Sergio Ortobelli Lozza is a full professor in Applied Mathematics at the Department of Management , University of Bergamo (Italy), where he coordinates the Master in Economics and Finance and PhD program in Management Accounting and Finance . With a Ph.D. in Computational Methods for Financial and Economic Forecasting and a Master's in Mathematics, his research focuses on applying probability theory and operational research to economics and finance. His scholarly work includes over 180 refereed publications in prestigious journals such as: European Journal of Operational Research Journal of Banking and Finance Insurance: Mathematics and Economics Mathematical Problems in Engineering His research emphasizes: Portfolio optimization with applications to crisis scenarios Stochastic dominance for sector-based investments De Novo programming in renewable energy location systems Network reputation analysis in financial contexts Scientific achievements include: Best paper award at EBES Athens (2010) Principal Investigator for GACR research projects Editorial board member of Mathematical Problems in Engineering Contributor to PRIN research teams He actively participates in academic conferences and has served on organization committees for events like Managing and Modelling of Financial Risks (2013-2019) and ICSP XIII (2013). Current contact: sergio.ortobelli@unibg.it | ORCID: 0000-0003-4983-8165
Professor Georgina Maria Tinungki is a distinguished academic serving as a Full Professor at Hasanuddin University's Department of Statistics within the Faculty of Mathematics and Natural Sciences. She simultaneously holds an Associate Professor position at Makassar Merchant Marine Polytechnic in the Sea and Port Transportation Management department and serves as Head of Department at Hasanuddin University since September 2019. With a career spanning over three decades since her initial lecturer position at Christian University of Indonesia Paulus (1989-1991), Professor Tinungki has established herself as a leading figure in statistics and mathematics education in Indonesia. PhD in Educational Mathematics and Statistics from Indonesia University of Education (2016) PhD in Coastal and Marine Resources from IPB University (2005) MSc in Statistics from IPB University (2000) Dra in Mathematics (equivalent to Master's degree) Professor Tinungki's research spans multiple interconnected domains with particular emphasis on mathematics and statistics education, applied statistics, mathematical modeling, and coastal and marine resource management. Her recent work demonstrates a strong focus on financial applications, particularly examining dividend policy during the COVID-19 pandemic using sophisticated panel data approaches. She has made significant contributions to educational methodology through her research on Team-Assisted Individualization (TAI) cooperative learning models that enhance mathematical problem-solving, communication, and self-proficiency among students. Her methodological expertise includes advanced statistical techniques such as copula methods for risk assessment, geographically weighted regression, and robust statistical approaches for handling outliers and non-normal data. Postgraduate scholarship from Directorate General of Higher Education for 2nd PhD degree study (2011) Postgraduate scholarship from Directorate General of Higher Education for PhD degree study (2001) Postgraduate scholarship from Directorate General of Higher Education for Master degree study (1997) With 77 publications and 585 citations, Professor Tinungki maintains an active research program with recent publications spanning finance, education, and statistical methodology. Her work on dividend policy during the pandemic demonstrates rigorous application of both static and dynamic panel data models. In education research, she continues to refine cooperative learning approaches with particular attention to developing students' self-proficiency alongside technical skills. Her laboratory work focuses on applying statistical methods to real-world problems in finance, education, and resource management, often collaborating with colleagues across multiple institutions. Professor Tinungki leads Georgina Maria Tinungki's Lab, which focuses on interdisciplinary research bridging statistics, education, and practical applications. The lab environment emphasizes methodological rigor while addressing real-world challenges in financial modeling, educational assessment, and resource management. Current projects include analyzing market reactions to corporate policies during crises, developing innovative statistical teaching methods, and applying spatial statistics to socioeconomic issues in Indonesia.
Dr. Giulia Risca is a researcher in the Department of Medicine and Surgery at the University of Milano-Bicocca, School of Medicine and Surgery. She recently completed her doctoral thesis on Bayesian methods for basket trials in rare diseases under the supervision of Professor Stefania Galimberti. Her research spans multiple domains including clinical trial methodology, hematology/oncology, and proteomics. Dr. Risca's research interests focus on developing innovative clinical trial designs for rare diseases, with particular expertise in Bayesian statistics and basket trial methodology. Her work addresses critical challenges in rare disease research where limited patient populations make traditional trial designs impractical. She has made significant contributions to understanding how information can be borrowed across sub-trials while maintaining appropriate statistical properties. Additionally, she conducts important translational research in CAR-T cell therapy for leukemia and develops diagnostic algorithms for iron metabolism disorders. Analysis of Dr. Risca's publication record reveals a strong focus on methodological innovation in clinical trials combined with impactful clinical applications. Her work demonstrates expertise in bridging statistical theory with practical clinical research needs, particularly in areas with limited patient populations. The publications span multiple disciplines but maintain a cohesive thread of methodological rigor applied to challenging clinical problems. Dr. Risca actively collaborates with clinical researchers across multiple medical specialties, contributing her statistical expertise to studies in hematology, oncology, nephrology, and ophthalmology. Her work on CARCIK-CD19 cell therapy, rare disease trial design, and diagnostic algorithms for iron overload represents significant contributions to their respective fields. She participates in multicenter studies across European institutions, demonstrating her integration into the broader research community. Dr. Risca leads research activities in biostatistics and clinical trial methodology, supervising analytical components of multiple clinical studies. Her work often involves developing and implementing sophisticated statistical approaches to address complex research questions where traditional methods are inadequate, particularly in the context of rare diseases with small sample sizes.
Wang You-Gan is an accomplished academic researcher with over three decades of publication history spanning from 1991 to 2024. His work demonstrates a strong foundation in statistical methodology with applications across multiple domains including environmental science, fisheries management, machine learning, and computational statistics. His research has been published in high-impact journals across statistics, computer science, environmental science, and biology. Wang You-Gan's primary research interests include statistical modeling, regression analysis (particularly support vector regression), longitudinal data analysis, machine learning applications, and environmental statistics. His work shows a clear evolution from early fisheries and environmental applications to broader computational statistical methods with applications in energy systems, genomics, and cloud computing. His research demonstrates strong methodological development coupled with practical applications. His recent publications (2022-2024) show a strong focus on advanced regression techniques, particularly support vector regression with innovations in handling heterogeneous variances, autoregressive processes, and automatic hyperparameter selection. He has also expanded into machine learning applications for energy demand forecasting, air quality prediction, and data center optimization. His work often bridges theoretical statistical development with practical implementation across diverse domains. Wang You-Gan has received significant citation impact for his methodological contributions, with several papers accumulating over 100 citations. His collaborations span multiple institutions and disciplines, indicating his work's broad relevance across fields. His research has important applications in environmental monitoring, energy systems management, computational biology, and cloud computing infrastructure. The consistent publication record over more than 30 years demonstrates his sustained contribution to statistical methodology and its applications.
