Prof. Dr. Cornelia Aurora Gyorod | Machine Learning | Research Excellence Award
Prof. Dr. Cornelia Aurora Gyorod | Machine Learning | University of Oradea | Romania
Prof. Dr. Cornelia Aurora Gyorod is a senior academic and internationally recognized researcher in Computer Science and Information Technology, specializing in database systems, data mining, expert systems, and large-scale data-driven computing architectures that underpin modern intelligent and sensing-based systems. She holds a Ph.D. in Computer Science from the University of Oradea and currently serves as a Professor in the Faculty of Electrical Engineering and Information Technology, Department of Computers and Information Technology, where she has demonstrated long-standing excellence in teaching, research, and academic leadership. Her educational background is complemented by advanced professional certifications in project management, project evaluation, and enterprise database technologies, reflecting her strong methodological and organizational competence. Her professional experience spans progressive academic roles including junior assistant, assistant professor, lecturer, associate professor, and full professor, during which she has been responsible for delivering core and advanced courses such as Databases, Expert Systems, Computer Programming, Advanced Database Systems, and Data Warehousing, alongside supervising undergraduate, master’s, and doctoral research. Her strengths for this award include a strong international research profile, with 70+ peer-reviewed publications, primarily indexed in Scopus and IEEE-affiliated venues, accumulating 800+ citations and an established Scopus Author ID and ORCID record.
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Featured Publications
A Comparative Study of Relational and Non-Relational Database Models in a Web-Based Application
– IJACSA, 2015 · 94 Citations
Performance Analysis of NoSQL and Relational Databases with CouchDB and MySQL
– Applied Sciences, 2020 · 84 Citations
Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases
– Applied Sciences, 2021 · 56 Citations