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.

Professional Profile

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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 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.

Professional Profile

Scopus

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.