Mart Laanpere is Professor of Mathematics and Computing Education at Tallinn University, where he has held various academic positions since 2003. His career includes leadership roles such as head of educational technology centers and extensive international collaborations. He completed his doctoral studies at Tallinn University and research master's at University of Twente, specializing in educational systems design. His research examines educational technology integration, semantic web applications for knowledge management, and didactics of STEM subjects. Current investigations focus on smart school architectures, flexible learning pathways, and computational thinking assessment. Research consistently emphasizes practical applications in Estonian and Baltic educational contexts. Recent publications demonstrate strong focus on curriculum innovation, particularly in computing education reform and mathematics pedagogy. Works frequently address technology integration challenges, digital competence frameworks, and validation of educational models across diverse learning environments. Honors and Awards: TLU Badge of Merit (2024) National Research Award in Social Sciences (2020) Order of White Star 5th Class (2017) Best PhD Thesis in Educational Sciences (2014) TLU Open University Trainer of the Year (2013) Most Entrepreneurial Researcher (2007, 2009) Supervises doctoral candidates and coordinates international research projects including EU-funded initiatives on digital competence frameworks. Leads the Smart Schoolhouse research stream developing reference architectures for technology-enhanced learning environments. Directs collaborative projects with European partners through EDEN Digital Learning Europe and ICEM. Serves on Estonia's national digital competence task force developing standards and assessment models for digital literacy across educational sectors.
Crystal Taylor is an Assistant Professor in the Department of Psychology within the College of Arts and Sciences at Appalachian State University, specializing in evidence-based approaches to school mental health and educator support. Her academic credentials include: Ph.D. in School Psychology (2019), University of Missouri–Columbia M.A. in School Psychology (2016), University of Missouri–Columbia B.A. in Psychology (2011), University of North Carolina–Greensboro Dr. Taylor's research program addresses critical gaps in universal screening for social-emotional and behavioral risks, with particular emphasis on implementation barriers in elementary schools. She investigates how teacher stress and burnout impact classroom effectiveness, develops social-emotional learning integration strategies (including innovative applications in mathematics instruction), and creates practical classroom management interventions. Her work bridges research and practice through direct school partnerships. Analysis of her 2019-2023 publications reveals increasing attention to teacher well-being as a foundational element of student success, alongside consistent focus on universal screening systems. Her research demonstrates practical translation of psychological science into classroom applications, with growing emphasis on telehealth-supported teacher mentorship and the co-occurrence of academic/behavioral risks. Mentorship: Actively advises graduate students with multiple co-authorship opportunities Research Impact: Publications inform school mental health practices nationally Current Focus: Teacher stress reduction and telehealth classroom support systems Dr. Taylor leads a dynamic research group collaborating with K-12 schools to implement and evaluate evidence-based practices, particularly focusing on scalable interventions for classroom management and teacher development through novel mentorship approaches.
Professor Rosalind Eggo is a Professor of Infectious Disease Dynamics at the London School of Hygiene & Tropical Medicine (LSHTM), where she works within the Department of Infectious Disease Epidemiology and Dynamics in the Faculty of Epidemiology and Population Health. She leads research at the intersection of mathematical modeling, infectious disease dynamics, and public health policy. Her work spans multiple research centers including the Centre for Mathematical Modelling of Infectious Diseases, Health in Humanitarian Crises Centre, and Global Health Economics Centre. Professor Eggo's research focuses on mathematical modeling of infectious disease transmission, vaccine impact assessment, and health inequalities. Her work addresses critical questions in epidemic preparedness, vaccine policy, and healthcare system responses to infectious disease threats. She has made significant contributions to understanding the transmission dynamics of influenza, norovirus, pneumococcal disease, and SARS-CoV-2, with particular attention to vulnerable populations and humanitarian settings. Her recent publication portfolio demonstrates a strong emphasis on mathematical modeling approaches to evaluate the health and economic impact of vaccination strategies across various settings, from national health systems to humanitarian crises. Her work frequently leverages large-scale healthcare data through platforms like OpenSAFELY to address pressing public health questions related to disease transmission, healthcare utilization, and health inequalities. Member of SAGE Subgroup on COVID-19 and Ethnicity (2020-2021) Member of SAGE Task & Finish Working Group on COVID-19 and Children (2020-2021) Member of SPI-M-O (2020) Member of WHO Technical Advisory Group on COVID-19 and Educational Establishments (2020) Professor Eggo leads multiple research grants funded by major organizations including the Medical Research Council, National Institute for Health Research, World Health Organisation, and Bill & Melinda Gates Foundation. Her current research portfolio addresses critical areas such as health inequality from infectious disease dynamics, epidemic preparedness toolkits, winter pressures in primary care, and next-generation influenza vaccines. She also leads the Women in Infectious Disease Modelling initiative at LSHTM.
