جستجوی استاد راهنمای دانشگاهی

• Statistical Power Analysis
• Optimal Experimental Design
• Multilevel Data Analysis
• +۴ مورد دیگر

Mirjam Moerbeek is an Associate Professor at the Department of Methodology and Statistics , Faculty of Social and Behavioural Sciences , Utrecht University , Netherlands. She holds a PhD in Applied Statistics (Maastricht University, 2000) and a MSc in Biometrics (Wageningen Agricultural University, 1996). Her work focuses on statistical power analysis and optimal experimental design for hierarchical and survival data , with over 100 peer-reviewed publications. Education MSc in Biometrics (cum laude), Wageningen Agricultural University (1996) PhD in Statistics, Maastricht University (2000) Research Focus Statistical power analysis Optimal design theory Multilevel modeling Survival analysis Cluster randomized trials Stepped-wedge trials Recent Publications Bayesian sample size determination for longitudinal studies Optimal group sizes in Bayes factor analysis Robustness in cluster randomized trials Cost-efficient designs for social and biomedical sciences Scientific Awards NWO Veni Grant (2003) NWO Vidi Grant (2008) NWO Open Competition Grants (2006, 2022) Ius Promovendi (2023) Organizational Roles President, European Association of Methodology (2023–2025) Vice President, European Association of Methodology (2021–2023) Co-Organizer, International Conference on Multilevel Analysis (2009–2022) Chair, VI European Congress of Methodology (2014) Editorial & Review Activities Editorial Board, PLOS ONE (2014–2017) Peer Reviewer, Tijdschrift voor Psychiatrie Scientific Committee, European Congress of Methodology Consultant for multilevel analysis software

• Computational Genetics
• Genetic Risk Modeling
• Bioinformatics
• +۵ مورد دیگر

Albert Smith is a computational geneticist and biostatistician at the University of Michigan School of Public Health , specializing in genetics and genomics research. Education : PhD in Biology, Massachusetts Institute of Technology (1993) BA in Biochemistry and Cell Biology, University of California-San Diego (1985) Research Focus : With over 30 years of experience, Dr. Smith develops and applies analytical methods in genetics, including genetic risk modeling and bioinformatics. His work emphasizes large-scale data management and the creation of data-intensive web applications and databases. He has contributed to major initiatives like the International HapMap Project, the CHARGE Consortium, and the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. Scientific Contributions : Recent publications highlight his role in analyzing expression/splicing quantitative trait loci, investigating persistent opioid use genetics, studying clonal hematopoiesis, and integrating multi-omics data across diverse populations. His expertise spans gene-environment interactions, rare variant analysis, and polygenic risk scoring. Methodologies : Dr. Smith utilizes computational tools including R , Perl , and SQL , and is recognized for leading interdisciplinary teams in tackling complex statistical challenges in genomics.

Johanna Schwarzمشاهده پروفایل

دانشیار

• Sleep Science
• Stress and Recovery
• Cognitive Neuroscience
• +۵ مورد دیگر

Johanna Schwarz is an Associate Professor at Stockholm University's Department of Psychology, specializing in sleep science and stress-related recovery mechanisms. Her research focuses on sleep's role in emotional regulation, cognitive performance, and aging through experimental and population-based studies. Stockholm University, Department of Psychology Division for Sleep and Alertness Research Research interests center on sleep's interaction with stress load, emotional processing, and cognitive function across adult lifespan. Key methodologies include: Magnetoencephalography (MEG) for neurophysiological measurements Polysomnography in real-life contexts Experience sampling method (ESM) for longitudinal tracking fMRI and EMG for brain-behavior relationships Recent publications examine age-related differences in sleep deprivation impacts, subjective sleep quality interpretation, and emotional regulation neuroimaging. While specific awards aren't detailed in current data, her collaborative studies with Stress Research Institute demonstrate significant contributions to sleep neuroscience. Key research trends show cross-disciplinary integration of: Cognitive neuroscience with gerontology Clinical sleep medicine with epidemiology Neuroimaging with behavioral economics Population health with neurophysiological biomarkers Stress response modeling with affective regulation Subjective sleep experience with objective measurement

Torben Bach Pedersenمشاهده پروفایل

استاد

• Data Engineering
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Professor Torben Bach Pedersen at Aalborg University's Department of Computer Science within The Technical Faculty of IT and Design is a leading expert in Data Engineering, Artificial Intelligence, and Energy Systems. With over 394 publications and 19 completed projects, he directs research at the Daisy – Center for Data-intensive Systems and leads innovations in energy flexibility and smart grid technologies. Key research areas: Data Warehousing, AI/ML, Energy Systems, Smart Grids Major projects: domOS (Smart Building OS), FEVER (Virtual Power Plants), DiCyPS (Cyber-Physical Systems) His research spans data-intensive systems, AI applications in energy management, and smart infrastructure development. Recent work focuses on transformer-based network AI and energy flexibility metrics. Scientific recognition includes: Æresdoktor (Honorary Doctor) at TU Dresden (2021) Best Paper Award Runner-Up (2019) WWW 2017 Best Demo Award Best Poster Award World Smart Grid Forum (2013) Member of Danish Academy of Technical Sciences (2013) As principal investigator and supervisor in 17 PhD projects, he advances AI-driven solutions for 6G wireless systems, smart buildings, and energy market optimization.

