Assoc. Prof. Dr. Lisandra Díaz de la Paz, Central University “Marta Abreu” of Las Villas, Cuba
Assoc. Prof. Dr. Lisandra Díaz de la Paz is a Cuban computer scientist and academic with a Ph.D. in Technical Sciences (2023), a Master’s (2011), and a Bachelor’s (2008) in Computer Science from the Central University “Marta Abreu” of Las Villas (UCLV). She serves as an Associate Professor and researcher specializing in databases, decision-support systems, data integration, metadata management, and artificial intelligence. With over 15 years of teaching experience, she has instructed various undergraduate and postgraduate courses in computer science and related fields. Dr. Díaz de la Paz has completed extensive postgraduate training in areas such as software engineering, machine learning, and data science. She currently leads the Information Systems discipline and serves as Vice Dean of Research and Postgraduate Studies at the MFC Faculty, UCLV. Her research focuses on data quality models, big data, Python programming, semantic web, LLMs, and generative AI. She is an active contributor to Cuba’s technological advancement.
Publication Profile
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Academic Background
Assoc. Prof. Dr. Lisandra Díaz de la Paz is a distinguished academic in the field of Computer Science with a robust educational foundation acquired from the Central University “Marta Abreu” of Las Villas (UCLV), Cuba. She earned her Bachelor’s degree in Computer Science in July 2008, followed by a Master’s degree in the same field in December 2011. Demonstrating a continuous commitment to academic excellence and research, she completed her Doctorate in Technical Sciences in November 2023. This progression reflects her deepening expertise and scholarly dedication within computing and technical disciplines. Her academic journey at UCLV has equipped her with strong theoretical and practical knowledge, forming the basis for her professional contributions as a university professor, researcher, and academic leader. Dr. Díaz de la Paz’s qualifications underpin her role in advancing research in artificial intelligence, databases, and data systems while mentoring the next generation of computing professionals in Cuba and beyond.
Professional Role and Academic Specialization
Assoc. Prof. Dr. Lisandra Díaz de la Paz is a dedicated professor and researcher with a strong focus on the field of Computer Science. Currently holding the academic rank of Associate Professor, she plays a vital role in higher education by teaching, mentoring, and guiding students across multiple levels of university instruction. Her primary specialization lies in computing, where she has developed expertise in areas such as databases, data quality, artificial intelligence, decision-support systems, and big data technologies. As both an educator and researcher, she combines theoretical knowledge with practical applications, contributing to academic excellence and technological advancement. Her position as a faculty member enables her to engage in curriculum development, academic leadership, and innovative research initiatives. Dr. Díaz de la Paz’s dual role as a professor and researcher allows her to bridge the gap between knowledge creation and dissemination, making her an influential figure in the Cuban academic and scientific community.
Awards and Recognitions
Assoc. Prof. Dr. Lisandra Díaz de la Paz has received multiple prestigious awards in recognition of her contributions to computing and educational technologies. She was a co-author of the project “Algorithms and Tools for the Library Management System,” which earned the 2024 Provincial CITMA Award in Villa Clara. In 2021, she received the Provincial CITMA Award for her work on improving the accuracy and completeness of bibliographic records in MARC 21 format. In 2019, she received the Annual Award from the Minister of Higher Education for her research in database systems and computing. Her 2018 work on the ABCD Library Management System implementation across Cuban higher education institutions was recognized for its scientific and educational impact. She also received CITMA awards in 2016 and 2012 for her innovative contributions to active database rule maintenance and business rule implementation in relational databases, respectively—highlighting her sustained excellence in research and technical innovation.
Research Focus
Assoc. Prof. Dr. Lisandra Díaz de la Paz focuses her research primarily on data quality, metadata management, bibliographic systems, and decision support through data-driven computing. Her work encompasses key areas such as the completeness and accuracy of bibliographic records in MARC 21 format, ETL process optimization, metadata profiling, and author name disambiguation using ontologies and deep learning. She has also explored big data integration with NoSQL systems, MapReduce techniques for anomaly detection, and frameworks for metadata quality evaluation in the context of open science. Her contributions have practical applications in library science, digital repositories, and institutional decision-making, particularly within educational and academic information systems. Additionally, her interdisciplinary approach blends artificial intelligence, machine learning, semantic web technologies, and business intelligence, supporting national and international collaboration for improving data infrastructure. These efforts position her as a leading researcher in data-centric computing, database technologies, and intelligent information systems.
Publication Top Notes
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📘 Algorithm to correct instance-level anomalies in large data using MapReduce – Cited by 7 – 2016
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📗 Data quality analysis in ABCD suite sources – Cited by 7 – 2015
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📕 Techniques to capture changes and maintain updated data warehouse – Cited by 5 – 2015
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📙 Data market for decision-making on teaching/research staff at UCLV – Cited by 5 – 2013
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📒 Techniques to capture data changes (extended version) – Cited by 4 – 2015
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📓 Automation of data loading processes in HR data market at UCLV – Cited by 4 – 2014
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📘 Weights estimation in completeness measurement of bibliographic metadata – Cited by 3 – 2021
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🧠 Author name disambiguation using ontology & deep learning – Cited by 1 – 2022
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📊 CompMARC tool for measuring completeness in MARC 21 – Cited by 1 – 2016
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📚 Model for metadata quality evaluation: Proposal for open science – Published – 2024
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📝 Accuracy measurement of author names in MARC 21 records – Published – 2018
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📈 Optimal weight estimation for completeness in MARC 21 metadata – Published – 2017
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🔍 Metadata profiling tool in MARC 21 PMMarc v2.0 – Published – 2017
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💾 Method for selecting data model and NoSQL system in big data – Published – 2017
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🛠 Procedure to improve completeness in MARC 21 records – Published – 2017