Sven Bertel is a Professor at Flensburg University of Applied Sciences in the Faculty of Information and Communication, where he leads the CIVU – Center for Interaction, Visualization and Usability. His research focuses on Human-Computer Interaction, particularly in usability engineering, spatial cognition, and educational technology applications. Professor Bertel's research interests span several interconnected domains. His primary focus is on spatial cognition and mental rotation, where he investigates how people solve spatial problems and how these abilities can be trained through technology. A significant portion of his work applies these principles to medical ultrasound training through the development of SonoGame, a serious game designed to improve visuospatial skills for medical professionals. He also explores digital competencies development, particularly how user-centered software development can be integrated into educational contexts to prepare students for a digital world. His publication trends reveal a consistent focus on spatial abilities and educational technology since 2017, with recent work (2021-2024) increasingly emphasizing medical applications of spatial cognition research. His collaborations span multiple disciplines, particularly with medical researchers in ultrasound imaging and educators working on digital transformation in teaching. Professor Bertel is actively involved in several research projects related to digital competencies and medical training technologies. His leadership of the CIVU center indicates his role in coordinating interdisciplinary research efforts focused on interaction design, visualization techniques, and usability engineering. His teaching responsibilities include courses in the Applied Informatics program, with a specific focus on usability engineering. Through his work, he bridges theoretical research in spatial cognition with practical applications in medical training and educational technology.
Kijung Shin is an Associate Professor at KAIST (Korea Advanced Institute of Science and Technology), holding dual appointments in the Kim Jaechul Graduate School of AI and the School of Electrical Engineering (Computer Division). He leads the Data Mining Lab and teaches multiple courses including Graph Mining and Social Network Analysis, Data Mining and Search, and other foundational courses in electrical engineering and AI. Education Ph.D. in Computer Science, Carnegie Mellon University (February 2019) M.S. in Computer Science, Carnegie Mellon University (December 2017) B.S. in Computer Science and Engineering, Seoul National University (August 2015) B.A. in Economics (Double Major), Seoul National University (August 2015) Research Interests Professor Shin's research primarily focuses on data mining, graph algorithms, and network science, with particular expertise in hypergraph analysis, tensor decomposition, and graph neural networks. His work bridges theoretical foundations with practical applications, developing algorithms that can efficiently analyze complex real-world networks. His recent research has expanded into multimodal learning, integration of large language models with graph neural networks, and applications in recommendation systems, satellite imagery analysis, and biological data analysis. His approach combines rigorous mathematical analysis with practical implementation, resulting in numerous open-source software tools that have been widely adopted in both academia and industry. His research has significant implications for social network analysis, fraud detection, recommendation systems, and scientific discovery in various domains. Research Trends Professor Shin's recent publications show a clear trajectory toward more complex network structures, particularly hypergraphs that capture higher-order interactions beyond simple pairwise relationships. His work increasingly integrates traditional graph algorithms with deep learning approaches, especially focusing on how graph neural networks can be improved and made more interpretable. There's also a growing emphasis on practical applications in areas like satellite imagery analysis, medical data, and recommendation systems that address real-world challenges. Scientific Awards Received the PAKDD Best Survey Paper Award for 'Multi-Behavior Recommender Systems: A Survey' (2025) Selected as one of the best short paper candidates of ACM RecSys 2024 (top 7) for 'Revisiting LightGCN' (2024) Selected for oral presentation (2.6% of accepted papers) at AAAI 2024 for 'VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication Recommendation' (2024) Received the IEEE ICDM Best Student Paper Runner-up Award for 'TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions' (2023) Received the SIGKDD Best Research Paper Award and CogX Award for Best Student Paper in AI for 'FRAUDAR: Bounding Graph Fraud in the Face of Camouflage' (2016) Received the Best Senior Thesis Award from Seoul National University (2015) Received the Samsung Humantech Paper Award (1st in Computer Science) (2015) Teaching and Mentoring Professor Shin has taught multiple graduate and undergraduate courses at KAIST since 2019, including Graph Mining and Social Network Analysis, Data Mining and Search, and foundational courses in electrical engineering. He has also co-organized tutorials at major conferences including AAAI, KDD, ICDM, and CIKM on advanced topics in hypergraph neural networks and real-world hypergraph analysis. As the leader of the Data Mining Lab, he mentors numerous graduate students and postdoctoral researchers, fostering a collaborative research environment that has produced significant contributions to the field of data mining and network analysis. Research Leadership Professor Shin leads the Data Mining Lab at KAIST, which focuses on developing novel algorithms for analyzing complex networks and high-dimensional data. The lab has produced numerous influential software tools including D-Cube, M-Zoom, CoreScope, and DenseAlert, which are widely used in both academic research and industry applications. His research group maintains active collaborations with institutions worldwide and has received funding from various sources to support their innovative work in data mining and network analysis.
