Vanja Bevanda is a Full Professor at the Faculty of Economics and Tourism, University of Primorska in Pula. She holds a PhD from 2002 and has been employed at the institution since 2005, contributing to the Department of Quantitative Methods. Her educational background includes a BSc (1989), MSc (1995), and PhD (2002). Research Interests : Her work focuses on decision analytics, business intelligence, artificial intelligence applications in SMEs, data mining for customer behavior analysis, tourism development strategies, and the integration of technology in education. She explores topics like AI-driven managerial journeys, microchip implant adoption, and post-pandemic technology use trends. Publications : Recent articles highlight her contributions to AI transformation in SMEs, sentiment analysis during the pandemic, and mobile BI adoption in SMEs. Her research spans both theoretical frameworks and practical case studies across Croatia and beyond. Teaching : She teaches courses including Business Decision-Making, IT Project Management, and Database Systems. She emphasizes bridging theoretical knowledge with practical skills in information systems education.
Katharina Kaiser is affiliated with TU Wien's Fachbereich Software Services. She specializes in medical informatics with a focus on computerized clinical guidelines and healthcare system optimization. Her work bridges temporal data analysis, information extraction from clinical texts, and workflow modeling in medical contexts. Key areas: Clinical decision support systems, guideline implementation frameworks, temporal logic in healthcare processes Notable contributions: Development of TimeML-based clinical guideline modeling, heuristic methods for condition-action sentence identification Her research emphasizes semantic enrichment of medical documents and interactive visualization tools for therapy planning and patient data correlation. Collaborations include the PROTOCOL project and ReMINE deliverables in adverse risk management.
Prof. Dimitrios Karampinos is a Professor at the Technical University of Munich (TUM), leading the Experimental Magnetic Resonance Imaging group within the TUM School of Medicine and Health. He specializes in developing novel MRI techniques for quantitative biomarker discovery, focusing on musculoskeletal, metabolic, and oncological applications. His career includes a PhD from the University of Illinois (2008), postdoctoral research at UCSF (2009–2012), and leadership roles at TUM since 2012. Prof. Karampinos has pioneered advancements in MRI reconstruction, signal modulation, and biomarker validation for clinical translation. Educations: BSc in Mechanical Engineering (National Technical University of Athens, Greece), PhD in Biomedical Engineering (University of Illinois, Urbana-Champaign, 2008). Research Interests: Development of MRI measurement techniques, quantitative biomarkers for disease diagnosis, and improving therapy monitoring. Key areas include musculoskeletal disease imaging, metabolic disorder assessment, and oncology applications. His work emphasizes translating research into clinical practice through innovations like accelerated imaging, artifact correction, and AI-driven analysis. Awards: ERC Starting and Proof of Concept Grants (2015, 2019), TUM Supervisory Award (2020), ISMRM Junior Fellow (2011). Grants: Multiple ERC grants for MRI method development. Labs/Teams: Leads the Experimental Magnetic Resonance Imaging group at TUM, collaborating on clinical and technical MRI advancements.
Robert Charles Wallon serves as a Teaching Assistant Professor in Biomedical and Translational Sciences and Director of Academic Support at the Carle Illinois College of Medicine, University of Illinois. His work focuses on innovative educational approaches for medical students through technology-enhanced learning environments. Dr. Wallon's research interests include: Augmented Learning Environments Gesture-based Technology in Education Problem-Based Learning for Medical Students Virtual Reality Applications in Medical Education Spaced Repetition Learning Techniques USMLE Preparation and Student Wellness His scholarly work demonstrates a commitment to improving medical education through technological innovation and evidence-based teaching practices. Wallon's research examines how different learning modalities impact medical student comprehension and retention, with particular attention to gesture-based interactions and virtual reality environments. Recent publications highlight his focus on evaluating educational software tools, implementing effective study techniques, designing holistic support systems for high-stakes exams, and developing innovative learning environments. His work bridges educational theory with practical applications in medical training. Wallon collaborates extensively with colleagues at the Carle Illinois College of Medicine, including Roberto Galvez and Rachel Lindgren, demonstrating interdisciplinary work across medical education, educational psychology, and clinical training domains.
