Vassilios S. Verykios | Computer Science | Research Excellence Award

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Prof. Vassilios S. Verykios | Computer Science | Research Excellence Award

Hellenic Open University | Greece

Prof. Vassilios S. Verykios is a distinguished academic serving as a professor in the field of data science and information systems, with expertise in privacy-preserving data mining, data management, and knowledge discovery. He holds advanced degrees in computer science with specialization in data-centric technologies and has built a strong professional career through academic leadership, research supervision, and participation in collaborative scientific projects. His research focuses on secure data analytics, big data processing, and intelligent information systems, resulting in a substantial body of highly cited publications and impactful scholarly contributions. He has demonstrated leadership through editorial responsibilities, conference organization, and active engagement in international research communities. His work reflects sustained innovation and interdisciplinary relevance, contributing significantly to both theoretical advancements and applied solutions. Recognized for his scholarly excellence, he has received multiple honors and maintains active membership in professional organizations, reinforcing his standing as a leading contributor to advancing research and innovation in data science.

Citation Metrics (Google Scholar)

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Documents

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Top 5 Featured Publications

 


Duplicate Record Detection: A Survey


– IEEE Transactions on Knowledge and Data Engineering


Association Rule Hiding


– IEEE Transactions on Knowledge and Data Engineering


Disclosure Limitation of Sensitive Rules


– KDEX Workshop Proceedings

Sarah Marzen | Data Science | Best Researcher Award

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Prof. Sarah Marzen | Data Science | Best Researcher Award

Associate Professor Claremont McKenna College, United States

Sarah Marzen is a distinguished physicist and interdisciplinary researcher whose work bridges information theory, cognitive science, and biology. As an associate professor, she has contributed extensively to the study of sensory prediction, reinforcement learning, and resource rationality, securing leadership roles in numerous federally funded research projects. Her academic background includes a Ph.D. from the University of California, Berkeley, and postdoctoral work at MIT. She has published widely in peer-reviewed journals and played a vital role as a guest editor for multiple special issues. Sarah is actively involved in professional service, mentoring, and organizing scientific workshops. Her research stands out for its originality and interdisciplinary reach, tackling complex questions in neural computation and theoretical biology. Through her editorial work, teaching, and committee service, she has helped shape the scientific community’s understanding of cognition and prediction. Sarah Marzen’s scholarly excellence and leadership position her as a significant figure in contemporary scientific research.

Professional Profile 

Google Scholar | Scopus Profile

Education

Sarah Marzen pursued her undergraduate studies in physics at the California Institute of Technology, where she developed a strong foundation in theoretical and experimental research. She continued her academic journey at the University of California, Berkeley, earning a Ph.D. in physics. Her doctoral work focused on bio-inspired problems in rate-distortion theory, under the guidance of Professor Michael R. DeWeese. This research bridged information theory and biological systems, laying the groundwork for her future interdisciplinary pursuits. In addition to her formal degrees, she attended several prestigious summer schools and workshops, including the Santa Fe Institute’s Complex Systems School and the Machine Learning Summer School. These programs helped her expand her understanding of machine learning, complex systems, and computational neuroscience. Sarah’s educational background is marked by both academic excellence and a consistent interest in the convergence of physics, information theory, and biological intelligence, making her uniquely equipped for innovative cross-disciplinary research.

Experience

Sarah Marzen’s academic career reflects deep engagement with both research and teaching. She currently serves as an associate professor of physics at the W. M. Keck Science Department, affiliated with Claremont McKenna, Pitzer, and Scripps Colleges. Prior to this, she was an assistant professor in the same department and a postdoctoral fellow at MIT, where she worked with Professors Nikta Fakhri and Jeremy England. Her early research experience includes graduate work at UC Berkeley and multiple assistantships and fellowships during her undergraduate years at Caltech. She has also held advisory roles in academia and private research, such as mentoring for Google Summer of Code and advising a stealth startup. Her experience spans experimental physics, theoretical modeling, machine learning, and neuroscience. Alongside her teaching, she contributes significantly to committee service and program development within her department, reflecting a well-rounded academic profile. Her professional trajectory demonstrates a strong commitment to both discovery and mentorship.

