Ahmad Hassanat | Machine Learning | Best Researcher Award

Posted on by Network Science

Prof. Ahmad Hassanat | Machine Learning | Best Researcher Award

Professor at Mutah University, Jordan

Professional Profile

Scopus
Orcid
Google Scholar

Summary

Prof. Ahmad B. A. Hassanat is a Full Professor of Computer Science at Mutah University, Jordan, and a senior IEEE member. He is globally recognized for his extensive contributions to artificial intelligence, machine learning, biometrics, and image processing. With over two decades of academic and research experience, he has authored numerous impactful papers and books and is widely known for pioneering innovative techniques like the "Hassanat Distance" metric and deep learning-based biometric systems. He is also active in international collaborations, editorial work, and AI-driven healthcare research.

Educational Details

Prof. Hassanat earned his Ph.D. in Computer Science from the University of Buckingham, UK,, with a focus on automatic lip-reading. He holds an M.Sc. in Computer Science from Al al-Bayt University, Jordan, where he specialized in fast string matching algorithms. He completed his B.Sc. in Computer Science at Mutah University, Jordan. His academic foundation reflects a strong blend of theoretical depth and applied research skills in computing and AI.

Professional Experience

Prof. Hassanat has served in multiple academic roles across Jordan and Saudi Arabia, including as a Full Professor at Mutah University and the University of Tabuk. He was Head of the IT Department at Mutah University and a visiting researcher at the Sarajevo School of Science and Technology. Earlier in his career, he worked for the Jordanian Armed Forces as a programmer and systems analyst, where he developed over a dozen mission-critical ICT systems. He is also a founder or co-founder of academic programs, conferences, and novel biometric solutions.

Research Interests

His research spans machine learning, artificial intelligence, image processing, biometrics, pattern recognition, and evolutionary algorithms. He is known for practical innovations such as deep learning for veiled-face recognition, genetic algorithm optimization, voice-based Parkinson’s detection, and machine learning models for epidemiology, security, and finance. He also created the widely referenced Hassanat Distance, improving classifier performance in imbalanced data scenarios.

Author Metrics

Prof. Hassanat has published over 100 journal articles and conference papers, with an H-index of 33, i10-index of 56, and more than 4,000 citations. His work is featured in top journals such as IEEE Access, PLOS ONE, Sustainability, Applied Sciences, and Computers. His algorithmic contributions and models are highly cited in the fields of AI, healthcare informatics, and big data analytics.

Awards and Honors

Prof. Hassanat has been named among the world’s top 2% scientists by Stanford–Elsevier in 2021, 2022, and 2023. He has received the Best Scientist award at Mutah University for 2023 and 2024, and multiple competitive research grants from Jordan and Saudi Arabia. He was the recipient of Mutah University’s Distinguished Researcher Award (2018, 2019), and granted IEEE Senior Membership for his research excellence. His innovations, including terrorist identification from hand gestures and COVID-19 forecasting tools, have received global media attention.

Publication Top Notes

1. Deep learning computer vision system for estimating sheep age using teeth images

  • Authors: AB Hassanat, MA Al-Sarayreh, AS Tarawneh, MA Abbadi, et al.

  • Journal: Connection Science

  • Volume/Issue: 37 (1)

  • Article ID: 2506456

  • Year: 2025

  • Summary:
    This study presents a deep learning-based computer vision system designed to estimate the age of sheep by analyzing images of their teeth. The model likely leverages convolutional neural networks (CNNs) or similar architectures to accurately assess age-related dental features, offering a non-invasive and automated method for livestock age estimation that can assist farmers and veterinarians.

  • Citations: Not provided

  • Access: Details not provided

2. ICT: Iterative Clustering with Training: Preliminary Results

  • Authors: AB Hassanat, AS Tarawneh, AS Alhasanat, M Alghamdi, K Almohammadi, et al.

