Dr. Maryam Khanian |Natural Language Processing award |Women Researcher Award
Dr. Maryam Khanian, University, Australia
Dr. Maryam Khanian Najafabadi is an accomplished academic and researcher known for her significant contributions to the fields of Artificial Intelligence (AI) and Computational Intelligence. With a strong academic background and extensive experience in academia, she has earned international recognition for her work in Tier 1 journals, particularly in areas related to data science, machine learning, and recommender systems.
Dr. Najafabadi completed her Doctor of Philosophy (Ph.D.) in Computer Science with a specialization in Data Science and Machine Learning from Universiti Teknologi Malaysia (UTM) in 2016. Her doctoral thesis focused on developing recommender systems using machine learning algorithms, specifically addressing data sparsity issues through unsupervised and supervised learning techniques.
Prior to her Ph.D., she earned a Master of Science (M.Sc.) degree in Software Engineering with a focus on AI and data mining, also from UTM. She holds a Bachelor of Science (B.Sc.) in Software Engineering and Statistics from University Jihad Arak in Iran.
Education:
- Ph.D. in Computer Science (Data Science/Machine Learning)
Universiti Teknologi Malaysia (UTM), Malaysia, 2013-2016
Thesis Topic: Developing recommender systems using machine learning algorithms (cross-domain recommender systems)
Research Experience:
Maryam has led and contributed to various research projects funded by prestigious organizations such as the Malaysia government and Ministry of Higher Education Malaysia. Her research focuses on areas like recommender systems, big data analytics, sentiment analysis, and machine learning/AI. Notable publications include papers on improving collaborative filtering recommendations, big data analytics, and tag recommendation models using feature learning via word embedding.
Key Skills and Expertise:
- High-quality teaching and supervision at both undergraduate and postgraduate levels
- Extensive experience in data science, analytics, and information technology
- Strong research background with numerous publications in Tier 1 journals
- Leadership in coordinating programs and strategic development in higher education
- Proficiency in educational technologies and online delivery methods
Publication Top Notes:
A systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedback
CITED:86
An impact of time and item influencer in collaborative filtering recommendations using graph-based model
CITED:74
A survey on data mining techniques in recommender systems
CITED:74
MN r. Mahrin, S. Chuprat, and HM Sarkan,” Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data,”
CITED:16