Mr Tommaso Giordano | Risk assessment | Best Researcher Award |

Mr. Tommaso Giordano | Risk assessment | Best Researcher Award | 

PhD, at Consiglio Nazionale delle Ricerche , Italy

Mr. Tommaso Giordano is a dedicated research fellow at the Institute for Bioeconomy of the National Research Council (CNR-IBE) in Florence, Italy. With a multidisciplinary background spanning environmental engineering and development economics, he is currently pursuing a Ph.D. in Environmental Engineering through the prestigious International Doctorate in Civil and Environmental Engineering program at the University of Florence. His work is rooted in the intersection of urban environmental sustainability, data-driven risk assessment, and geospatial analysis. Mr. Giordano’s research is characterized by a strong commitment to applying statistical and technological tools to address real-world urban challenges.

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Education 🎓

Mr. Giordano holds a dual MSc degree in Development Economics from the University of Florence, Italy, and the University of Göttingen, Germany, as part of an esteemed joint program. His academic journey began with a High School Diploma from Liceo Scientifico “Piero Gobetti” and has progressed into doctoral-level research in environmental engineering. His master’s thesis focused on “Environmental regulation and innovation: empirical evidence for plastic,” highlighting his early interest in the interaction between environmental policy and technological development. His ongoing Ph.D. builds upon this interdisciplinary foundation, focusing on environmental modeling and urban sustainability.

Experience 👩‍

Mr. Giordano has served in various research fellow roles at CNR-IBE since March 2021, contributing to urban environmental projects funded by both national and local institutions. His responsibilities have included data collection, advanced statistical processing, geospatial analysis, and socio-economic evaluations for climate and sustainability initiatives. Prior to his research career, he completed a sales assistant internship at IRPLAST SpA, gaining experience in client services and data management systems. His professional background reflects a seamless integration of technical, analytical, and applied economic skills.

Research Interests 🔬

Mr. Giordano’s research primarily explores the application of statistical and geospatial methods to analyze urban environments, assess population risks related to natural and anthropogenic hazards, and evaluate ecosystem services. His work integrates data from IoT sensor networks, satellite imagery, and socio-economic indicators to provide holistic assessments of environmental quality, heat stress, and air pollution in urban settings. He has contributed to key research projects such as “Prato Urban Jungle,” “SMARTCITIES AIRQINO,” and “ADESFUR,” which aim to enhance urban resilience and sustainable planning through advanced data analytics.

Awards 🏆

Although still early in his career, Mr. Giordano has demonstrated noteworthy academic and research excellence. His consistent fellowship appointments at CNR-IBE reflect trust in his capabilities and the value of his contributions. His published research in internationally recognized journals, and his selection for doctoral studies at a competitive international program, further affirm his academic merit and potential as a leading researcher in the field.

Top Noted Publications 📚

Mr. Giordano has co-authored peer-reviewed scientific articles addressing key environmental issues. Notable publications include:

“Assessment of risk components for urban population to heat intensity and air pollution through a dense IoT sensor network” (Urban Climate, 2025), which explores how low-cost sensors can inform climate and health risk assessments in cities.

“Potential of low-cost PM monitoring sensors to fill monitoring gaps in areas of Sub-Saharan Africa” (Atmospheric Pollution Research, 2024), which underscores the global applicability of affordable environmental technologies.

Conclusion

Tommaso Giordano is highly suitable for a Best Researcher Award, particularly if aimed at early-career or emerging researchers in environmental engineering, urban climate, or sustainability science. He brings together technical rigor, societal impact, and cross-disciplinary training in a way that aligns well with current global research priorities.

If the award also values career trajectory, international collaboration, and data-driven innovation, he would be a strong contender. With further leadership, communication, and outreach, he could become a standout figure in urban environmental research in the next few years.

Dr Ahmed Ramses El-Saeed | Mathematical Statistics | Best Researcher Award |

Dr. Ahmed Ramses El-Saeed | Mathematical Statistics | Best Researcher Award | 

Faculty of Science, at Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.

Dr. Ahmed Ramses El-Saeed is an accomplished Assistant Professor of Statistics at Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia. With a deep expertise in Bayesian and non-Bayesian inference, lifetime data analysis, and statistical modeling, he has made significant contributions to the field of mathematical statistics. His research focuses on the development of advanced statistical methodologies for real-world applications, including reliability engineering, data science, and econometrics. Over the years, Dr. El-Saeed has built a strong academic and research career, publishing impactful studies and actively engaging in statistical education and training.

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Education 🎓

Dr. El-Saeed earned his Ph.D. in Statistics from Cairo University, Egypt (2020), with a thesis titled “Bayesian and Non-Bayesian Inference for Some Inverted Lifetime Distributions under Progressive Censoring Schemes.” Prior to that, he completed his M.Sc. in Statistics (2015) at Cairo University, focusing on life testing sampling plans. His academic journey began with a B.A. in Commerce (Statistics and Insurance) from Zagazig University (2010), where he graduated with distinction. He has also completed specialized certifications in Deep Learning, R Programming, SPSS, and Structural Equation Modeling, enhancing his expertise in data analytics and computational statistics.