  • Conference: 2025 International Conference on New Trends in Computing Sciences (ICTCS)

  • Year: 2025

  • Summary:
    This paper introduces a novel method named Iterative Clustering with Training (ICT), presumably a machine learning or data clustering approach. Preliminary results demonstrate its effectiveness in improving clustering accuracy or training efficiency for datasets common in computing science. The approach likely combines clustering with supervised training iterations for better performance.

3. Decision tree-based learning and laboratory data mining: an efficient approach to amebiasis testing

  • Authors: E Al-Khlifeh, AS Tarawneh, K Almohammadi, M Alrashidi, R Hassanat, et al.

  • Journal: Parasites & Vectors

  • Volume/Issue: 18 (1)

  • Article Number: 33

  • Year: 2025

  • Summary:
    This research applies decision tree-based machine learning techniques to mine laboratory data for efficient and accurate diagnosis of amebiasis. The study demonstrates how data mining on clinical data combined with decision trees can improve testing accuracy and streamline diagnostic procedures in parasitology.

4. Non-Invasive Cancer Detection Using Blood Test and Predictive Modeling Approach

  • Authors: AS Tarawneh, AK Al Omari, EM Al-Khlifeh, FS Tarawneh, M Alghamdi, et al.

  • Book/Series: Advances and Applications in Bioinformatics and Chemistry

  • Pages: 159-178

  • Year: 2024

  • Summary:
    This paper proposes a non-invasive method for cancer detection by combining blood test results with predictive modeling approaches, likely using machine learning algorithms. The approach aims to provide an early, cost-effective screening tool for cancer by analyzing biomarkers and patterns in blood test data.

5. Extended spectrum beta-lactamase bacteria and multidrug resistance in Jordan are predicted using a new machine-learning system

  • Authors: EM Al-Khlifeh, IS Alkhazi, MA Alrowaily, M Alghamdi, M Alrashidi, et al.

  • Journal: Infection and Drug Resistance

  • Pages: 3225-3240

  • Year: 2024

  • Summary:
    This study develops and applies a machine learning system to predict the occurrence of extended spectrum beta-lactamase (ESBL) producing bacteria and multidrug resistance patterns in Jordan. The predictive model aids in understanding and managing antibiotic resistance, supporting healthcare decision-making and antimicrobial stewardship.

Conclusion

Prof. Ahmad Hassanat embodies the qualities of a world-class researcher—his work is innovative, deeply applied, and globally relevant. From introducing original metrics and models in AI to developing life-saving diagnostic systems and biometric security applications, his impact is both academic and practical.

His dedication to research excellence, mentorship, and cross-disciplinary innovation makes him highly deserving of the Best Researcher Award in Machine Learning.

Hedieh Sajedi – Machine learning – Best Researcher Award

Posted on by Network Science

Hedieh Sajedi – Machine learning – Best Researcher Award

Dr. Hedieh Sajedi  distinguished academic and researcher in the field Machine learning.  Her research interests encompass a wide range of advanced topics, including deep learning and machine learning, where she delves into the development and refinement of algorithms that enable computers to learn from and make decisions based on data. She is also deeply involved in multimedia processing, exploring techniques to enhance and manipulate various forms of media, such as images, videos, and audio. Additionally, her work in data mining and information retrieval focuses on extracting meaningful patterns and insights from large datasets, improving the efficiency and accuracy of information retrieval systems. Furthermore, she investigates bio-inspired algorithms, drawing inspiration from natural processes to create innovative computational methods that solve complex problems.

🌐 Professional Profile

Educations📚📚📚

She completed her Ph.D. in Artificial Intelligence and Robotics at Sharif University of Technology in May 2010, following her M.Sc. in the same field from the same institution, which she earned in August 2005. Prior to her postgraduate studies, she obtained her B.Sc. in Computer Software Engineering from Amir Kabir University of Technology in September 2002.