Research Focus 

Sarah Marzen’s research centers on understanding how intelligent systems—both biological and artificial—predict and adapt to their environments. Her primary focus areas include sensory prediction, reinforcement learning, and resource rationality, particularly through the lens of information theory. She explores the ways in which brains and machines can perform efficient, predictive computations under constraints, contributing to theoretical frameworks that bridge physics, neuroscience, and cognitive science. Her work has applications in neural networks, artificial intelligence, and computational biology. She also investigates how delayed feedback and memory structures affect learning dynamics, as reflected in her studies of reservoir computing and time-delayed decision processes. Through her interdisciplinary approach, she addresses fundamental questions about how information is processed and used by complex systems. Her research aims to uncover principles of learning and adaptation that apply across different domains of intelligence, providing insight into both natural cognition and the design of intelligent machines.

Award and Honor

Sarah Marzen has received numerous honors and awards recognizing her academic excellence and contributions to interdisciplinary research. Early in her career, she was awarded prestigious fellowships including the NSF Graduate Research Fellowship and the MIT Physics of Living Systems Fellowship. At Caltech and UC Berkeley, she earned several merit-based scholarships and prizes for outstanding performance in physics. As her career progressed, she received grants and awards from major institutions such as the Sloan Foundation, Templeton Foundation, and the Air Force Office of Scientific Research. She has also been recognized for her editorial leadership, serving as guest editor for prominent journals like Entropy and Journal of the Royal Society Interface Focus. Her selection as a Scialog Fellow and finalist for the SIAM-MGB Early Career Fellowship further highlight her growing influence in computational neuroscience and mathematical biology. Her service and scholarly impact reflect a sustained commitment to advancing science across disciplinary boundaries.

Publications Top Notes

  • Title: Statistical mechanics of Monod–Wyman–Changeux (MWC) models
    Authors: S. Marzen, H. G. Garcia, R. Phillips
    Year: 2013
    Cited by: 128

  • Title: On the role of theory and modeling in neuroscience
    Authors: D. Levenstein, V. A. Alvarez, A. Amarasingham, H. Azab, Z. S. Chen, …
    Year: 2023
    Cited by: 100

  • Title: The evolution of lossy compression
    Authors: S. E. Marzen, S. DeDeo
    Year: 2017
    Cited by: 65

  • Title: Informational and causal architecture of discrete-time renewal processes
    Authors: S. E. Marzen, J. P. Crutchfield
    Year: 2015
    Cited by: 46

  • Title: Predictive rate-distortion for infinite-order Markov processes
    Authors: S. E. Marzen, J. P. Crutchfield
    Year: 2016
    Cited by: 45

  • Title: Time resolution dependence of information measures for spiking neurons: Scaling and universality
    Authors: S. E. Marzen, M. R. DeWeese, J. P. Crutchfield
    Year: 2015
    Cited by: 42

  • Title: Difference between memory and prediction in linear recurrent networks
    Authors: S. Marzen
    Year: 2017
    Cited by: 39

  • Title: Nearly maximally predictive features and their dimensions
    Authors: S. E. Marzen, J. P. Crutchfield
    Year: 2017
    Cited by: 39

  • Title: Structure and randomness of continuous-time, discrete-event processes
    Authors: S. Marzen, J. P. Crutchfield
    Year: 2017
    Cited by: 37

  • Title: Informational and causal architecture of continuous-time renewal processes
    Authors: S. Marzen, J. P. Crutchfield
    Year: 2017
    Cited by: 31

  • Title: Information anatomy of stochastic equilibria
    Authors: S. Marzen, J. P. Crutchfield
    Year: 2014
    Cited by: 30

  • Title: Statistical signatures of structural organization: The case of long memory in renewal processes
    Authors: S. E. Marzen, J. P. Crutchfield
    Year: 2016
    Cited by: 26

  • Title: First-principles prediction of the information processing capacity of a simple genetic circuit
    Authors: M. Razo-Mejia, S. Marzen, G. Chure, R. Taubman, M. Morrison, R. Phillips
    Year: 2020
    Cited by: 25

  • Title: Optimized bacteria are environmental prediction engines
    Authors: S. E. Marzen, J. P. Crutchfield
    Year: 2018
    Cited by: 24

  • Title: Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive
    Authors: W. Zhong, J. M. Gold, S. Marzen, J. L. England, N. Yunger Halpern
    Year: 2021
    Cited by: 22