Professional Experience 💼

With over a decade of experience in academia, Dr. El-Saeed has held various teaching and research positions:

Assistant Professor of Statistics at IMSIU, Saudi Arabia (2023 – Present), where he teaches advanced statistical methodologies and supervises research projects.

Lecturer of Statistics at Al-Obour High Institute for Management and Informatics, Egypt (2020 – 2023), contributing to curriculum development and statistical training.

Assistant Lecturer and Demonstrator of Statistics (2012 – 2020), mentoring students and conducting research in mathematical statistics.

Awards & Honors 🏆

Dr. El-Saeed has been recognized for his academic excellence and contributions to statistical research. His work has been featured in reputable peer-reviewed journals, and he has received accolades for his dedication to statistical education and innovation. Additionally, his engagement in international research collaborations has positioned him as a respected scholar in the field.

Top Noted Publications 📚

Power Inverted Topp–Leone Distribution in Acceptance Sampling Plans
Authors: SGN Tahani A. Abushal, Amal S. Hassan, Ahmed R. El-Saeed
Journal: Computers, Materials & Continua
Citations: 26
Year: 2021

A New Distribution for Modeling Data with Increasing Hazard Rate: A Case of COVID-19 Pandemic and Vinyl Chloride Data
Authors: AH Tolba, CK Onyekwere, AR El-Saeed, N Alsadat, H Alohali, OJ Obulezi
Journal: Sustainability
Citations: 21
Year: 2023

Estimation of Entropy for Log-Logistic Distribution under Progressive Type II Censoring
Authors: ME M. Shrahili, Ahmed R. El-Saeed, Amal S. Hassan, Ibrahim Elbatal
Journal: Journal of Nanomaterials
Citations: 21
Year: 2022

Classical and Bayesian Estimation of the Inverse Weibull Distribution: Using Progressive Type‐I Censoring Scheme
Authors: A Algarni, M Elgarhy, A M Almarashi, A Fayomi, A R El-Saeed
Journal: Advances in Civil Engineering
Citations: 21
Year: 2021

Bayesian and Non-Bayesian Estimation of the Nadarajah–Haghighi Distribution: Using Progressive Type-I Censoring Scheme
Authors: I Elbatal, N Alotaibi, SA Alyami, M Elgarhy, AR El-Saeed
Journal: Mathematics
Citations: 12
Year: 2022

Acceptance Sampling Plans for the Three-Parameter Inverted Topp–Leone Model
Authors: SG Nassr, AS Hassan, R Alsultan, AR El-Saeed
Journal: Mathematical Biosciences & Engineering
Citations: 12
Year: 2022

Conclusion

Dr. Ahmed R. El-Saeed is a strong candidate for a Best Researcher Award, particularly if the award criteria emphasize expertise in Bayesian inference, lifetime data analysis, and statistical modeling. To enhance his chances, he should increase high-impact journal publications, seek research funding, and highlight past recognitions.

Prof. Dr Jozef Ristvej | Security Sciences | Best Researcher Award |

Prof. Dr. Jozef Ristvej | Security Sciences | Best Researcher Award | 

Professor, at University of Žilina, Slovakia.

Prof. Dr. Jozef Ristvej is a distinguished researcher and academic in crisis management, specializing in disaster risk assessment, decision-making support systems, and security engineering. Currently serving as a Full Professor at the Department of Crisis Management, Faculty of Security Engineering, University of Žilina, Slovakia, he is also the Vice-Rector for International Relations and Marketing. With extensive international experience, he has been a Visiting Scholar at UC Berkeley, USA, and has collaborated with leading institutions worldwide. His work integrates technology, policy, and emergency response strategies to enhance crisis preparedness and resilience.

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Education 🎓

Prof. Ristvej holds a Ph.D. in Crisis Management from the University of Žilina, where he focused on the economic impacts of disasters. His academic journey includes research stays at renowned institutions such as the University of Pittsburgh (USA), Tilburg University (Netherlands), and Linköpings University (Sweden). He has also participated in prestigious international programs, including the ISCRAM-TIEMS Summer School on Global Disaster Risks and Humanitarian Assistance.

Professional Experience 💼

Prof. Ristvej has held several key academic and administrative positions:

Vice-Rector for International Relations and Marketing at the University of Žilina (2014–present).

Full Professor in Crisis Management at the Faculty of Security Engineering, University of Žilina (2019–present).

Visiting Scholar at UC Berkeley, USA (2024), focusing on catastrophic risk management.

Research Fellow at the University of Pittsburgh, USA (2010), working on decision-making support systems.

Erasmus Scholar at Linköpings University, Sweden (2003), studying European integration.

