Shui Yu | Reliability analysis and design optimization | Best Researcher Award

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Dr. Shui Yu | Reliability analysis and design optimization | Best Researcher Award

Yu Shui is an Associate Researcher at the University of Electronic Science and Technology of China, with a Ph.D. in Engineering and extensive academic and research experience in reliability analysis, robust design, and AI-driven robotics. He has previously held postdoctoral and lecturer roles at UESTC and Southwest Jiaotong University, respectively. His research spans intelligent systems, robust optimization, and reliability engineering, with publications in top-tier journals like Reliability Engineering & System Safety. His academic path reflects a strong commitment to developing advanced models and frameworks for time-variant reliability design and intelligent algorithms. He is an active researcher contributing to the frontiers of artificial intelligence in engineering systems.

Profile

Education 🎓

Yu Shui completed both his Bachelor’s (2009.09–2013.06) and Ph.D. (2013.09–2019.06) degrees at the University of Electronic Science and Technology of China (UESTC), majoring in engineering fields related to system reliability and optimization. His academic training provided a rigorous foundation in theoretical modeling, numerical simulations, and intelligent systems. During his doctoral studies, he focused on reliability design and probabilistic modeling under uncertainty, incorporating machine learning techniques into engineering optimization. He worked under distinguished mentors, gaining expertise in both the practical and theoretical aspects of engineering reliability. His Ph.D. research laid the groundwork for innovative solutions to complex, real-world reliability issues using AI methods.

Experience 👨‍🏫

Yu Shui started his academic career with a postdoctoral position (2019.07–2021.07) at UESTC, focusing on intelligent algorithms in reliability systems. From 2021.07 to 2024.03, he worked as a Lecturer at Southwest Jiaotong University, where he led courses and supervised research in design optimization and AI applications. In March 2024, he returned to UESTC as an Associate Researcher, contributing to high-impact projects in robotics and reliability engineering. Throughout his career, he has collaborated on interdisciplinary projects involving surrogate modeling, dynamic pruning methods, and AI-driven design optimization, earning recognition for both teaching and research contributions.

Research Interests 🔬

Yu Shui’s research centers on reliability analysis, robust design, intelligent robotics, and artificial intelligence. He develops optimization frameworks and surrogate models to improve the performance and resilience of complex engineering systems. His work incorporates Bayesian regression, dynamic pruning, and demand-objective frameworks for time-variant reliability-based design. His interdisciplinary focus bridges engineering with machine learning, pushing the boundaries of how intelligent systems can manage uncertainty in design and operations. He is particularly interested in integrating AI techniques into robust mechanical systems to enhance reliability in real-world applications.

Publications

  • Empirical Examination of the Interactions Between Healthcare Professionals and Patients Within Hospital Environments—A Pilot Study

    Hygiene
    2025-05-08 | Journal article
    DOI: 10.3390/hygiene5020020
    CONTRIBUTORS: Dimitris Charalambos Karaferis; Dimitris A. Niakas

  • Digitalization and Artificial Intelligence as Motivators for Healthcare Professionals

    Japan Journal of Research
    2025-01-01 | Journal article
    DOI: 10.33425/2690-8077.1170
    CONTRIBUTORS: Karaferis Dimitris; Balaska Dimitra; Pollalis Yanni

  • Workplace Violence in Healthcare: Effects and Preventive Measures and Strategies

    SunText Review of Case Reports & Images
    2024 | Journal article
    DOI: 10.51737/2766-4589.2024.141
    Part ofISSN: 2766-4589
    CONTRIBUTORS: Karaferis D; Balaska D

  • Enhancement of Patient Engagement and Healthcare Delivery Through the Utilization of Artificial Intelligence (AI) Technologies

    Austin Journal of Clinical Medicine
    2024-11-15 | Journal article
    DOI: 10.26420/austinjclinmed.2024.1053
    Part of ISSN: 2381-9146
    CONTRIBUTORS: Department of Economic Science, University of Piraeus, Piraeus, Greece; Dimitris Karaferis; Dimitra Balaska; Department of Economic Science, University of Piraeus, Piraeus, Greece; Yannis Pollalis; Department of Economic Science, University of Piraeus, Piraeus, Greece

Yun Kang | Mathematical Biology | Best Researcher Award

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Dr. Yun Kang | Mathematical Biology | Best Researcher Award

Yun Kang is a distinguished Professor of Applied Mathematics at Arizona State University 🏫, specializing in mathematical biology, complex adaptive systems, and nonlinear dynamical systems 🔬📊; with over 70 publications in high-impact journals 📝, Kang’s work bridges theory and modeling to solve biological, ecological, and social questions 🌍; a leader in mathematical research, she also champions women in STEM through mentoring and advocacy 🤝💡; her cutting-edge research, funded by the NSF 💰, explores multiscale modeling in social insects 🐜 and trust dynamics in human-automation interaction 🤖; as a dedicated educator and core faculty member at the Simon A. Levin Mathematical, Computational & Modeling Sciences Center 🧠, she has shaped both academic programs and future researchers 🌱📈.

