (recognizing a piece of music), tutorial systems that identify student learning errors, and music composition through planning and genetic algorithms.
Dr. Junxiu Zhou's research is in machine learning and deep learning, concentrating on problems of computer vision, image processing, and network resource optimization. Her Ph.D. dissertation explored object recognition, developing systems for visual navigation aid for those with visual impairments. Recently, Dr. Zhou has expanded her research interests into the area of artificial intelligence for wireless networks. Her current research projects with students include sentiment analysis, fruit recognition, and facial emotion recognition.
Cloud computing is an Internet-based computing model. It allows users to access a shared pool of compute, storage, network resources through the Internet. In cloud computing, the resources can be dynamically provisioned and released without much management effort.
Dr. Wei Hao’s main research areas are Cloud Computing, Mobile Computing, and Web Technologies. He has published numerous articles with both graduate and undergraduate students in these areas. He, along with Dr. Fox, are publishing a book on Internet Infastructure.
Cybersecurity is the study of how to protect information, networks, and systems from threats ranging from cybercriminals to cyberespionage and cyberwarfare. Inventive attackers evolve new attacks every day, forcing defenders to continually innovate with new techniques to prevent, detect, and respond to cyberattacks. Simultaneously, engineers are creating new types of connected devices without thinking of security like the smart TVs, cars, and medical devices that make up the Internet of Things, creating new vulnerabilities in existing networks. Traditional perimeter and endpoint defenses like firewalls and anti-virus that were effective in the 1990s, no longer stop most attackers, pushing defenders to focus on adaptive security techniques based on threat intelligence and machine learning. ÁÔÓ¥ÌåÓýÖ±²¥â€™s School of Computing and Analytics is home to a National Security Agency Center of Excellence in Cybersecurity. Several of the school's faculty are involved in different facets of cybersecurity research.
Dr. Yi Hu’s research concentrates on Cloud Security, Intrusion Detection, Penetration Testing, Vulnerability Assessment, Data Security, Data Mining, and Trust Management in Cyberspace. He is also the faculty advisor of ÁÔÓ¥ÌåÓýÖ±²¥ Cyber Defense Team since 2009. The team placed top nationally and regionally in Collegiate Cyber Defense Competition. Some of his recent projects include Secure Data Erasure in Cloud, IoT Security and Privacy, Penetration Testing Using Miniaturized Devices, Data Mining for Data Exfiltration Detection, etc. He is also a Certified Ethical Hacker (CEH) and Certified Information Systems Security Professional (CISSP).
Dr. Rasib Khan works on cybersecurity in service oriented computing systems. Dr. Khan’s research interests and experiences include service protocols, Internet-of-Things, cloud computing, secure authentication and access control frameworks, and secure and trustworthy provenanceaware systems. His current research projects focus on pervasive secure authentication for the Internet-of-Things, a smart and wearable secure service delivery, and scalable service-oriented computing data streams. His research on a smart cloud computing jacket has been recently featured in various tech-blogs, national media, and research portals.
Dr. James Walden works primarily in the area of software security, focusing on how to better design, implement, and validate secure software. He has worked with a variety of techniques, such as penetration testing and static analysis to identify vulnerabilities in different types of software, ranging from traditional server applications to web and mobile applications. His recent projects have focused on building vulnerability prediction models using a variety of machine learning techniques and using Internet-scale scan data to identify software update patterns.
Dr. Ankur Chattopadhyay’s recent projects in online healthcare information assurance and assessment focus on the design of online healthcare knowledge-based trust models. His work comprises development of knowledge-based recommender systems for advising online healthcare consumers to prevent cyber-psychological issues, like cyberchondria, and to address online healthcare information related credibility plus mental health problems. His recent publications in this regard can be found online at
From a computational perspective and utility perspective, data privacy sits at the boundary between data collection and data usage. It involves a balancing act where the goal is to change the data to the right degree so it does not leak private information when released to third parties, or publicly, while also preserving important information that the data carries. A broader interdisciplinary view of data privacy also includes the public expectation of privacy and the legal privacy regulations and associated issues. More about the data privacy research at ÁÔÓ¥ÌåÓýÖ±²¥ is online at
Dr. Traian Marius Truta works primarily on data anonymization models and social networks anonymity. His main contributions in this field are developing a new anonymity model for microdata titled p-sensitive k-anonymity and analyzing the effect of anonymization over the utility of data. He published over 50 papers in peer reviewed journals and conferences with two best paper awards. More than 10 of this research papers were co-authored with ÁÔÓ¥ÌåÓýÖ±²¥ undergraduate students. He serves as workshop chair on the Privacy and Anonymity in Information Society International Workshop, which is currently in its 10th edition. Since 2013, he serves as an associate editor for the Transactions on Data Privacy Journal.