D.H.J. Epemaمشاهده پروفایل

استاد

• Distributed Systems
• Resource Management
• Scheduling Algorithms
• +۳ مورد دیگر

D.H.J. Epema is a full Professor at the Faculty of Electrical Engineering, Mathematics, and Computer Science at Delft University of Technology (TU Delft), specializing in Data-Intensive Systems . His research focuses on resource management, scheduling algorithms, performance analysis, trust/reputation systems, and blockchain applications in distributed systems. Research Interests: Distributed Systems, Blockchain, IoT, and Trust Management Notable Awards: Best Paper Awards at CCGrid (2010, 2014), MASCOTS (2013), and LCN (2021) Recent Publications emphasize blockchain integration in IoT, energy trading platforms, and decentralized identity systems. His work bridges theoretical frameworks with practical implementations in large-scale systems. Editorial Roles: Editor for ACM (2013–present) and Lecture Notes in Computer Science (2009–present)

Daniel Hummerمشاهده پروفایل

دانشیار

• Mineralogy
• Geochemistry
• Crystallography
• +۴ مورد دیگر

Daniel Hummer serves as an Associate Professor in the Department of Geology at Southern Illinois University, teaching courses including Mineralogy, Ore Deposits, and Earth's Deep Interior. His research program investigates fundamental thermodynamics and kinetics of mineral formation across diverse geological conditions, with emphasis on mineral stability, crystal structure evolution, and Earth's chemical history. Education: B.S. in Chemistry, Iowa State University, 2004 B.S. in Geology, Iowa State University, 2004 Ph.D. in Geoscience, Penn State University, 2010 Dr. Hummer's research integrates experimental, computational, and data-driven approaches across four primary domains: (1) Predictive mineral discovery through the Carbon Mineral Challenge, which identified 31+ new carbon-bearing species; (2) High-pressure mineral compression modeling using ion-based compressibility frameworks; (3) Development of MinKin software for quantifying mineral-fluid reaction kinetics; and (4) Reconstruction of Earth's redox evolution via transition-metal mineral occurrence databases. His work bridges mineral physics, geochemistry, and data science to address planetary-scale questions. Analysis of his publication record reveals strong thematic continuity in mineral ecology and data-intensive methodologies, with significant contributions to carbon mineral systems, manganese redox chemistry, and computational mineralogy. Key trends include network analysis of mineral associations, fractal distributions in crystallography, and planetary habitability assessments through hydrothermal mineral studies. Scientific Awards: No major scientific awards listed in available information Dr. Hummer leads active research projects supported by collaborations with Penn State, Oak Ridge National Lab, Brookhaven National Lab, Carnegie Institution, and international partners. While specific grant details aren't provided, his work on mineral discovery and kinetics modeling demonstrates sustained external funding. His Carbon Mineral Challenge initiative exemplifies large-scale collaborative science involving global mineralogical networks. His research teams include geoscientists from Johns Hopkins, Rensselaer Polytechnic Institute, and University of Arizona, focusing on mineral occurrence databases and redox evolution studies. Current fieldwork targets thallium-arsenic deposits in North Macedonia for novel mineral discovery, while computational efforts expand MinKin's capabilities for complex mineral reaction systems.