Julia A. Palacios is an Associate Professor of Statistics and Biomedical Data Science at Stanford University, with a courtesy appointment in Biology. She leads the Palacios Lab, focusing on developing statistical methods for evolutionary genomics, infectious diseases, and stochastic processes impacting public health. Her work integrates Bayesian nonparametric techniques, probabilistic AI, and computational statistics to address challenges in genetics, health, and cancer research. Her educational background includes a PhD in Statistics and postdoctoral research in computational biology. Current lab members include postdocs Bingjing Tang and Isaac Goldstein, PhD students Yi-Ting Tsai, Ivan Specht, Julie Zhang, and Leda Liang, and undergraduate researcher Shinnosuke Yagi. Former postdocs like Airam Blancas and Jaehee Kim have moved to faculty positions at ITAM and Cornell, respectively. Research funding includes NIH, NSF, Sloan Foundation grants, and the Terman Fellowship. Key contributions span phylodynamic modeling, coalescent theory, and real-time pathogen surveillance. Her lab's software tools include phylodyn (R package for phylodynamic inference) and adaPop (Bayesian population dynamics inference). Awards include the Sloan Research Fellowship and Gabilan Fellowship. Teaching roles include courses like Stats 376 and Stats 305A . Her lab actively recruits students and postdocs for research in evolutionary stochastic processes and biomedical data science. Labs/Teams: Palacios Lab at Stanford's Department of Statistics, collaborating with institutions globally on pandemic tracking and genomic studies. Current projects focus on multifurcating trees in infectious diseases, Bayesian nonparametric coalescent models, and computational tools for public health.
John Ryan is a Lecturer at Monash University's Department of Medical Imaging and Radiation Sciences. With a clinical background as a radiation therapist spanning Ireland, England, and Australia, he combines practical expertise with academic leadership. His work focuses on enhancing radiation therapy education through blended teaching methods and innovative software tools like a personal dosimeter app. Honours Bachelor of Science (Radiation Therapy), Trinity College Dublin Master of Medical Imaging Science (Hybrid Imaging), University of Sydney Part-time PhD candidate at RMIT University on Functional Imaging-Guided Radiotherapy John's research bridges radiation therapy and functional imaging for glioblastoma treatment planning, with recent publications exploring PET scan timing and digital education tools. His work emphasizes radiation safety and technological integration in clinical workflows. Scientific Awards include the RMIT School of Health and Biomedical Science Team Award (2019), and dual accolades from Trinity College Dublin: the St Luke’s Award and University Gold Medal (2010).
Dr. Sam S. Ramanujan is Professor of Computer Information Systems and Analytics at the University of Central Missouri , affiliated with the Harmon College of Business and Professional Studies . He teaches advanced object-oriented programming and software engineering courses, combining over two decades of academic expertise with substantial industry experience in complex system deployment. Doctor of Philosophy in Information Systems (University of Houston, 1995) MBA in CIS and Quantitative Analysis (University of Arkansas, 1989) PGDM in Information Systems (XLRI Institute of Management Studies, 1987) Bachelor of Arts (Hons) in Economics (University of Delhi, 1985) His research spans big data architecture , visual analytics , healthcare IT , and legal aspects of technology . He has published extensively on topics including software maintenance, e-commerce trust models, and cloud-based healthcare systems, with a focus on bridging technical and legal challenges in digital environments. Dr. Ramanujan's academic work shows a consistent focus on software engineering (1995–2017), healthcare IT (2004–2017), and legal-compliance frameworks (2000–2017). His publications demonstrate interdisciplinary expertise in merging technical systems with regulatory requirements . Best Paper Award , Journal of American Academy of Business, Cambridge (2006) He has contributed to pedagogical advancements in distributed computing curricula and collaborates with scholars like S. Kesh and S. Nerur. His industry experience informs real-world applications of his research in software maintenance and offshore operations.