Conclusion

Sarah Marzen’s publication record reflects a strong and sustained impact across interdisciplinary fields such as statistical physics, neuroscience, and information theory. Her most highly cited work, including studies on Monod–Wyman–Changeux models and theoretical frameworks in neuroscience, demonstrates both depth in fundamental science and relevance to contemporary research challenges. The consistent citation of her papers over more than a decade indicates the enduring influence of her contributions. Many of her works are co-authored with leading researchers, reflecting strong collaborative networks and thought leadership. Her research not only advances theoretical understanding but also bridges to applied domains like machine learning and biological computation. Overall, the citation metrics, combined with the quality and diversity of topics, reinforce Sarah Marzen’s stature as a respected and influential figure in modern scientific research, making her a compelling candidate for recognition such as the Best Researcher Award.

Yang Han | Computer Science | Best Researcher Award

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Dr. Yang Han | Computer Science | Best Researcher Award

Associate Researcher at Tianjin University, China

Yang Han is an emerging researcher with a strong academic background in mathematics, having completed both his Master’s and PhD at Nankai University, followed by a research position at Tianjin University. His work bridges mathematical theory and practical applications in engineering, focusing on areas such as topological data analysis, signal processing, and intelligent fault diagnosis. In recent years, he has published extensively in high-impact journals like IEEE Transactions on Instrumentation and Measurement and Chaos, Solitons & Fractals, and presented at reputable international conferences such as IEEE PESGM and ACPEE. His interdisciplinary research is marked by innovation and relevance, especially in appliance identification, load forecasting, and fault detection using advanced mathematical tools. Though early in his research career, Yang has demonstrated strong potential and a clear trajectory of growth. His dedication, academic rigor, and collaborative approach position him as a promising candidate for the Best Researcher Award.

🔹Professional Profile 

Google Scholar
ORCID Profile 

🏆Strengths for the Award

Yang Han demonstrates a highly impressive academic and research trajectory. With a strong foundation in mathematics from Nankai University, progressing through a Master’s and PhD (2015–2023), and currently holding an associate researcher position at Tianjin University, he shows continuity and growth in academic rigor. His research spans interdisciplinary areas, merging topological data analysis, signal processing, machine learning, and fault diagnosis—fields of significant importance in both academia and industry. Notably, his recent publications in high-impact journals such as IEEE Transactions on Instrumentation and Measurement and Chaos, Solitons & Fractals reflect both quality and innovation. Additionally, his contributions to top-tier conferences like IEEE PESGM and ACPEE signal strong peer recognition. The combination of applied AI techniques and deep mathematical theory shows versatility, a rare and commendable strength for a young researcher.

Areas for Improvement

While the publication record is strong and growing, most of the impactful work is very recent (primarily in 2024–2025), indicating that Yang Han is in the early stages of building a long-term research profile. Sustained contributions over a longer timeline will better establish him as a leading authority. Another point of improvement would be to take on more lead or sole authorship roles in future publications, as many current works are collaborative with shared credit, which can make it harder to isolate individual impact. Additionally, while his interdisciplinary work is a strength, expanding his network internationally through collaborations beyond China and participating in global research programs could enhance the visibility and influence of his work.

Conclusion

Yang Han is a highly promising and impactful early-career researcher with a unique blend of mathematical depth and applied AI-driven engineering. His recent output demonstrates a clear upward trajectory, both in productivity and innovation. While there is room to further solidify his independent research identity and global presence, his current achievements strongly support his candidacy for the Best Researcher Award. Given his solid grounding, interdisciplinary focus, and growing impact, he is indeed a suitable and deserving nominee for this recognition.

🎓Education

Yang Han began his academic journey at Nankai University, a prestigious institution known for mathematical excellence. From 2015 to 2018, he completed his Master’s degree at the School of Mathematical Sciences and LPMC, focusing on advanced mathematical theories and computational techniques. His strong academic performance and deep interest in topology, algebra, and their applications led him to continue his research as a PhD student in the same department from 2019 to 2023. During his doctoral studies, he expanded his expertise into applied mathematics and began to explore connections with engineering systems and data-driven problem solving. His doctoral research provided the foundation for his transition into interdisciplinary areas such as topological data analysis and graph signal processing. His time at Nankai University was marked by academic growth, critical thinking, and active participation in scholarly research. This rigorous educational background prepared him for a successful research career bridging mathematics and electrical engineering.

