Yuanming Zhang | Intelligent data processing and analysis | Best Researcher Award

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Dr. Yuanming Zhang | Intelligent data processing and analysis | Best Researcher Award

Yuanming Zhang is an Associate Professor at the College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China. He earned his Ph.D. in Information Science from Utsunomiya University, Japan, in 2010. His research focuses on data processing, graph neural networks, knowledge graphs, prognostics, health management, and condition monitoring. With expertise in deep learning and artificial intelligence, he has contributed significantly to neural network advancements. His work integrates cutting-edge technologies for intelligent data analysis and predictive maintenance. šŸ“ŠšŸ§ šŸ”

Profile

Education šŸŽ“

Yuanming Zhang obtained his Ph.D. in Information Science from Utsunomiya University, Japan, in 2010. His academic journey emphasized computational intelligence, machine learning, and advanced data analytics. He developed expertise in deep learning models, including convolutional and graph neural networks. His education laid a strong foundation for interdisciplinary research, integrating artificial intelligence with real-world applications. šŸ“ššŸ§‘ā€šŸŽ“šŸ“ˆ

Experience šŸ‘Øā€šŸ«

Yuanming Zhang has been an Associate Professor at Zhejiang University of Technology since completing his Ph.D. in 2010. His professional journey spans over a decade in academia, focusing on AI, neural networks, and knowledge graphs. He has supervised research projects, collaborated on industry applications, and contributed to advancements in predictive analytics and condition monitoring. His expertise extends to teaching, mentoring, and interdisciplinary AI applications. šŸ«šŸ¤–šŸ“”

Research Interests šŸ”¬

Yuanming Zhang specializes in deep learning, attention mechanisms, graph neural networks, and AI-driven predictive analytics. His research explores neural architectures for data processing, knowledge representation, and condition monitoring. His expertise spans convolutional networks, LSTMs, GRUs, and deep belief networks. His work contributes to advancements in AI-driven diagnostics, intelligent systems, and real-time health monitoring applications. šŸ§ šŸ“ŠšŸ–„ļø

Awards & Recognitions šŸ…

Yuanming Zhang has received recognition for his contributions to AI, machine learning, and data analytics. His work in deep learning and knowledge graphs has earned him accolades from research institutions and conferences. His papers in neural networks and predictive maintenance have been highly cited, solidifying his impact in the field. His research excellence has been acknowledged through grants and academic distinctions. šŸŽ–ļøšŸ“œšŸ”¬

PublicationsĀ 

 

Gokhan Yildirim | Marketing analytics | Best Researcher Award

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Dr. Gokhan Yildirim | Marketing analytics | Best Researcher Award

Gokhan Yildirim is an Associate Professor of Marketing at Imperial College Business School, specializing in marketing analytics and return on investment. His expertise spans digital marketing, long-term marketing effectiveness, and customer mindset metrics. With a strong foundation in applied time series econometrics and machine learning, he has made significant contributions to the field of marketing science. Yildirim has held academic positions at Lancaster University and has been a visiting researcher at Tilburg University. His research has been widely published in top-tier journals, influencing both academia and industry.

Profile

Education šŸŽ“

Gokhan Yildirim earned his PhD in Business Administration and Quantitative Methods from Universidad Carlos III de Madrid (UC3M) in 2012, with a dissertation on marketing dynamics. His academic journey began with a BA in Business Administration (1999ā€“2003) and an MSc in Quantitative Methods (2003ā€“2006) from Marmara University, Istanbul. He also conducted research as a visiting scholar at Tilburg University, Netherlands, further strengthening his expertise in marketing analytics and econometrics.

Experience šŸ‘Øā€šŸ«

Yildirim has been an Associate Professor of Marketing at Imperial College Business School since 2019, following his tenure as an Assistant Professor from 2016 to 2019. Before that, he was an Assistant Professor of Marketing Analytics at Lancaster University (2012ā€“2016). His industry collaborations focus on marketing resource allocation, customer analytics, and data-driven decision-making. His research integrates econometric modeling and machine learning to optimize marketing strategies and enhance business performance.

Research Interests šŸ”¬

Yildirim’s research centers on return on marketing investment, digital marketing effectiveness, and customer mindset metrics. He applies advanced econometric and machine learning techniques to analyze marketing resource allocation and long-term advertising impacts. His work explores how marketing strategies influence consumer behavior and business growth, contributing to both academic literature and real-world marketing practices

Awards & Recognitions šŸ…

Yildirim has received several prestigious awards, including the 2017ā€“2018 Gary Lilien ISMS-MSI-EMAC Practice Prize for his work on multichannel marketing at Lā€™Occitane. He has also secured multiple research grants, such as the Wharton Customer Analytics Initiative (2015ā€“2016) and the Spanish Ministry of Science and Innovation grants (2012ā€“2018). His contributions have been recognized through funding from AiMark and other leading research bodies, further cementing his influence in marketing analytics.