Work Experience:

She has delivered several invited talks on various topics, including “Computer vision and machine learning for medical image analysis” at the Children’s International Research Center in Washington DC, USA, in July 2022, and “Age Prediction based on brain MRI images” at Pompeu Fabra University in Barcelona, Spain, in June 2022. Additionally, she discussed a “Blind Spot Warning System based on Vehicle Analysis in Stream Images” at the same university and “Brain Age Estimation based on Brain MRI Images” at Sehir University of Istanbul, Turkey, in March 2018. Earlier, in March 2014, she presented on the “Application of Steganography and Steganalysis Methods in Medical and Healthcare Systems” at the University of Pavia, Italy. Her executive activities include serving as the Scientific Chair of the International Conference on Pattern Recognition and Image Analysis (IPRIA) in 2023, head of the Computer Science Department from 2018 to 2022, and Scientific Chair of the 6th International Conference on Pattern Recognition and Image Analysis at the University of Tehran in 2022. She also held the position of Head of Computer Services and Information Technology in the College of Science from 2018 to 2020 and served as Inspector of the Image Processing and Machine Vision Society in Tehran, Iran, in 2015 and 2017. Her funded projects include research on the “Detection and Classification of Circular Objects on the Basis of Convolutional Neural Network (CNN)” funded by the Iran National Science Foundation (INSF) from 2021 to the present, “Investigating Brain Health from Brain MRI Images Using Machine Learning Methods,” partially funded by the Institute for Research in Fundamental Sciences (IPM) from 2018 to 2019, “Brain Age Estimation with Mathematical Modeling” funded by INSF from 2017 to 2018, and the development of “A High-Security and High-Capacity Steganography System” funded by INSF from 2011 to 2014.

Honors & Awards

She was recognized as a member of the University of Tehran Top Researchers Club in 2022 and received the Erasmus Mobility Award from the European Union in the same year. Additionally, she was honored with the Honors Program Graduate Award from Sharif University of Technology for the period from 2006 to 2010. Since 2009, she has been an active member of the Scientific Society for Image Processing and Machine Vision.

She has been an instructor at the University of Tehran since 2013, teaching courses such as “Machine Learning,” “Artificial Intelligence,” “Data Mining,” and “Digital Image Processing” in the Department of Computer Science. She has also instructed “Advanced Topics in Artificial Intelligence” since 2020 and “Advanced Information Retrieval” from 2017 to 2020. Additionally, she taught “Advances in AI” from 2013 to 2020 and “Machine Learning in Physics” from 2018 to 2019. Her teaching portfolio includes courses for Ph.D. students at the Institute of Biochemistry and Biophysics, such as “Advanced Data Structure” in 2018-2019. At AmirKabir University of Technology, she instructed “Machine Learning” from 2010 to 2011 and “Artificial Intelligence” in 2010-2011. She also co-instructed “Machine Vision” at Pompeu Fabra University in Barcelona, Spain, in May 2022. Her experience in bio-inspired computing includes teaching “Evolutionary Computing” at the University of Tehran from 2013 to 2016.

Furthermore, she has taught “Distributed Systems” at Azad University, Qazvin, from 2011 to 2013, and courses such as “Computer Networks,” “Compiler Design and Principles,” and “Introduction to Programming” at the University of Tehran. She also taught “Operating Systems,” “Introduction to Programming,” and other foundational courses at Tarbiat Moallem University from 2006 to 2008. Her early teaching roles include instructing “Introduction to Programming” at Sharif University of Technology in 2006-2007 and several technical and scientific presentation courses at AmirKabir University of Technology from 2009 to 2011.

📝🔬Publications📝🔬

Jianxiao Wang – Data Science – Best Researcher Award

Posted on by Network Science

 Dr. Jianxiao Wang  distinguished academic and researcher in the Data Science and Smart Grid. He is currently an Assistant Professor at the National Engineering Laboratory For Big Data Analysis and Applications, Peking University, and a Distinguished Researcher at the PKU-Changsha Institute for Computing and Digital Economy. He obtained Bachelor’s degrees in Engineering and Economics from Tsinghua University in 2014, and completed his Ph.D. in Electrical Engineering from Tsinghua University in 2019. From 2016 to 2017, he served as a visiting researcher at Stanford University and the University of California, Berkeley. From 2019 to 2020, he worked as a project leader at the Ministry of Science and Technology of the People’s Republic of China, contributing to the 14th Five-Year Plan for High Tech Development in the field of energy and transportation, and conducting research on the 2035 National Science and Technology Development Strategy in China.