Dr. Alina Campan works on data mining applications and data anonymity. She is looking at what are the effects that one incurs over the other, and how data mining (clustering in particular) can be applied to enforce various data privacy models on data. One of her contributions to the field was introducing a clustering anonymization approach along with an efficient anonymization method for social networks. She published over 50 papers in peer reviewed journals and conferences, and won one best paper awards. Several of these research papers were co-authored with ÁÔÓ¥ÌåÓýÖ±²¥ undergraduate students.
Dr. Ankur Chattopadhyay's doctoral research work in data privacy is focused on the topic of visual privacy i.e. privacy through visual anonymity in computer vision. His research interests include the design and performance evaluation of privacy-enhancing video surveillance (PEVS) techniques and models. His research work also involves adversarial modelling experiments for vulnerability assessment and performance evaluation of visual recognition algorithms, like re-identification, and visual analytics tools, like IBM Watson, Microsoft Azure. Additionally, his recent projects explore inclusive privacy and security in public video surveillance, including accommodations for under-served populations, and the challenges in applying General Data Protection Regulation (GDPR) in machine vision. His visual privacy plus PEVS related work can be followed at
The study of data structures and computational techniques to capture, represent, process and analyze geographic information. Areas of study include visualization of geospatial data, spatial data analysis, representation of geospatial data, applications of Geographic Information Systems in various fields etc.
Dr. Hongmei Wang works on Geographic Information Systems concentrating on the human-computer interaction design, uncertainty handling, applications of Geographic Information Systems and remote sensing data in various application domains. Dr. Wang has worked with students on various projects, such as knowledge elicitation for human-computer interaction design, mapping spatial distribution of honeysuckle in large forests by using remote sensing techniques, comparison of different remote sensing techniques for honeysuckle mapping, prioritizing mapping cover units in local park areas by using of GIS tools.
A research discipline for CS educators to address issues related to the development, implementation, innovation, and/or evaluation of computing programs, curricula, and courses, as well as computing labs, and other elements of teaching plus pedagogy.
Dr. Ankur Chattopadhyay's CS Ed research interests stem around CS teaching enhancements and student learning process improvements, including scaffolded learning, experimental peer led team learning (PLTL), plus creative peer mentoring driven collaborative learning models. His CS Ed research works have been published in multiple ACM and IEEE conferences. His recent projects involve undergraduate research focused innovations and robotic learning based experimental case studies. His CS Ed research work can be followed at
A research discipline for cyber educators to address issues related to the development, implementation, innovation, and/or evaluation of cyber programs, curricula, and courses, as well as laboratories, hands-on activities and other elements of teaching plus pedagogy. It focuses on best practices with practical education and training for modern cybersecurity professionals. Cyber Ed researchers are dedicated to promoting scholarship of teaching and learning on cyber educational topics. They look to publish high-quality research, perspective, and best practice articles on the understanding, investigation, and instruction of security related topics, specifically meant for an academic audience.
Dr. Chattopadhyay's Cyber Ed research interests revolve around cyber educational innovations and student learning process enhancements, including design and development of novel experiential learning models based upon visual privacy education, improvised secure coding lessons, combination of cyber education with robotics, intersection of cybersecurity and data science educational topics. He has worked closely with NSA/NSF as the lead PI of multiple GenCyber cyber-educational grant projects, and his Cyber Ed research has been published in multiple ACM and IEEE conferences. His Cyber Ed research publications can be found online at
