Ms. Fatemeh Abdi | Software Testing | Research Excellence Award
Amirkabir University of Technology | Iran
Ms. Fatemeh Abdi is a computer engineering researcher with strong expertise in machine learning, artificial intelligence, data mining, and computational intelligence. Her research focuses on neural networks, information retrieval, speech recognition, and intelligent systems, with practical experience using PyTorch and TensorFlow. She has contributed academically as a teaching assistant in data structures, algorithms, and applied linear algebra, supporting foundational computing education. Her research outputs include diverse technical projects spanning deep learning architectures, fuzzy systems, graph algorithms, data mining techniques, and mathematical computing applications. Her scholarly impact is reflected by an h-index of 2, with 14 citations across 3 published documents, demonstrating a problem-driven, implementation-oriented research approach supported by strong analytical foundations.
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Featured Publications
A Globally Convergent BFGS Method for Pseudo-Monotone Variational Inequality Problems
– Optimization Methods & Software
A New Descent Method for Symmetric Non-Monotone Variational Inequalities with Applications
– Journal of Optimization Theory and Applications
Power Iteration and Inverse Power Iteration for Eigenvalue Complementarity Problems
– Numerical Linear Algebra with Applications
Enhancing software quality attributes through multi-dimensional refactoring at source-level
– Numerical Optimization