His research interests revolve around data-driven decision making for energy storage and renewable power systems, focusing on evaluating the levelized cost and designing national low-carbon pathways for emerging technologies such as AIGC, roadside photovoltaics, electric and fuel cell vehicles, power-to-hydrogen, green ammonia chemical industry, and carbon capture and storage utilization. As the first or corresponding author, he has published 1 paper in Nature Sustainability, 2 papers in Nature Communications, 1 paper in Cell The Innovation, 1 paper in Cell Patterns, and 2 papers in Cell iScience. Additionally, he has authored 99 papers published in top journals and conferences, including two 0.1% ESI Hot Paper, two 1% ESI Highly Cited Paper, and four IEEE Conference Best Paper Awards. He has applied for 35 China Invention Patents and 2 US Patents. Furthermore, he has published one Springer monograph as the first author, one IEEE Press monograph as a contributing author, and coauthored three Chinese monographs. The research article in Nature Sustainability was selected as a cover paper candidate and received an official report from Johns Hopkins University. As of August 2023, the research article in Nature Communications has been accessed over 10,000 times and cited 147 times, selected as a 0.1% ESI Hot Paper and officially reported by People’s Daily.

🌐 Professional Profiles

Educations📚📚📚

He pursued his academic journey at Tsinghua University, where he completed his Bachelor’s degrees in Electrical Engineering from the Department of Electrical Engineering and Economics from the School of Economics and Management from August 2010 to July 2014. Subsequently, he continued his academic pursuit at Tsinghua University, specializing in Electrical Engineering, where he earned his Ph.D. from the Department of Electrical Engineering from August 2014 to January 2019. During his Ph.D., he achieved a GPA of 92.8, ranking 2nd out of 55 students. His outstanding performance led to his recognition as an Outstanding Doctoral Graduate and his dissertation being acknowledged as one of the top 2% at Tsinghua University. Additionally, he was awarded the Tsinghua University President Jiang Nanxiang Scholarship, a prestigious honor bestowed upon 10 Ph.D. students annually.

As part of his academic journey, he expanded his horizons through visiting researcher positions at renowned institutions. From April 2017 to December 2017, he conducted research as a visiting researcher at the University of California, Berkeley. Prior to this, from November 2016 to December 2017, he engaged in research activities as a visiting researcher at Stanford University. Furthermore, he broadened his research experience with a visiting researcher position at Texas A&M University from July 2013 to September 2013.

Research Experience

He has been serving as an Assistant Professor at the National Engineering Laboratory for Big Data Analysis and Applications at Peking University since June 2022. Concurrently, he holds the position of Distinguished Researcher at the PKU-Changsha Institute for Computing and Digital Economy. Prior to his current roles, from October 2019 to September 2020, he worked as a Project Manager at the Ministry of Science and Technology of the People’s Republic of China. Additionally, he gained valuable experience as a Lecturer at North China Electric Power University from June 2019 to May 2022.

He has received numerous accolades and awards for his outstanding contributions to science, technology, and innovation. In 2023, he was honored with the Second Prize of the Science and Technology Progress Award from the China Electrotechnical Society for his work on “Transmission and Distribution Network Coordinated Control Technology and Applications Under High Distributed Energy Penetration.” Additionally, he received the Second Prize of the Power Innovation Award from the China Electric Power Union for his achievements in “Technology and Application for Data Value-driven Hierarchical Scheduling of Renewable Power Systems.” He was also recognized with the Wu Wen-Jun Artificial Intelligence Outstanding Youth Award by the Chinese Association for Artificial Intelligence for his research on “Physics-informed Data Driven Theory for Smart Grid Operation.” Furthermore, he was selected for the Wiley Open Science Excellent Author Program.