Giulia Cereda serves as Associate Professor in the Department of Statistics, Computer Science, and Applications 'G. Parenti' (DiSIA) at the University of Florence since 2025. Her academic journey includes prior roles as Fixed-term Researcher (RTD-b, 2022-2024), Research Fellow (2021-2022), and Swiss National Science Foundation Postdoc Mobility Fellow (2019-2021) at Leiden University and University of Florence. She holds a Joint PhD in Statistics from Leiden University and University of Lausanne (2011-2016), complemented by Master's and Bachelor's degrees in Mathematics from the University of Milan. Her research spans forensic statistics with focus on rare type match problems in DNA evidence evaluation, medical statistics applied to SARS-CoV-2 pandemic modeling, and machine learning implementations for biogeographical ancestry prediction. Recent publications demonstrate methodological innovations in Bayesian approaches for forensic evidence, compartmental modeling of epidemic dynamics, and supervised learning applications in population genetics. Analysis of her 14 most recent publications (2020-2025) reveals dual research thrusts: (1) forensic statistics addressing DNA mixture interpretation and rare haplotype matching through Bayesian frameworks, and (2) epidemiological modeling of smoking dynamics and SARS-CoV-2 transmission using compartmental models with uncertainty quantification. Her work bridges theoretical statistics with practical public health and forensic applications, frequently employing machine learning for complex prediction tasks. Supported by the Swiss National Science Foundation for postdoctoral research (2019-2021), she has contributed to pandemic response through Tuscan regional modeling and school-based screening strategies. Current office hours are Thursdays 3:00-4:00 PM by appointment, with ongoing research in forensic identification systems and epidemic forecasting methodologies.
John Olson serves as Professor and Associate Dean for Academic Programs and Innovation at the University of St. Thomas' Opus College of Business. He has been a core faculty member since 2004, previously chairing the Operations and Supply Chain Management Department (2009-2015) and directing the Business Analytics Program (2015-2020). Currently, he leads the Healthcare Research Center while teaching operations management, quality management, and analytics courses. His educational foundation includes: PhD in Operations Management from University of Nebraska MBA in Operations Management from St. Cloud State University BS in Economics and Mathematics from University of Minnesota Olson's research centers on healthcare technology transformation , specifically examining how blockchain and data systems can reduce costs while improving care quality. His work bridges operations management and healthcare ecosystems , exploring quality improvement programs, hospital organizational culture, and technology adoption patterns. He emphasizes practical applications, translating complex systems dynamics into strategic healthcare decision-making frameworks through experiential teaching methods. His publication trajectory reveals a distinct shift toward healthcare technology since 2010, with 70% of recent work focusing on healthcare operations. Earlier research (2002-2006) covered manufacturing optimization, nonprofit operations, and consumer behavior, demonstrating methodological versatility across assembly line design, internet purchasing models, and cross-cultural quality perception studies. His scientific recognition includes: Department of Management Teaching Award (2003) As an educator, Olson has mentored numerous graduate students while maintaining active industry consultation. His grant leadership spans roles as Principal Investigator at DePaul University (2000-2001) and Grant Coordinator at University of Nebraska (1996-1999), focusing on operational research in healthcare and manufacturing contexts. Through the Healthcare Research Center, he directs interdisciplinary teams investigating data-driven solutions for healthcare delivery systems, fostering partnerships between academic researchers and healthcare providers to implement evidence-based operational improvements.
