Dr Chidimma Opara
Scholar

Dr Chidimma Opara

Google Scholar ID: CXPDefcAAAAJ
Teesside University
Machine Learning and Exploratory Data Analysis
Homepage↗Google Scholar↗
Citations & Impact
All-time
Citations
263
 
H-index
6
 
i10-index
6
 
Publications
10
 
Co-authors
0
 
Contact
Emailmailto:?subject=Chi%20Opara&body=Check%20out%20this%20researcher%20at%20Teesside%20University's%20Research%20Portal%3A%20Chi%20Opara | https://research.tees.ac.uk/en/persons/chi-opara/?utm_source=email&utm_medium=email&utm_campaign=sharelinkTwitterOpen ↗
Resume
Academic Achievements
  • - Published articles in journals such as Expert Systems with Applications and Journal of Cybersecurity and Privacy, and conference proceedings like IJCNN and AIED.
  • - Paper StyloAI nominated for Best Late Breaking Paper.
  • - Reviewer for journals including Expert Systems with Applications, IEEE Access, Computers in Industry, and AIED.
  • - Member of the British Computer Society (MBCS) and EPSRC Peer Review College.
Research Experience
  • - Serves as a Lecturer in Computer Science at Teesside University, leading multiple modules and contributing to areas like Big Data & Business Intelligence, Project Management, Information Systems, and Cybersecurity.
  • - Research Projects: Principal Investigator for the GUARD (Generated Unauthentic Ad Recognition & Detection) project.
Education
  • - PhD: Detection and Analysis of Phishing Attacks on Webpages and Virtual Transaction Websites, Teesside University, 8 May 2023
  • - Master's Degree: Kingston University, 11 September 2015
Background
  • - Research Interests: cybersecurity, machine learning, and artificial intelligence, with a focus on phishing detection, generative AI, and stylometry.
  • - Professional Field: Computer Science
  • - Brief Introduction: Lecturer in Computer Science at Teesside University, teaching modules such as Ethics and Governance, System Management & Information Governance, and IT Ethics & Law. Her teaching emphasizes inclusive, research-led practice that prioritizes student engagement, employability, and confidence-building.
Miscellany
  • - Actively supports women in STEM and underrepresented student groups, providing mentoring and workshops.
  • - Eager to mentor prospective PhD students interested in deep learning, web security, and data mining.