In 2022, he was honored with the Young Scientist Award by the Ministry of Science and Technology of China. He has also been recognized under the Young Elite Scientist Sponsorship Program by the China Association for Science and Technology for his work on “Smart City Energy System Operation Considering Adaptive Consistency Control of Water Electrolysis.” He received a Gold Medal at the Geneva International Exhibition of Inventions for his contribution to “Edge-intelligence Control Technology and System of Solar+Storage Powered Microgrid.” Additionally, his paper titled “Defending Against Adversarial Attacks by Energy Storage Facility” earned the IEEE PES General Meeting Best Paper Award.

He has been recognized for his exceptional service and expertise, receiving the IEEE Transactions on Sustainable Energy Excellent Reviewer Award multiple times. He was also listed in the Forbes China 30 Under 30 Elite List and acknowledged as one of the Outstanding Young Science and Technology Talents by the China Renewable Energy Society. In 2020, he was honored as one of the Beijing Outstanding Young Talents and recognized for his contributions to energy science and technology by the China Energy Research Association. Furthermore, his innovative work on “Renewable Energy-Dominated Virtual Power Plant” secured a place in the top 10 of the Elsevier Renewable Transformation Challenge. He has also been acknowledged with various awards for his inventions and innovations, including the Silver Award at the Beijing Invention and Innovation Competition and the Gold Award at the China Invention Exhibition. Additionally, he has been recognized as a Youth Of China’s High-End Technology Innovation Think Tank for his contributions to the technological development and environmental governance benefits of China’s photovoltaic and energy storage industry.

Monograph

He has authored several publications across various domains, showcasing his expertise and contributions to the field. As the first author, he collaborated with colleagues on the book “Sharing Economy in Energy Markets: Modeling, Analysis and Mechanism Design,” published by Springer in 2022. Additionally, he authored chapters 28-30 in the book “Microgrids: Theory and Practice,” published by IEEE Press in 2023.

Furthermore, he has contributed to monographs in different capacities. In the book “Introduction to the Electricity Spot Market: Trading Strategy and Profit Model for Information Driven Growth,” published by Mechanical Industry Press in 2021, he co-authored chapters 2, 4, and 6. Additionally, he was part of the team contributing to chapter 7 in “2060 China Carbon Neutrality,” published by Chemical Industry Press in 2022. He also co-authored chapters 1 and 2 in “Introduction to New-type Power Systems,” published by the China Association for Science and Technology Carbon Peak and Carbon Neutrality Series in 2022.

Representative Academic Service

He has been recognized for his exemplary contributions and leadership in various professional capacities. In 2023, he served as the Youth Editor for Cell The Innovation, a prestigious role reflecting his expertise in the field, with the journal boasting an Impact Factor of 32.1. He was also appointed as a National Graduate Education Evaluation and Monitoring Expert by the Ministry of Education, showcasing his commitment to education excellence.

In 2022, he was honored as a Distinguished Researcher under the Beijing “Thousands of People Entering Thousands of Enterprises” program, highlighting his significant contributions to research and innovation. He was also recognized as a National Excellent Educator in the National Simulation Innovation Application Competition and appointed as an Instructor by the IEEE Industry Applications Society (IAS) at Peking University. Additionally, he received accolades as an Excellent Editor from the IET Energy Conversion and Economics.

In 2021, he was appointed as an Executive Member of the IEEE Power and Energy Society (PES) China, underscoring his leadership and expertise in the field. He also served as a Youth Working Committee Member of the 9th Council of the Chinese Electrotechnical Society, demonstrating his active involvement in professional organizations.

In 2020, he was recognized as an expert in the 6th National Technology Prediction by the Ministry of Science and Technology of China, further acknowledging his expertise and contributions to technology development. Additionally, he joined the Editorial Board of IET Renewable Power Generation, contributing his insights to the publication.