Publications šŸ“š

Meryem Yankol-Schalck | Insurance and Machine Learning | Best Researcher Award

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Assist. Prof. Dr. Meryem Yankol-Schalck | Insurance and Machine Learning | Best Researcher Award

 

Profile

Education

She holds a Ph.D. in Econometrics and Machine Learning from the University of Orleans (2018ā€“2022), where she investigated new machine learning approaches for financial fraud detection and survival analysis in the insurance industry under the supervision of S. Tokpavi. In addition, she earned a Data Science Certificate (Executive) from the Institute of Risk Management (IRM) in 2016ā€“2017. Her academic background also includes a Masterā€™s degree in Mathematical Engineering (Applied Statistics) from Paris-Sud University (2004ā€“2007) and a Masterā€™s degree in Mathematics from the University of Marmara in Istanbul (1995ā€“1999). Since September 2022, she has been an Assistant Professor of Data Science at IPAG Business School in Nice and Paris. With extensive experience in the insurance sector, she integrates her professional insights into the classroom, emphasizing practical AI applications. Her curriculum reflects the latest trends in data science, fostering a dynamic learning environment tailored to students’ needs. She adapts resources and pedagogical methods to specific course objectives, utilizing tools such as Tableau for data visualization and exploring real-world business applications, including Netflix, Uber, ChatGPT, Gemini, and facial recognition technologies.

 

Work experience

She has held various academic and professional roles, combining her expertise in data science, machine learning, and business analytics. From September 2022 to January 2023, she was an adjunct faculty member at the International University of Monaco, where she taught Mathematics for Business. Prior to that, from September 2021 to August 2022, she served as an adjunct faculty member at IPAG Business School (Nice), teaching courses such as ā€œData Analysis for Business Managementā€ (BBA3), ā€œData Processingā€ (MSc, e-learning), ā€œDigital and Salesā€ (GEP 5th year), and ā€œIntroduction to Statisticsā€ (BBA1). Between September 2020 and October 2021, she was an adjunct faculty member at EMLV (Paris), where she taught ā€œQuantitative Data Analytics ā€“ SPSSā€ (GEP 4th year, hybrid learning) and supervised masterā€™s theses for GEP 5th-year students.

In addition to her academic roles, she has extensive experience in the consulting and insurance sectors. From March to November 2020, she worked as a Senior Consultant at Fraeris (Paris), supporting clients in project development and providing technical solutions. She collaborated with the ā€œCaisse de PrĆ©voyance Socialeā€ (CPS) of French Polynesia, modeling healthcare expenditures using machine learning techniques. She developed predictive models to analyze healthcare costs from both the insured’s and CPSā€™s perspectives, offering actionable insights and data-driven forecasts to aid long-term financial planning. Prior to that, in 2019ā€“2020, she was a Senior Manager in Pricing & Data P&C at Addactis (Paris), where she supported clients in project development, innovation, and strategic planning. As an expert referent for ADDACTISĀ® Pricing software, she worked on database processing for BNP Paribas Cardif, facilitating APLe software operations for quarterly account closings.

Memberships and Projects:

ā€¢ Membership of the American Risk and Insurance Association (ARIA)
ā€¢ Membership of the academic association AFSE.
ā€¢ Member of the RED Flag Project of the University of OrlĆ©ans in cooperation with CRJPothier.
ā€¢ Participation at 3 Erasmus+ Projects: Artificial Intelligence to support Education (EducAItion).
ā€¢ Virtual Incubator Tailored to All Entrepreneurs (VITAE).
ā€¢ Artificial Intelligence in high Education (PRAIME),

Research topics:

Studies focus on the application of data science techniques to business issues, particularly in the insurance
sector, and on climate change. Another topic of study is the relationship between AI and education.

Publication

  • Yankol-Schalck, M. (2023). Auto Insurance Fraud Detection: Leveraging Cost Sensitive and Insensitive
    Algorithms for comprehensive Analysis, Insurance: Mathematics and Economics.(
    (https://www.sciencedirect.com/science/article/abs/pii/S0167668725000216)
    Banulescuā€Radu, D., & Yankolā€Schalck, M. (2024). Practical guideline to efficiently detect insurance fraud
    in the era of machine learning: A household insurance case. Journal of Risk and Insurance, 91(4), 867-
    913.
    Yankol-Schalck, M. (2022). A Fraud Score for the Automobile Insurance Using Machine Learning and
    Cross-Data set Analysis, Research in International Business and Finance, Volume 63, 101769.
    Schalck, C., Yankol-Schalck, M. (2021). Failure Prediction for SME in France: New evidence from
    machine learning techniques, Applied Economics, 53(51), 5948-5963.
    On- going research:
    Yankol-Schalck (2025). Auto Insurance Fraud Detection: Machine Learning and Deep Learning
    Applications, submitted in Journal of Risk and Insurance.
    Schalck, C., Yankol-Schalck, M. (2024). Churn prediction in the French insurance sector using Grabit
    model, revision in Journal of Forecasting.
    Schalck, C., Seungho, L., Yankol-Schalck, M. (2024). Characteristics of firms and climate risk
    management: a machine learning approach. Work in progress for The Journal of Financial Economics.
    Yankol-Schalck M.and Chabert Delio C., (2024). The application of machine learning to analyse changes in
    consumer behaviour in a major crisis. Work in progress.
    Yankol-Schalck M. and Nasseri A. (2024).An investigation into the integration of artificial intelligence in
    education: Implications for teaching and learning methods. Work in progress.