Atif M. Memon

Atif M. Memon is affiliated with the University of Maryland, College Park in the United States. Their research spans multiple aspects of computer science, with a primary focus on artificial intelligence, software, and information systems.

Their work covers several specialized subfields, including:

• Artificial Intelligence
• Software
• Information Systems
• Computer Vision and Pattern Recognition
• Computer Networks and Communications

Within these areas, their main topics of research encompass:

• Topic Modeling
• Natural Language Processing Techniques
• Software Testing and Debugging Techniques
• Multimodal Machine Learning Applications
• Software System Performance and Reliability
• Web Application Security Vulnerabilities
• Machine Learning and Data Classification

Atif Memon has contributed to papers published in various academic venues. Their frequent publication outlets include:

• Software Testing Verification and Reliability
• IEEE Access
• Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
• arXiv (Cornell University)

Notable papers authored or coauthored by Atif Memon include:

• Toward Robust Evaluation: A Comprehensive Taxonomy of Datasets and Metrics for Open Domain Question Answering in the Era of Large Language Models, 2024, IEEE Access
• Editorial for the special issue of STVR on the 10th IEEE International Conference on Software Testing, Verification, and Validation (ICST 2017), 2020, Software Testing Verification and Reliability
• The IEEE 12th International Conference on Software Testing, Verification & Validation, 2021, Software Testing Verification and Reliability
• Improving ML-based information retrieval software with user-driven functional testing and defect class analysis, 2022, Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
• Towards Robust Evaluation: A Comprehensive Taxonomy of Datasets and Metrics for Open Domain Question Answering in the Era of Large Language Models, 2024, arXiv (Cornell University)

They have collaborated frequently with several coauthors, including:

• Akchay Srivastava
• Ina Schieferdecker
• Hironori Washizaki
• Myra B. Cohen
• Junjie Zhu

The combination of their diverse publication venues and broad research topics indicates a focus on improving software testing methodologies, especially with respect to machine learning applications and evaluation metrics within artificial intelligence. The recurring theme of software testing and verification surfaces in both their authored papers and collaborative efforts.