💼Experience

Yang Han currently holds the position of Associate Researcher at the School of Electrical and Information Engineering, Tianjin University. Since assuming this role in 2023, he has actively contributed to research in intelligent systems, signal processing, and data analytics. Before this, he spent nearly a decade at Nankai University, where he completed his Master’s and PhD studies, engaging in teaching support and foundational research. His experience spans a variety of projects focused on non-intrusive load monitoring, equipment fault diagnosis, and appliance identification—often leveraging advanced mathematical tools like topological data analysis and fast Fourier transforms. He has contributed to both national and international research collaborations, presented at prestigious conferences, and published in leading journals. His ability to blend abstract mathematical methods with real-world engineering challenges exemplifies his versatile experience. His role also involves mentoring junior researchers and contributing to interdisciplinary innovation at the intersection of mathematics, artificial intelligence, and electrical engineering.

🏆Awards and Honors

While formal individual awards are not explicitly listed in the available data, Yang Han’s growing list of high-impact publications and conference presentations serves as strong evidence of professional recognition. His work has been published in top-tier journals such as IEEE Transactions on Instrumentation and Measurement, Chaos, Solitons & Fractals, and Engineering Applications of Artificial Intelligence, reflecting a high level of peer recognition. He has also contributed to leading international conferences, including IEEE PESGM and the Asia Conference on Power and Electrical Engineering (ACPEE), where selection itself is a mark of merit. These platforms are known for their rigorous review processes, indicating that his work meets and often exceeds international research standards. Additionally, his involvement in collaborative, interdisciplinary projects and authorship in multiple papers shows that he is a valued team member in academic and industrial circles. As his career progresses, further formal awards and honors are likely to follow.

🔬 Research Focus on Computer Science

Yang Han’s research is centered at the intersection of applied mathematics, artificial intelligence, and electrical engineering. His primary focus lies in topological data analysis, signal processing, and machine learning techniques for complex system monitoring and fault detection. He has contributed significantly to non-intrusive load monitoring (NILM), using graph signal processing to identify energy consumption patterns without intrusive sensors. He also works on fault diagnosis through time-frequency analysis and the application of mathematical topology in real-world engineering systems. His innovative approach often involves transforming abstract mathematical concepts—such as Betti curves and topological invariants—into practical tools for appliance identification and power grid analysis. Furthermore, Yang Han is exploring adaptive methods for equipment behavior modeling and data-driven forecasting. This unique research blend offers both theoretical advancements and immediate practical value, demonstrating his ability to tackle emerging challenges in intelligent energy systems and industrial diagnostics with precision and depth.

📚 Publications Top Notes

  • Title: Energy dissipation analysis of elastic–plastic materials
    Authors: H Yang, SK Sinha, Y Feng, DB McCallen, B Jeremić
    Year: 2018
    Citations: 94

  • Title: Study on the mechanical behavior of sands using 3D discrete element method with realistic particle models
    Authors: WJ Xu, GY Liu, H Yang
    Year: 2020
    Citations: 46

  • Title: Nonlinear finite elements: Modeling and simulation of earthquakes, soils, structures and their interaction
    Authors: B Jeremić, Z Yang, Z Cheng, G Jie, N Tafazzoli, M Preisig, P Tasiopoulou, …
    Year: 2018
    Citations: 37

  • Title: The real-ESSI simulator system
    Authors: B Jeremić, G Jie, Z Cheng, N Tafazzoli, P Tasiopoulou, F Pisanò, JA Abell, …
    Year: 1988
    Citations: 35

  • Title: Study on the meso-structure development in direct shear tests of a granular material
    Authors: H Yang, WJ Xu, QC Sun, Y Feng
    Year: 2017
    Citations: 28

  • Title: Energy dissipation analysis for inelastic reinforced concrete and steel beam-columns
    Authors: H Yang, Y Feng, H Wang, B Jeremić
    Year: 2019
    Citations: 27

  • Title: Time domain intrusive probabilistic seismic risk analysis of nonlinear shear frame structure
    Authors: H Wang, F Wang, H Yang, Y Feng, J Bayless, NA Abrahamson, B Jeremić
    Year: 2020
    Citations: 22

  • Title: Seismic resonant metamaterials for the protection of an elastic-plastic SDOF system against vertically propagating seismic shear waves (SH) in nonlinear soil
    Authors: C Kanellopoulos, N Psycharis, H Yang, B Jeremić, I Anastasopoulos, …
    Year: 2022
    Citations: 21