From 2019 to 2022, he chaired panel sessions at the IEEE PES General Meeting, showcasing his leadership and expertise in the field. Through these diverse roles and responsibilities, he has demonstrated his commitment to advancing research, education, and innovation in his field.

Representative Research Project

He has undertaken numerous significant research projects as the primary investigator, showcasing his leadership and expertise in the field. From November 2022 to October 2025, he leads the National Key R&D Plan project titled “Resilience Enhancement Technology for Large Urban Power Grids Amidst Extreme Events,” with a funding of 4.5 million Chinese yuan. Similarly, from January 2023 to December 2026, he is leading a project funded by the National Natural Science Foundation of China titled “Renewable-dominated Power System Flexible Ramping Operation Considering Consistency Control of Water Electrolysis Multiphysics,” with a grant of 540 thousand Chinese yuan.

In previous years, he has led various other research projects funded by prestigious organizations such as the National Natural Science Foundation of China and the State Grid Corporation of China. These projects cover a wide range of topics including virtual power plant market operation, energy systems integration, self-sustained energy systems for highways, and collaborative regulation technology for large-scale energy storage clusters. Through his involvement in these projects, he has made significant contributions to advancing research and innovation in the energy sector.

📝🔬Publications📝🔬

1 Jianxiao Wang#, Liudong Chen#, Zhenfei Tan, Ershun Du, Nian Liu, Jing Ma, Mingyang Sun, Canbing Li, Jie Song,
Xi Lu*, Chin-Woo Tan*, Guannan He*. Inherent spatiotemporal uncertainty of renewable power in China. Nature
Communications, 2023, 14: 5379.
2 Yang Yu#, Jianxiao Wang#, Qixin Chen, Johannes Urpelainen, Qingguo Ding, Shuo Liu, Bing Zhang*.
Decarbonization efforts hindered by China’s slow progress on electricity market reforms. Nature Sustainability.
2023, 6, 1006-1015.
Special Comments from Johns Hopkins University:

China’s Sluggish Electricity Market Reforms Impede Decarbonization Efforts


Representative Publications
3 Jianxiao Wang#, Haiwang Zhong*, Zhifang Yang, Mu Wang, Daniel M Kammen*, Zhu Liu, Ziming Ma, Qing Xia
and Chongqing Kang. Exploring the trade-offs between electric heating policy and carbon mitigation in China,
Nature Communications, 2020, 11: 6054. (0.1% ESI Hot Paper) https://doi.org/10.1038/s41467-020-19854-y
Special Comments from People’s Daily and Tsinghua University News:
https://www.tsinghua.edu.cn/info/1175/21483.ht

4 Jianxiao Wang#, Feng Gao#, Yangze Zhou, Qinglai Guo, Chin-Woo Tan, Jie Song, Yi Wang*. Data sharing in energy
systems. Advances in Applied Energy, 2023, 10: 100132.
AEii International Applied Energy and EnergyVision Youth Scientists Forum Report:
https://mp.weixin.qq.com/s/9FyaH55gpaKFirXhlrboGg
5 Jianxiao Wang#, Haiwang Zhong*, Zhifang Yang, Xiaowen Lai, Qing Xia, Chongqing Kang. Incentive mechanism
for clearing energy and reserve markets in multi-area power systems. IEEE Transactions on Sustainable Energy, 2020,
11(4): 2470 – 2482.
6 Jianxiao Wang#, Junjie Qin, Haiwang Zhong*, Ram Rajagopal, Qing Xia, Chongqing Kang. Reliability value of
distributed solar+storage systems amidst rare weather events. IEEE Transactions on Smart Grid, 2019, 10(4): 4476 –
4486.
7 Jianxiao Wang#, Haiwang Zhong*, Xiaowen Lai, Qing Xia, Yang Wang, Chongqing Kang. Exploring key weather
factors from analytical modeling toward improved solar power forecasting. IEEE Transactions on Smart Grid, 2019,
10(2): 1417-1427.
8 Jianxiao Wang#, Haiwang Zhong, Qing Xia*, Chongqing Kang. Optimal planning strategy for distributed energy
resources considering structural transmission cost allocation. IEEE Transactions on Smart Grid, 2018, 9(5):
5236-5248.
9 Jianxiao Wang#, Haiwang Zhong, Chin-Woo Tan, Xiao Chen, Ram Rajagopal, Qing Xia*, Chongqing Kang.
Economic benefits of integrating solar-powered heat pumps in a CHP system. IEEE Transactions on Sustainable
Energy, 2018, 9(4): 1702-1712.
10 Jianxiao Wang#, Haiwang Zhong, Ziming Ma, Qing Xia*, Chongqing Kang. Review and prospect of integrated.

