Nematzadeh, Zahra
Zahra Nematzadeh
Visiting Assistant Professor | College of Engineering and Science - Electrical Engineering and Computer Science
Contact Information
F.W. Olin Engineering Complex (OEC), room 353
Personal Overview
Dr. Nematzadeh received both her PhD and Master's degrees in Computer Science from Universiti Teknologi Malaysia. Her doctoral research focused on noise detection and classification, demonstrating her strong foundation in machine learning.
At Emory University, she contributed to groundbreaking work in the early-stage detection of laryngeal cancer using advanced deep learning and machine learning techniques, showing that voice can be recognized as a biomarker for detecting laryngeal cancer.
Currently, Dr. Nematzadeh's research interests have expanded to include the use of digital biomarkers. She aims to leverage these biomarkers to capture medical outcomes and understand the underlying causes of diseases, serving as front-line assessments before the diseases emerge. Her work is at the intersection of technology and healthcare, striving to develop innovative solutions to improve patient outcomes.
She is also interested in applying machine learning and AI in other disciplines, continually exploring new ways to integrate these technologies to solve diverse problems.
Educational Background
- Ph.D. in Computer Science, Universiti Teknologi Malaysia, 2017
- M.Sc. in Computer Science, Universiti Teknologi Malaysia, 2012
Professional Experience
- Visiting Assistant Professor, Florida Institute of Technology, 2024 - Present
- Research Scientist, Emory University, May 2022 - Dec 2023
Current Courses
- CSE 1400, Applied Discrete Mathematics
- CSE 4020, Database Systems
Selected Publications
- Nematzadeh, Z., Ibrahim, R., & Selamat, A. (2020). Improving class noise detection and classification performance: A new two-filter CNDC model. Applied Soft Computing, 94, 106428.
- Nematzadeh, Z., Ibrahim, R., Selamat, A., & Nazerian, V. (2020). The synergistic combination of fuzzy C-means and ensemble filtering for class noise detection. Engineering Computations, 37(7), 2337-2355.
- Nematzadeh, Z., Ibrahim, R., & Selamat, A. (2020). A hybrid model for class noise detection using k-means and classification filtering algorithms. SN Applied Sciences, 2, 1-10.
- Nematzadeh, Z., Ibrahim, R., & Selamat, A. (2017). Class Noise Detection Using Classification Filtering Algorithms. In Computational Intelligence in Information Systems: Proceedings of the Computational Intelligence in Information Systems Conference (CIIS 2016) (pp. 121-130). Springer International Publishing.
- Nematzadeh, Z., Ibrahim, R., & Selamat, A. (2015). A method for class noise detection based on k-means and SVM algorithms. In Intelligent Software Methodologies, Tools and Techniques: 14th International Conference, SoMet 2015, Naples, Italy, September 15-17, 2015. Proceedings 14 (pp. 308-318). Springer International Publishing.
Research
- Artificial Intelligence
- Machine Learning
- Noise Detection
- Digital Biomarkers and Healthcare