  • Title: Energy dissipation in solids due to material inelasticity, viscous coupling, and algorithmic damping
    Authors: H Yang, H Wang, Y Feng, F Wang, B Jeremić
    Year: 2019
    Citations: 20

  • Title: 3-d non-linear modeling and its effects in earthquake soil-structure interaction
    Authors: SK Sinha, Y Feng, H Yang, H Wang, B Jeremic
    Year: 2017
    Citations: 19

  • Title: Plastic-energy dissipation in pressure-dependent materials
    Authors: H Yang, H Wang, Y Feng, B Jeremić
    Year: 2020
    Citations: 18

  • Title: Relationship between multifunctionality and rural sustainable development: Insights from 129 counties of the Sichuan Province, China
    Authors: X Li, J Liu, J Jia, H Yang
    Year: 2022
    Citations: 17

  • Title: Modeling and simulation of earthquake soil structure interaction excited by inclined seismic waves
    Authors: H Wang, H Yang, Y Feng, B Jeremić
    Year: 2021
    Citations: 17

  • Title: An energy-based analysis framework for soil structure interaction systems
    Authors: H Yang, H Wang, B Jeremić
    Year: 2022
    Citations: 14

  • Title: A robust and efficient federated learning algorithm against adaptive model poisoning attacks
    Authors: H Yang, D Gu, J He
    Year: 2024
    Citations: 11

Shishir Tewari | Computer Science | Technology and Innovation Leadership Award

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Mr. Shishir Tewari | Computer Science | Technology and Innovation Leadership Award

Senior Manager, Data Engineering at Procore Technologies, United States

Shishir Tewari is a seasoned technology leader with over 19 years of experience driving innovation in data engineering, data warehousing, and analytics across top-tier organizations such as Google, Amazon, Morgan Stanley, and Microsoft. He currently leads strategic data initiatives at Procore Technologies, where he has spearheaded the development of AI/ML-driven platforms, cloud migrations, and real-time analytics systems. Known for his expertise in building scalable, high-performance data solutions, Shishir has successfully led global engineering teams and transformed complex data ecosystems on AWS, GCP, and Databricks. His technical vision, operational excellence, and commitment to data quality and governance have consistently delivered measurable business value. Shishir’s continuous pursuit of innovation and deep cross-functional leadership make him a standout contributor in the technology landscape. With a strong foundation in data science, cloud architecture, and team mentorship, he exemplifies the qualities of a forward-thinking, impact-driven technology leader worthy of recognition.

Professional Profile 

Google Scholar

Education

Shishir Tewari holds a Bachelor of Technology in Information Technology from U.P.T.U., India, graduating in 2006. Demonstrating a commitment to lifelong learning and innovation, he further enhanced his credentials with a specialization in Data Science and Analytics from Rutgers University, New Jersey, in 2018–2019. This advanced academic training equipped him with modern analytical techniques, machine learning algorithms, and statistical modeling—skills that have been instrumental in his professional success. His educational background lays a strong foundation for his technical leadership, blending theoretical knowledge with real-world application. The combination of engineering fundamentals and data science expertise positions Shishir as a well-rounded technology leader who can bridge the gap between innovation and implementation in enterprise environments.

Professional Experience

Shishir Tewari brings over 19 years of robust experience across global technology firms, including Google, Amazon, Morgan Stanley, Microsoft, and currently, Procore Technologies. His career spans technical leadership, large-scale data architecture, and cloud-native platform innovation. At Google, he led a global team optimizing financial data pipelines and infrastructure. At Amazon, he designed high-performance advertising data systems, enabling substantial revenue impact. At Procore, he has driven major initiatives including AI/ML-powered data platforms and cloud migrations. His ability to manage large engineering teams, align data strategy with business goals, and optimize performance at scale reflects his leadership maturity. Shishir’s diverse experience across industries—finance, tech, construction, and advertising—gives him a unique, cross-sector perspective on data-driven transformation.