Brent Lagesse is an Associate Professor at the University of Washington - Bothell , affiliated with the Division of Computing & Software Systems under the School of Science, Technology, Engineering & Mathematics . His research focuses on security in emerging environments , particularly secure machine learning and privacy in sensor-rich systems . Ph.D. in Computer Science from the University of Texas at Arlington (2009) Research Interests include: Detecting and locating hidden webcams Scalable AI/ML defense mechanisms Privacy-preserving video sharing AI systems for air quality prediction Automated yeast cell analysis CRISPR/CAS9 guide-donor libraries Article Trends : Recent publications emphasize secure machine learning for smart city applications, privacy-preserving technologies , and resource-constrained security in crowdsensing environments . Collaborative work spans cybersecurity education , environmental monitoring , and context-aware systems . Scientific Awards : Cybersecurity Fulbright Scholar (University of Cambridge, 2018) Johann-von-Spix International Guest Professorship (University of Bamberg, 2019-20) Advising & Grants : Advises current research students Neil Prakasam and Nicholas Handaja NSA grant ($96k) for GenCyber curriculum development (2022) NSF grant ($300k) for AI-enhanced cybersecurity workforce studies (2021) T-Mobile grants for ML security metrics and dataset anonymization (2020-2022) Laboratory : Leads the Security of Emerging Environments (SEE) Lab , developing practical and theoretical frameworks for smart city security and privacy-preserving technologies .
David Ellis is a Professor of Behavioural Science at the University of Bath's School of Management, with affiliations to the Applied Digital Behaviour Lab , Centre for Healthcare Innovation and Improvement , Centre for Business, Organisations and Society (CBOS) , and Institute for Digital Security and Behaviour (IDSB) . His interdisciplinary research bridges psychology and data science , focusing on digital technologies' impact on human behavior and healthcare systems. Ellis earned a PhD in Psychology (2013), MSc in Psychology (2009), and MA in Psychology (2008) from the University of Glasgow. His work addresses health inequality , digital ethics , and open research practices , influencing NICE guidelines and UK government reports. Recent publications highlight his leadership in computational reproducibility and digital behavioral interventions , including tools like Optimeet for attendance optimization and frameworks for ethical data exploration (DECIDE). His research spans healthcare planning , cybersecurity , and social data science , with over 85 peer-reviewed articles. Scientific awards include: Royal College of General Practitioners – Research Paper of the Year (2020) Dean's Award for Research Communication and Translation (2021) Stanford-Elsevier Top-Cited Scientist (2022) Doctoral Recognition Award (2023) Ellis supervises doctoral students and leads EPSRC-funded projects like Co-designing Technological Solutions for Loneliness and Digital Health Hub Pilots. He chairs the Social Sciences Research Ethics Committee and contributes to UKRI and Wellcome-funded initiatives.
Xin Wang is a Professor at Fudan University's School of Computer Science, specifically within the Department of Communication Science and Engineering and affiliated with the State Key Laboratory of ASIC and System in Shanghai, China. With 185 publications spanning two decades (2003-2025), Wang maintains an exceptionally active research profile, particularly evident in recent high-output years including 22 publications in 2019, 19 in 2021, and 13 in 2024. The research portfolio demonstrates deep collaboration networks, most notably with Yang Chen (45 co-authored papers), Yangfan Zhou, and Qingyuan Gong. Wang's research spans multiple critical areas in computer science, with significant contributions to networking systems (particularly CDN optimization, HTTP/3 implementation, and IPv6 infrastructure), software engineering (focusing on work rhythms, testing methodologies, and GUI analysis), mobile applications (including healthcare implementations and accessibility features), and security (especially account security and fraud detection in e-commerce). The interdisciplinary nature of the work is evident through applications in healthcare, e-commerce, campus safety, and IoT systems. Analysis of recent publications (2023-2025) reveals a strong trend toward practical system implementations addressing real-world challenges. The research demonstrates a consistent pattern of moving from theoretical foundations to deployable solutions, with particular emphasis on optimizing performance in networking systems, enhancing security in digital platforms, and improving user experience across diverse application domains. The work frequently incorporates machine learning techniques to solve complex system problems while maintaining practical applicability. While specific grant information isn't detailed in the publication records, the extensive collaboration network spanning multiple institutions in China and internationally suggests substantial research funding support. The consistent publication output across top venues including IEEE/ACM Transactions, INFOCOM, SIGCOMM, and ICSE indicates sustained research productivity and impact.
