Luis Emilio Alvarez-Dionisi

Work place: School of Systems Engineering, Polytechnic University Institute “Santiago Mariño,” Barinas, Venezuela



Research Interests: Medical Informatics, Robotics, Information Systems, Data Structures and Algorithms


Dr. Luis Emilio Alvarez-Dionisi is a professor of AI, machine learning, and deep learning. He is an international management consultant with extensive experience working with chief executive officers, boards of directors and senior management in Fortune 500 companies. He has advised numerous organizations worldwide, including Intel, IBM, Merck, Chevron, Isuzu, Smiths Detection, the Beijing 2008 Olympic Games, and the Government of Singapore Investment Corporation (GIC) on project, program, and portfolio management. Alvarez-Dionisi’s research work focuses on global project management trends, agile project management, AI, cybersecurity culture, chatbots for business, engineering robotics, big data applications, IT governance, and medical information systems.

Author Articles
Teaching Artificial Intelligence and Robotics to Undergraduate Systems Engineering Students

By Luis Emilio Alvarez-Dionisi Mitali Mittra Rosbelia Balza

DOI:, Pub. Date: 8 Jul. 2019

The skills of artificial intelligence (AI) and robotics provide a wide window of job opportunities for the following professionals: computer scientists, mechanical engineers, system engineers, computer engineers, biomedical engineers, and electrical engineers. Additionally, other professionals benefiting from AI and robotics’ job opportunities are information technologists, informatic engineers, electronic engineers, data scientists, industrial engineers, big data engineers, and related specialists in the dynamic field of engineering robotics. Therefore, the purpose of this research was to study the effort of teaching AI and robotics to undergraduate systems engineering students at the Polytechnic University Institute “Santiago Mariño” in Barinas, Venezuela. Consequently, the methodology used in this academic research was the case study approach, which included three phases, namely Initiation Phase, Fieldwork Phase, and Closing Phase. In that sense, the design of research adopted in this study was based on the development of an exploratory single case study method. As a result, the Theoretical Framework created as a cornerstone of this research highlighted the following three research variables: (1) Robotic Applications, (2) Mechanics of Robotic Manipulation and Computer Vision, and (3) Object-oriented Analysis and Design (OOAD) and Object-oriented (OO) High-level Programming Languages. In conclusion, two nondirectional null hypotheses were tested, leading to the positive answers of the following research questions: (1) “Can undergraduate systems engineering students apply OOAD and OO High-level Programming Languages to analyze, design, and develop Robotic Applications?” and (2) “Can undergraduate systems engineering students use Mechanics of Robotic Manipulation and Computer Vision to analyze, design, and develop Robotic Applications?” as stated in this case study.

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Envisioning Skills for Adopting, Managing, and Implementing Big Data Technology in the 21st Century

By Luis Emilio Alvarez-Dionisi

DOI:, Pub. Date: 8 Jan. 2017

The skills for big data technology provide a window of new job opportunities for the information technology (IT) professionals in the emerging data science landscape. Consequently, the objective of this paper is to introduce the research results of suitable skills required to work with big data technology. Such skills include Document Stored Database; Key-value Stored Database; Column-oriented Database; Object-oriented Database; Graph Database; MapReduce; Hadoop Distributed File System (HDFS); YARN Framework; Zookeeper; Oozie; Hive; Pig; HBase; Mahout; Sqoop; Spark; Flume; Drill; Programming Languages; IBM Watson Analytics; Statistical Tools; SQL; Project Management; Program Management; and Portfolio Management. This paper is part of an ongoing research that addresses the link between economic growth and big data.

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Toward Grasping the Dynamic Concept of Big Data

By Luis Emilio Alvarez-Dionisi

DOI:, Pub. Date: 8 Jul. 2016

The idea of Big Data represents a growing challenge for companies such as Google, Yahoo, Bing, Amazon, eBay, YouTube, LinkedIn, Facebook, Instagram, and Twitter. However, the challenge goes beyond private companies, government agencies, and many other organizations. It is actually an alarm clock that is ringing everywhere: newspapers, magazines, books, research papers, online, offline, it is all over the world and people are worried about it. Its economic impact and consequences are of unproportioned dimensions. This research outlines the fundamental literature required to understand the concept of Big Data. Additionally, the present work provides a conclusion and recommendations for further research on Big Data. This study is part of an ongoing research that addresses the link between Economic Growth and Big Data.

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