Research Interest

Shishir Tewari’s research interests lie at the intersection of big data engineering, AI/ML-driven analytics, and cloud computing. He is particularly passionate about optimizing large-scale data systems for performance, governance, and real-time decision-making. With practical expertise in cloud platforms like AWS, GCP, and Databricks, his focus is on leveraging modern data stacks and open-source technologies to power next-generation analytics and automation. He is also interested in the application of machine learning for master data management, anomaly detection, and predictive modeling within business intelligence ecosystems. While not rooted in academic publishing, his work consistently applies research principles to solve real-world business problems, delivering measurable impact. Future interests include exploring the integration of generative AI with enterprise data platforms and advancing data democratization through self-service analytics tools.

Award and Honor

While specific awards and honors are not listed in his profile, Shishir Tewari’s consistent elevation to senior technical and leadership roles in globally respected organizations serves as a testament to his excellence and recognition within the industry. Being entrusted with mission-critical projects at Google, Amazon, and Morgan Stanley speaks to his reliability, vision, and execution skills. His role in leading high-visibility initiatives such as financial data certification, AI/ML-driven analytics platforms, and major cloud migrations reflects the high degree of trust and credibility he commands. He has likely received internal accolades for his contributions to performance optimization, cost reduction, and innovation. A nomination for a Technology and Innovation Leadership Award would further formalize and honor his significant contributions to data-driven transformation and technological advancement in enterprise settings.

Conclusion

Shishir Tewari exemplifies the qualities of a forward-thinking technology leader, with deep expertise in data engineering, cloud architecture, and strategic innovation. His two-decade-long career reflects a commitment to excellence, from hands-on development to executive-level leadership. With advanced training in data science, he brings both theoretical rigor and practical vision to his work. His impactful roles at top-tier organizations demonstrate his ability to lead cross-functional teams, optimize large-scale systems, and implement transformative technologies. Passionate about leveraging AI/ML and cloud platforms to drive business value, Shishir’s professional journey is marked by continuous learning and measurable outcomes. He stands out as a prime candidate for recognition through a Technology and Innovation Leadership Award, not only for his technical contributions but also for his ability to inspire, mentor, and lead organizations into the future of data-driven innovation.

Publications Top Notes

  1. Title: AI Powered Data Governance – Ensuring Data Quality and Compliance in the Era of Big Data
    Authors: S. Tewari
    Year: 2025

  2. Title: Operationalizing Explainable AI in Business Intelligence: A Blueprint for Transparent Enterprise Analytics
    Authors: A. Chitnis, S. Tewari
    Year: 2024

  3. Title: AI and Multi-Cloud Compliance: Safeguarding Data Sovereignty
    Authors: S. Tewari, A. Chitnis
    Year: 2024

  4. Title: Scalable Metadata Management in Data Lakes Using Machine Learning
    Authors: S. Tewari
    Year: 2023
    Citation: (Update needed)

  5. Title: AI-Powered Financial Forecasting: Enhancing Accuracy with Machine Learning in Enterprise System
    Authors: S. Tewari
    Year: 2023)

  6. Title: Detecting Data Drift and Ensuring Observability with Machine Learning Automation
    Authors: A. Chitnis, S. Tewari
    Year: 2022

  7. Title: Anomaly Detection in Large Scale Data Platforms with Machine Learning
    Authors: S. Tewari
    Year: 2022

  8. Title: Leveraging Graph Based Machine Learning to Analyze Complex Enterprise Data Relationships
    Authors: S. Tewari, A. Chitnis
    Year: 2021

Arturo Benayas Ayuso | Computer Science | Best Researcher Award

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Prof. Arturo Benayas Ayuso | Computer Science | Best Researcher Award

PhD Candidate, Universidad Politécnica de Madrid, Spain

Arturo Benayas Ayuso is a highly skilled Naval Architect with a distinguished career in naval shipbuilding and digital transformation. He currently leads the integration of the “El Cano” platform at NAVANTIA, spearheading Industry 4.0 innovations in ship design, construction, and management. His expertise in integrating PLM systems and IoT into shipbuilding projects has positioned him as a leader in naval digitization. Fluent in multiple languages, Arturo also serves as a lecturer, sharing his knowledge of statistics at Universidad Complutense de Madrid. 🚢💡

Publication Profile

ORCID

Education

Arturo holds a Master’s in Naval Architecture from Universidad Politécnica de Madrid and is currently pursuing a PhD, focusing on IoT applications in ship design, shipbuilding, and management. His academic background, combined with his professional experience, allows him to seamlessly bridge the gap between theory and practice in the maritime industry. 🎓📚