Vladimir Filkov is a Professor in the Department of Computer Science at the University of California, Davis, College of Engineering. He leads two research labs: the DECAL Lab and the AI for Health Lab. He is actively engaged in research, teaching, and service, with a focus on open-source software sustainability, AI in healthcare, and data science. He has held leadership roles such as General Chair of ASE 2024 and inaugural Director of Translational Data Science at UCD DataLab. Professor, Department of Computer Science, UC Davis Director, DECAL Lab Director, AI for Health Lab General Chair, ASE 2024 Director of Translational Data Science, UCD DataLab (2020–2024) His research centers on the sustainability of open-source software, using socio-technical and governance data to forecast project success and evolution. He also investigates AI applications in health, particularly multimodal models for atrial fibrillation and NLP in medicine. His work bridges empirical software engineering, data science, and healthcare informatics, with strong community engagement through forums and podcasts. He has led major NSF, Google, and Sloan Foundation-funded projects on OSS sustainability and UC-wide OSPO initiatives. The recent publications highlight a strong trend in empirical software engineering, particularly around open-source governance, lifecycle analysis, and sustainability forecasting. There is also a growing emphasis on health-related AI, including multimodal models for cardiac conditions and natural language processing in clinical settings. The work combines data-driven modeling with real-world impact in both software ecosystems and healthcare. ACM Distinguished Member ACM SIGSOFT Distinguished Paper Award Vladimir Filkov has successfully secured competitive grants from the NSF (GCR, Phase I and II), Google, and the Sloan Foundation. He advises PhD students including Likang Yin, Raiyan Jahangir, and postdoc Stefan Stanciulescu. His mentoring spans topics in software engineering, AI, and computational biology. He has organized major research forums and collaborative initiatives across the UC system. He leads the DECAL Lab and the AI for Health Lab at UC Davis, fostering interdisciplinary research in software sustainability and healthcare AI. These labs support graduate students, postdocs, and collaborative projects with national and international partners.
Laurence Brooks is Professor of Information Systems at the Information School (iSchool), University of Sheffield, and Visiting Professor of Technology and Social Responsibility at De Montfort University’s Centre for Computing and Social Responsibility (CCSR). He has held significant academic leadership roles, including Director of the Doctoral College at DMU and Programme Coordinator at the iSchool. His work bridges technology and societal impact, with a focus on ethics, governance, and development. Education: PhD in Information Systems, University of Liverpool, UK BSc (Hons) in Psychology, University of Bristol, UK His research interests center on the ethical and societal implications of emerging technologies, especially in public sectors. He employs social theories such as Structuration Theory, Actor Network Theory, and Sociomateriality to investigate how ICT shapes and is shaped by individuals, groups, and institutions. Key domains include eGovernment, ICT4D, healthcare informatics, and digital policy. His methodological approach is predominantly qualitative, emphasizing interpretive and ethnographic methods. The recent publications reflect a sustained engagement with big data governance, digital public services in developing countries, and ethical AI. Trends show a focus on institutional arrangements, user trust, and socio-technical design in complex environments. His work often involves cross-cultural and international case studies, particularly in Nigeria, Bangladesh, and Zambia. Scientific Awards and Recognition: Brunel University London Star Award (2012) Shortlisted for Brunel Innovative Teaching Award (2012) Volunteer Spotlight Award, AIS (2008) ICIS Best Paper Nomination (2005) ECIS Best Paper Award (2000) Laurence Brooks has supervised several PhD students, including Ruairi Blake, Daniel Malok, Abigail Udoma, Tonii Leach, and Vincent Bryce. He has secured major research grants, including the €4 million EU Horizon 2020 TechEthos project and the €2.9 million SHERPA project, both focused on ethical dimensions of smart information systems. He is also a local PI for the EPSRC Horizon CDT on digital identity and data creativity. He is actively involved in research communities, having served as Past President of UKAIS, UKSS, and AIS SIG on Philosophy of IS. He contributes as an editorial board member for journals like Journal of the AIS and Information Technology and People , and has led numerous conference tracks and panels. He also co-chairs the ICT4D North of England group.