Research Interests 🌍

His research primarily revolves around crisis management, disaster risk assessment, decision-making support systems, security engineering, and information systems for emergency response. Prof. Ristvej has contributed to the development of frameworks that improve risk analysis, crisis preparedness, and the integration of technology in disaster response. His work has significant implications for both government policies and private sector risk management strategies.

Awards & Honors 🏆

Throughout his career, Prof. Ristvej has been recognized for his contributions to crisis management and security studies. He has been a key member of Slovak delegations at the UNESCO Youth Forum in Paris and international science fairs in the USA. His leadership in organizing international conferences and academic events has further solidified his reputation as a leading researcher in the field.

Top Noted Publications 📚

Smart city, safety and security – M. Lacinák, J. Ristvej (Citations: 292, Published: 2017, Procedia Engineering 192, 522-527)

On smart city and safe city concepts – J. Ristvej, M. Lacinák, R. Ondrejka (Citations: 124, Published: 2020, Mobile Networks and Applications 25, 836-845)

Data mining and machine learning in the context of disaster and crisis management – A. T. Zagorecki, D. E. A. Johnson, J. Ristvej (Citations: 82, Published: 2013, International Journal of Emergency Management 9 (4), 351-365)

Assessing importance of disaster preparedness factors for sustainable disaster risk management: The case of the Slovak Republic – M. Titko, J. Ristvej (Citations: 58, Published: 2020, Sustainability 12 (21), 9121)

Critical infrastructure protection systems effectiveness evaluation – T. Lovecek, J. Ristvej, L. Simak (Citations: 52, Published: 2010, Journal of Homeland Security and Emergency Management 7 (1))

Conclusion

With his extensive expertise, international collaborations, and impactful research in crisis management, Prof. Dr. Jozef Ristvej is a respected scholar in his field. His dedication to advancing risk assessment, security engineering, and decision-making systems has made significant contributions to both academia and real-world crisis response strategies.

Dr Yan Wang | Risks | Best Researcher Award |

Dr. Yan Wang | Risks | Best Researcher Award

Applied Scientist, at Kennesaw State University, United States

Dr. Yan Wang is a distinguished researcher in data science, analytics, and machine learning, specializing in credit risk modeling, statistical analysis, and investment decision-making. With an exceptional academic background and hands-on industry experience, Dr. Wang has made significant contributions to predictive modeling, fraud detection, and financial risk assessment. Her work integrates advanced statistical techniques, machine learning algorithms, and big data analytics, impacting both academia and industry.

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Education 🎓

Dr. Wang holds a Ph.D. in Analytics and Data Science from Kennesaw State University (GPA: 4.00), where she pioneered research in data-driven investment decision-making in peer-to-peer lending. She also earned an M.S. in Statistics from the University of Georgia (GPA: 4.00), further strengthening her expertise in mathematical modeling and predictive analytics. Her foundational education includes an M.S. in Pharmacokinetics (GPA: 3.92) and a B.S. in Pharmacy (GPA: 3.81) from China Pharmaceutical University, providing a unique interdisciplinary perspective in data science applications within finance, healthcare, and pharmaceuticals.

Experience 💼

Currently, Dr. Wang is a Statistician at Credigy Solutions, where she applies advanced analytics and machine learning to credit risk modeling and investment strategies. Her expertise in data visualization, predictive analytics, and risk assessment has led to a 10% reduction in model errors and improved financial forecasting. Previously, she interned at Hexaware Technologies, where she developed fraud detection models for Starbucks, leveraging cost-sensitive learning methods and ensemble techniques.

Research Interest 🔬

Dr. Wang’s research revolves around machine learning applications in financial analytics, statistical modeling, and credit risk assessment. She has developed novel models for predicting loan defaults, fraud detection, and investment risk. Her work integrates time-series analysis, ensemble learning, deep learning, and feature selection techniques to enhance model accuracy and efficiency. She has also contributed to text mining and natural language processing (NLP), applying these techniques to analyze National Science Foundation (NSF) funding trends.

Award 🏅

Dr. Wang has been recognized for her research excellence with multiple accolades, including the Best Poster Award at ACMSE 2019 for her groundbreaking work in fraud detection. Her proposed machine learning models have significantly improved industry-standard risk assessments, and her patent-pending innovation in predictive modeling showcases her contributions to data-driven financial decision-making.

Top Noted Publication 📑

Title: A Survey of Machine Learning Methodologies for Loan Evaluation in Peer-to-Peer (P2P) Lending

Authors: Yan Wang, Xuelei (Sherry) Ni

This book chapter provides a comprehensive overview of machine learning techniques applied to loan evaluation in P2P lending, exploring methodologies such as supervised learning, ensemble models, and deep learning to enhance credit risk assessment and investment decision-making

Conclusion

Yan Wang is an exceptional candidate for the Best Researcher Award, with an impeccable academic record, innovative research, and real-world industry contributions in data science, finance, and machine learning. Strengthening publication output and expanding interdisciplinary collaborations will further enhance their research impact.