Our Research Groups
The Department of Computer Science has established three interdisciplinary research groups, providing a rich environment for cross-pollinating work among these groups. Faculty members have proactively promoted students’ participation in these research groups and co-authored peer-reviewed articles with students in national and international conferences.
Students are encouraged to join one of these research groups based on their interested research area. This will allow students to work on various interdisciplinary research projects and to develop an in-depth understanding of real-world cybersecurity challenges. These research projects can also be used as a capstone design experience or master's project/thesis.
Research Areas
Cyber Security Research Group
The Cybersecurity research group is interested in the study of digital investigations, multimedia computing and forensics, mobile forensics, network security, biometrics, computer vision, surveillance analytics, bioinformatics, computational forensics, and large data computation analytics, integrated with machine learning, pattern recognition, and deep learning techniques, as well as the applications.
Cyber Security Lead Administrator
- Dr. Frank (Qingzhong) Liu
- Office: 216D
- Phone: 936-294-3569
- Email: qxl005@shsu.edu
Data Science Research Group
The members of the Data Science research group focus on developing computational methods, tools, and software to better analyze big data by overcoming limitations of human cognitive ability. Real-world problems can benefit from techniques, theories, and research findings in computational science and machine intelligence. Close cooperation between forensic scientists and computational scientists is important for modern crime investigations.
Data Science Lead Administrator
- Dr. Hyuk Cho
- Office: 216H
- Phone: 936-294-1535
- Email: hxc005@shsu.edu
Digital Forensics Research Group
The Digital Forensics research group investigates the sound extraction and analysis of digital evidence from all types of digital media so that it will stand up in a court of law. As societies become increasingly dependent on technology, the importance of digital forensics continues to escalate in today’s globally connected world. In this context, digital forensics research includes topics such as software engineering, reverse engineering, application development, software testing, and algorithm development to understand how devices, software, and information systems can be compromised, investigated, and mitigated.
DFII Lead Administrators
- Dr. Karpoor Shashidhar
- Office: 216B
- Phone: 936-294-1591
- Email: nks001@shsu.edu
- Dr. Cihan Varol
- Office: 216F
- Phone: 936-294-3930
- Email: cxv007@shsu.edu
| Cyber Security | Digital Forensics | Autonomous Systems & IoT | Data Science |
|---|---|---|---|
| Network Security | Multimedia Forensics | Hardware Design & Embedded System Security | Pattern Recognition & Machine Learning |
| IoT Security | 3D Printing Forensics | IoT Communications | Data Mining & Big Data Analytics |
| Cryptography | Computational Forensics | Hardware-based IDS | Text & Data Mining |
| Steganography | Hardware Forensics | CAN Bus Security for an interconnected system | Soft Computing & Natural Computation |
| Privacy Preservation | Social Media Forensics | Vehicle Network Security | Scientific Data Visualization |
| Data Assurance | Web Browser Forensics | Power Optimized Crypto Engines for IoT | Pervasive (Ubiquitous) Computing |
| AI-Powered Cybersecurity Analytics | Infotainment Forensics | Cyber-Physical Systems | Situation Awareness on Mobile Phones |
| Biometrics | Mobile Forensics | Robot Intelligence | Feature Engineering for Machine Learning |
| Video Surveillance | Algorithm & Application Development | Edge Computing Security | Information Retrieval & Recommendation |
| Internet Crime Investigation | Software Engineering & Reverse Engineering | Unmanned Aerial Systems (UAS) Forensics & Security | Computational Biology & Bioinformatics |
| Human Identity Identification | Biometric Forensics | AI-Driven Anomaly Detection in IoT Systems | High-Performance Computing |