Óscar Andrey Herrera Sancho is a Full Professor at the University of Costa Rica and a researcher at the Atomic, Nuclear and Molecular Sciences Research Center. He holds a PhD in Physics from Leibniz University Hannover and has held roles including Head of the Physical Metrology Department at the National Metrology Institute of Costa Rica. His research spans Quantum Optics, Quantum Biology, Surface Physics, and Educational Physics, with interdisciplinary work in art conservation, biophysics, and cultural heritage science. Education: Postdoc Fellowship, Institute for Quantum Optics and Quantum Information, University of Innsbruck (2016) PhD in Physics, Leibniz University Hannover (2012) M.Sc. in Physics, University of Costa Rica (2008) B.Sc. in Physics, University of Costa Rica (2003) Research Interests: His work bridges quantum physics with cultural heritage preservation, including material analysis of historical artifacts, fungal biodeterioration studies, and pedagogical innovations linking literature and science. He also investigates surface physics phenomena relevant to astrophysical ice transitions and quantum gas systems. Scientific Awards: Alexander von Humboldt Foundation Fellow Advising & Grants: While specific grants are not listed, his extensive co-authorship network and leadership roles indicate active collaboration in interdisciplinary research. His work on cultural heritage has involved student teams in material analysis and software development for artifact preservation. Labs & Teams: Leads the Spatially Resolved Ultracold Rydberg Physics group at the 5th Institute of Physics, focusing on quantum systems and their applications in precision measurement and cultural heritage science.
EUNKYENG BAEK is an Associate Professor in the Department of Educational Psychology at Texas A&M University. She specializes in multilevel modeling (MLM) for analyzing educational and psychological data, with a focus on longitudinal and time-series data such as single-case experimental design (SCED) data. Her research bridges methodological advancements and applied studies, addressing gaps in data analysis techniques for SCED and educational datasets. **Education**: Ph.D., Educational Research, Measurement and Statistics, University of South Florida (2015) M.A., Psychometrics, Korea University (2006) B.A., Psychology, Sungshin Women University (2003) **Research Interests**: Dr. Baek’s work centers on Bayesian analysis, meta-analysis of single-case studies, and the integration of machine learning techniques in educational contexts. She explores statistical methods to handle autocorrelation, heterogeneity, and variance in SCED datasets, contributing to robust methodologies for synthesizing research findings. **Grants & Leadership**: She has led or co-led multiple grants, including projects on developing effect size toolkits for SCED meta-analyses and studying online learning readiness. She serves as an Action Editor for Behavior Research Methods and holds leadership roles in academic organizations like EREL. **Teaching**: She teaches courses in educational measurement, statistical analysis, and SCED data techniques, such as EPSY622 and EPSY661.
Dr. Francis L. Huang is a Professor in the Department of Educational, School, and Counseling Psychology at the University of Missouri-Columbia 's College of Education and Human Development. An applied quantitative methodologist , he teaches courses in program evaluation, multilevel modeling, and data management while researching school climate, bullying prevention, and large-scale educational data analysis. PhD in Research, Statistics, and Evaluation (University of Virginia) MA in Instructional Technology and Media (Teachers College, Columbia University) BS in Legal Management (Ateneo de Manila University) His methodological expertise spans clustered data analysis , plausible values modeling , and robust standard error estimation . He develops tools like the MLMusingR package for multilevel modeling in education research. Recent work focuses on 2025 grant-funded studies about data weighting in international assessments and 2024 open-access replication frameworks for nonexperimental datasets. He advocates for rigorous causal inference and equitable discipline policy analysis through projects like the National Center for Rural School Mental Health (funded by Institute of Education Sciences). Dr. Huang contributes to Missouri Prevention Science Institute as Methodology Co-Director and collaborates with interdisciplinary teams on school violence prevention and behavioral threat assessment systems. His 2023-2025 publications demonstrate technical innovations in three-level cluster-robust errors and missing data handling for large-scale assessments.