Jorge Laureano Moya Rodríguez – Neural Networks – Best Researcher Award

Posted on by Network Science

Jorge Laureano Moya Rodríguez – Neural Networks

 Prof Dr.  Jorge Laureano Moya Rodríguez distinguished academic and researcher in the field Neural Network.  Jorge Laureano Moya Rodríguez is a Professor Emeritus at the Central University “Marta Abreu” de las Villas. Cuba. He received his Ph.D. in Mechanical Engineering at this university in 1994. He published over a three hundred papers in professional journals and he has authored several books in mechanical and electrical engineering. He has several international and national awards, including some from the Academy of Sciences of Cuba. He has lectured in different Universities of Spain, México, Nicaragua and Brazil. He is currently visiting professor at the Federal University of Bahia in Brazil. Dr. Moya’s research interests are Multiobjective Optimization, Logistics, Computer Aided Design, and Computer Aided Engineering.

Ele é também membro da ERASMUS MUNDUS ASSOCIATION (EMA) e da Associação Mexicana de Modelagem Numérica e Engenharia (AMMNI). Reconhecido como bolsista de produtividade em Pesquisa pelo CNPq (nível 2) e consultor ad hoc do CNPq, ele contribui como árbitro para diversas revistas científicas e instituições acadêmicas em países como Venezuela, Colômbia, Peru e Cuba. Com uma ampla lista de mais de 50 projetos de pesquisa concluídos e implementados em Cuba, ele é considerado Professor de Mérito pela Universidade Central Marta Abreu de Las Villas. Sua atuação como professor abrange cursos de pós-graduação e disciplinas de mestrado em várias universidades, incluindo a Universidade Federal do Espírito Santo (Brasil), Universidade Veracruzana (México), Universidade Técnica do Estado de Aragua (Venezuela) e Universidade Nacional de Engenharia (Nicarágua). Ele também coordenou o Mestrado em Engenharia Mecatrônica em várias universidades na Venezuela e trabalhou como professor convidado em diversas instituições no México, Peru e Espanha. Anteriormente, ele foi pesquisador do ITEGAM, professor visitante na Universidade Federal do Espírito Santo e na Universidade Federal da Bahia.

 

🌐 Professional Profiles

Educations: 📚🎓

Jorge Laureano Moya Rodríguez, known in bibliographic citations as J. L. M. Rodríguez, J. L. Moya, Jorge Moya, Jorge Laureano Moya Rodríguez, J. Moya, Jorge Rodríguez, Jorge L. Moya Rodríguez, or variations thereof, is affiliated with the University Federal da Bahia, where he works within the Program of Industrial Engineering Postgraduate Studies.

He completed a postdoctoral position in 2011 at the Universidad de Oviedo, UNIOVI, Spain, funded by the Agencia Española de Colaboración Internacional, AECI, Spain. The research was in the field of Engineering.

In 2008, he undertook a postdoctoral fellowship at the Universidad de Oviedo, UNIOVI, Spain, funded by ERASMUS MUNDUS, EM, Germany. The research focus was in Engineering.

In 2005, he conducted postdoctoral research at the Universidad Católica de Leuven, KLU, Belgium, supported by VLIR, VLIR, Belgium. The research was within the field of Engineering.

Publication