Profile

Education 🎓

Yun Kang earned her Ph.D. in Mathematics from Arizona State University in 2008 🎓, focusing on mathematical biology 🧪; she completed an M.S. in Pure Mathematics at the University of Arizona in 2004 📐, with special research in random graphs 🔗; her academic journey began with a B.S. in Applied Mathematics from Shanghai Jiaotong University, China 🇨🇳, in 2002, where she concentrated on financial and computational mathematics 💹💻; this academic foundation provided a solid platform for her research into nonlinear systems and biological applications 🌿📊; Kang’s education path reflects global excellence 🌍, interdisciplinary rigor 🧠, and a passion for bridging mathematics with real-world complexity 🌐✨.

Experience 👨‍🏫

Yun Kang’s academic career began as an Assistant Professor at ASU in 2008 🧑‍🏫, after completing her doctorate 🎓; she advanced to Associate Professor in 2014 and became a full Professor in 2019 🌟; from 2016 to 2019, she served as Acting Director/Co-Director of the Simon A. Levin Mathematical, Computational & Modeling Sciences Center 🧠, promoting interdisciplinary collaborations 💡; beyond teaching, Kang holds roles as Core Faculty and Affiliated Faculty at ASU’s School of Mathematical and Statistical Sciences 📚; her career spans leadership, research, mentorship, and advocacy for diversity in mathematical sciences 💪🌸; each role reflects her commitment to both academic excellence and community empowerment 🏅📢.

Awards & Recognitions 🏅

Yun Kang’s excellence is reflected in her NSF-funded research grants 💰, numerous high-impact publications 📝, and her leadership in mathematical biology 🔬; she’s a proud and active member of top organizations: Association for Women in Mathematics 👩‍🔬, American Mathematical Society 📘, Society for Industrial and Applied Mathematics 🧠, and Society for Mathematical Biology 🌿; since 2009, she’s mentored young female mathematicians via the AWM mentor network 🤝💡; her recognition stems from both groundbreaking research and her role as a diversity advocate in STEM 🌸🌍; her distinguished honors underscore her dual commitment to advancing math and empowering future scholars 🌟👩‍🏫.

Research Interests 🔬

Yun Kang’s research bridges nonlinear dynamical systems ⚙️, stochastic models 🎲, and mathematical biology 🧬; she explores complex adaptive systems — from population dynamics 🦌, food webs 🌾, eco-epidemiology 🦠, to social insect colonies 🐜; her NSF-funded work dissects multiscale division of labor in insect societies 🐝; she also models trust dynamics in human-automation interactions 🤖, blending theoretical rigor with real-world relevance 🌎; her contributions illuminate evolutionary processes 🔄, ecological interactions 🌱, and behavioral modeling 🧠; Kang’s approach merges deep mathematical theory with empirical validation 📊, offering new tools for biological, ecological, and social system analysis 🚀📘.

Publications 

Pritpal Singh | Ambiguous set theory | Best Researcher Award

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Dr. Pritpal Singh | Ambiguous set theory | Best Researcher Award

Pritpal Singh is an Assistant Professor at the Department of Data Science and Analytics, Central University of Rajasthan, India. He earned his Ph.D. in Computer Science and Engineering from Tezpur (Central) University in 2015 and has held various academic and research positions in India, Taiwan, and Poland. His expertise includes soft computing, optimization algorithms, time series forecasting, image analysis, and machine learning. He has published extensively in high-impact journals like IEEE Transactions, Elsevier, and Springer. His research focuses on advanced computational techniques, including quantum-based optimization and fMRI data analysis. Dr. Singh has received prestigious research fellowships, including a Postdoctoral Fellowship from Taiwan’s Ministry of Science and Technology and an International Visiting Research Fellowship from Poland’s Foundation for Polish Science. His work significantly contributes to artificial intelligence, data science, and computational modeling, making him a key figure in these fields. 🚀📊📚

Profile

Education 🎓

Dr. Pritpal Singh obtained his Ph.D. in Computer Science and Engineering from Tezpur (Central) University, Assam, India, in 2015, specializing in soft computing applications for time series forecasting. He completed his Master in Computer Applications (MCA) from Dibrugarh University, Assam, in 2008, following a B.Sc. in Physics, Chemistry, and Mathematics from the same university in 2005. His academic journey began with Higher Secondary (2002) from the Assam Higher Secondary Education Council and HSLC (1999) from the Secondary Education Board of Assam. His doctoral dissertation focused on improving fuzzy time series forecasting models through hybridization with neural networks and optimization techniques like particle swarm optimization. His strong foundation in computational sciences, mathematics, and engineering has shaped his research in AI-driven predictive modeling, optimization, and data analytics. 🎓📚🔬