Experience

As the Integration Lead of NAVANTIA’s “El Cano” platform, Arturo manages the digitization and PLM integration of naval shipbuilding processes. His past roles include overseeing the FORAN-PLM integration for Spain’s S80 submarine and collaborating on several high-profile naval projects, including the Royal Navy’s CVF program. His work has consistently focused on improving digital workflows in naval engineering using systems like Windchill and Teamcenter PLM. 🛠️⚙️

Research Focus

Arturo’s research revolves around applying IoT technology to ship design and manufacturing. His work aims to enhance the efficiency of shipbuilding processes by integrating advanced digital tools and IoT into ship management systems. This focus on Industry 4.0 in naval architecture ensures future-ready solutions in naval engineering. 🔍🌐

Awards and Honors

Arturo has contributed significantly to both industry and academia, sharing his insights at conferences like RINA and publishing in prestigious industry magazines. His thought leadership in naval shipbuilding and PLM system integration has earned him recognition within the maritime and technology sectors. 🏅📜

Publications

Integrated Development Environment in Shipbuilding Computer Systems – ICAS 2011, cited in studies related to shipbuilding digitization

Automated/Controlled Storage for an Efficient MBOM Process in Shipbuilding Managing IoT Technology – RINA, 2018, discussed in articles on smart ship management

Data Management for Smart Ship: Reducing Machine Learning Cost in IoS Applications – RINA, 2018, frequently referenced in works on IoT and machine learning integration

Osama Sohaib | Information Systems | Best Researcher Award

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Dr. Osama Sohaib | Information Systems | Best Researcher Award

Associate Professor, American University of Ras Al Khaimah, United Arab Emirates

Dr. Osama Sohaib is an Associate Professor of Business Analytics at the American University of Ras al Khaimah, UAE. He holds a Ph.D. in Information Systems from the University of Technology Sydney, Australia. With over 15 years of teaching experience, Dr. Sohaib is dedicated to educating and mentoring undergraduate and postgraduate students in information systems, focusing on the intersection of technology and business. 🌍📚

Publication Profile

Google Scholar

Education

Dr. Sohaib earned his Ph.D. in Information Systems in 2015 from the University of Technology Sydney, Australia. He is currently pursuing a Master of Business Analytics at the University of Queensland and holds a Graduate Certificate in Applied Artificial Intelligence from Charles Sturt University. His academic journey also includes a Master of Science in Computer Science, a Postgraduate Diploma in Information Management, and a Bachelor of Science in Software Development. 🎓📖

Experience

With over 15 years of experience in academia, Dr. Sohaib has held various positions, including Associate Professor at the American University of Ras al Khaimah and Lecturer at the University of Technology Sydney. He has also taught at Macquarie University and the University of New South Wales. His roles have included supervising research students, coordinating academic programs, and contributing to funded projects in business information systems. 💼👨‍🏫

Research Focus

Dr. Sohaib’s research interests encompass business information systems, e-services, digital privacy, digital transformation, business intelligence, decision-making, and applied machine learning. His work aims to enhance service effectiveness across various sectors, including digital business, healthcare, education, and government, with a strong emphasis on the ethical and societal implications of technology. 💡🔍

Awards and Honors

Dr. Sohaib has received multiple accolades, including the “Research of the Year” award from the School of Business at AURAK for his exceptional research contributions in 2023 and 2024. He was also honored with the “Best Paper Award” at the 25th International Conference on Information Systems Development in 2016 for his work on web content accessibility. 🏆🌟

Publication Top Notes

Assessing Web Content Accessibility of E-Commerce Websites for People with Disabilities
Best Paper Award, 2016
Link to Publication | 2016 | Journal of Information Systems Development | Cited by: 120

Digital Privacy in the Age of Big Data and Machine Learning: People’s Expectations and Experiences
Link to Publication | 2022 | International Journal of Information Management | Cited by: 85

Factors Influencing Continuance Intention in Augmented Reality Platforms
Link to Publication | 2023 | Journal of Business Research | Cited by: 45

Opportunities and Challenges in the Implementation of AI in Accounting and Auditing Software
Link to Publication | 2024 | International Journal of Accounting Information Systems | Cited by: 10

The Effect of Individual’s Technological Belief and Usage on their Absorptive Capacity towards their Learning Behaviour in the Learning Environment
Link to Publication | 2020 | Computers & Education | Cited by: 30