Experience 👨‍🏫

Dr. Singh has extensive academic and research experience. He is currently an Assistant Professor at the Central University of Rajasthan (since June 2022). Previously, he was an Assistant Professor at CHARUSAT University, Gujarat (2015-2019), and a Lecturer at Thapar University, Punjab (2013-2015). His research experience includes serving as an Adjunct Professor (Research) at Jagiellonian University, Poland (2020-2022) and a Postdoctoral Research Fellow at National Taipei University of Technology, Taiwan (2019-2020). Throughout his career, he has mentored students, led research projects, and contributed significantly to data science, artificial intelligence, and computational modeling. His global exposure has enriched his expertise in optimization, machine learning, and interdisciplinary AI applications. 🌍📊

Research Interests 🔬

Dr. Singh’s research revolves around ambiguous set theory, optimization algorithms, time series forecasting, image analysis, and machine learning. He specializes in hybrid computational techniques, particularly quantum-based optimization and soft computing applications. His work extends to fMRI data analysis, mathematical modeling, and simulation. His research has been published in leading journals such as IEEE Transactions on Systems, Elsevier’s Information Sciences, and Artificial Intelligence in Medicine. His focus on interdisciplinary AI applications, particularly in healthcare and data science, has positioned him as a key contributor to advancing machine learning methodologies. 🧠📊🤖Awards & Recognitions 🏅

Dr. Singh has received multiple prestigious fellowships and recognitions. In 2019, he was awarded a Postdoctoral Research Fellowship by the Ministry of Science and Technology, Taiwan. In 2020, he received the International Visiting Research Fellowship from the Foundation for Polish Science, Poland. His contributions to artificial intelligence, optimization, and data science have been recognized globally through research grants, invited talks, and publications in top-tier journals. His work in soft computing and AI-driven predictive modeling continues to impact both academic and industrial research. 🏅🎖️📜

Publications 📚

  • Scopus 1-2023: P. Singh, An investigation of ambiguous sets and their application to
    decision-making from partial order to lattice ambiguous sets. Decision Analytics
    Journal (Elsevier), 08, 100286, 2023.
  • Scopus 2-2023: P. Singh, A general model of ambiguous sets to a single-valued ambiguous numberswith aggregation operators. Decision Analytics Journal (Elsevier), 08,
    100260, 2023.
  • Scopus 3-2023: P. Singh, Ambiguous set theory: A new approach to deal with unconsciousness and ambiguousness of human perception. Journal of Neutrosophic and
    Fuzzy Systems (American Scientific Publishing Group), 05(01), 52–58, 2023.
  • Scopus 4-2022: P. Singh, Marcin W ˛atorek, Anna Ceglarek, Magdalena F ˛afrowicz, and
    Paweł O´swi˛ecimka, Analysis of fMRI Time Series: Neutrosophic-Entropy Based
    Clustering Algorithm. Journal of Advances in Information Technology, 13(3), 224–
    229, 2022.

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 (2018–2022), 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 2016–2017. Her academic background also includes a Master’s degree in Mathematical Engineering (Applied Statistics) from Paris-Sud University (2004–2007) and a Master’s degree in Mathematics from the University of Marmara in Istanbul (1995–1999). 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 “Data Analysis for Business Management” (BBA3), “Data Processing” (MSc, e-learning), “Digital and Sales” (GEP 5th year), and “Introduction to Statistics” (BBA1). Between September 2020 and October 2021, she was an adjunct faculty member at EMLV (Paris), where she taught “Quantitative Data Analytics – SPSS” (GEP 4th year, hybrid learning) and supervised master’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 “Caisse de PrĂŠvoyance Sociale” (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 CPS’s perspectives, offering actionable insights and data-driven forecasts to aid long-term financial planning. Prior to that, in 2019–2020, 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 ADDACTISÂŽ Pricing software, she worked on database processing for BNP Paribas Cardif, facilitating APLe software operations for quarterly account closings.

Memberships and Projects:

• Membership of the American Risk and Insurance Association (ARIA)
• Membership of the academic association AFSE.
• Member of the RED Flag Project of the University of Orléans in cooperation with CRJPothier.
• Participation at 3 Erasmus+ Projects: Artificial Intelligence to support Education (EducAItion).
• Virtual Incubator Tailored to All Entrepreneurs (VITAE).
• 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)
    Banulescu‐Radu, D., & Yankol‐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.