Malka Gorfine Orgadمشاهده پروفایل

استاد

• Survival Data
• Non-parametric Statistics
• Biostatistics
• +۵ مورد دیگر

Professor Malka Gorfine Orgad is a distinguished faculty member in the Department of Statistics and Operations Research at the School of Mathematical Sciences, Tel Aviv University. She has held the position of Professor since 2016, following her promotion from Associate Professor (2014-2016). Additionally, she maintains a long-standing affiliation as an Affiliate Investigator at the Division of Public Health Sciences, Fred Hutchinson Cancer Research Center in Seattle since 2003. Prof. Gorfine Orgad earned her MA (1994) and PhD (1999) in Statistics from the Hebrew University of Jerusalem under the supervision of Professors Banjamin Yakir and David Zucker. Her academic journey includes previous appointments as Associate Professor at Harvard School of Public Health (2011-2012) and at the Technion - Israel Institute of Technology (2010-2014), where she served as Senior Lecturer prior to that. Her research focuses on survival data analysis, non-parametric statistics, biostatistics, and machine learning. Specifically, she investigates inference based on Survival Deep Learning, natural experiment methods for causal inference, and various challenges of causal inference with survival outcomes. She has developed several innovative methodologies for survival analysis, particularly in the areas of competing risks, semi-competing risks, and illness-death models. Analysis of her recent publications reveals a strong emphasis on practical applications of survival analysis methodology to real-world health problems, particularly related to cancer and infectious diseases like COVID-19. Her work frequently bridges theoretical statistical development with practical implementation through software packages, demonstrating her commitment to making advanced methods accessible to applied researchers. Associate Editor of JASA Applications and Case Studies (2022 - present) Associate Editor of EJS (2022 - present) Associate Editor of Scandinavian Journal of Statistics (2021 - 2024) Co-Editor of Biometrics (2017 - 2019) Associate Editor of Biometrics (2009 - 2016) Member of Editorial Board of Lifetime Data Analysis (2013 - 2016) Prof. Gorfine Orgad actively mentors students at all levels, currently supervising one postdoc, two PhD students, and four MSc students, with numerous former students who have completed their degrees under her guidance. She serves as co-chair of the STRATOS TG8 (Survival Analysis Topic Group), which provides guidance on survival analysis methods for observational studies. She has also developed multiple software packages including PyMSM, PyDTS, frailty-LTRC, HHG, and others that implement her methodological contributions for the broader research community.

Ekaterina Gilmanمشاهده پروفایل

پژوهشگر

• Data Analytics
• Context Modeling
• Machine Learning
• +۶ مورد دیگر

Ekaterina Gilman, D.Sc. (Tech), is a Researcher at the University of Oulu's Center for Ubiquitous Computing, Faculty of Information Technology and Electrical Engineering. She is supported by the Academy of Finland and has collaborated with Northern Finland Biobank Borealis and the Centre for Health and Technology. Her research spans Data Analytics Ubiquitous Computing Machine Learning Internet of Things Smart Environments with applications in intelligent urban environments, stream reasoning, and wireless communications. Recent publications highlight trends in Edge computing architectures Concept drift detection Social distancing monitoring Well-being measurement Smart city data challenges . She has participated in projects funded by the European Regional Development Fund, Business Finland, Academy of Finland, and EU Horizon 2020. She serves as a reviewer and organizer in scientific communities and has over 30 publications.

Gintautas Grigasمشاهده پروفایل

دانشیار

• Programming teaching methodology
• Software localization
• Computer terminology
• +۳ مورد دیگر

Gintautas Grigas is an Associate Professor and Affiliated Scientist at the Institute of Mathematics and Informatics , Vilnius University, Lithuania, serving within the Educational Systems Group . Since joining the Institute in 1965, he has become one of Lithuania’s most prolific authors in computer science education and software localization. Education: 1959 – Graduated, Faculty of Electrical Engineering, Kaunas Polytechnic Institute (now Kaunas University of Technology) 1970 – Candidate of Technical Sciences (post-graduate), nostrified 1992 to Doctor of Mathematical Sciences Continuing education courses in programming languages, data types and programming technology at Vytautas Magnus University, Vilnius University and Vilnius Pedagogical University Research Interests Grigas’ scientific work centres on four tightly interwoven themes: abstract data types , programming teaching methodology , computer terminology , and software localization . His contributions range from theoretical analyses of data structures to practical guidelines for localizing major software suites into Lithuanian. He has also pioneered studies on how interface defects influence national language culture and on letter-frequency analyses supporting cryptography and linguistic research. Publication Trends Across more than 150 scholarly and professional works, a clear progression is evident: early focus on programming fundamentals and Lithuanian computing history, followed by intensive investigation of localization pedagogy, and more recently, mobile-device text input and cultural impacts of internationalization errors. Collaborative volumes such as the Encyclopedic Dictionary of Computing and the textbook Fundamentals of Software Localization have become standard references in Lithuanian computer science curricula. Awards & Recognition No specific competitive awards are listed; however, his sustained output—40 books, 100+ scientific articles, 250+ popular/professional articles—is itself a testament to national academic recognition. Doctoral Advising & Grants While individual doctoral students are not named, Grigas has long contributed to doctoral education through the Institute’s doctoral committees and study guides. His research has been supported by national projects on software localization and educational technology, although explicit grant numbers are not provided in the text. Labs & Teams He carries out his work within the Educational Systems Group , a subdivision of the Institute of Mathematics and Informatics, located at Akademijos St. 4, Vilnius. The group investigates learning technologies, curriculum design, and the societal impact of digital systems, providing a collaborative environment for his ongoing projects.

Jeremy M. Mikeczمشاهده پروفایل

پژوهشگر

• Digital Humanities
• Spatial History
• Data Visualization
• +۵ مورد دیگر

Jeremy M. Mikecz is a Researcher at Dartmouth College's Research Data Services , where he supports computational and data-intensive research in the arts, humanities, and social sciences. His work bridges digital humanities, spatial history, and Indigenous studies, with a focus on recovering marginalized histories through data visualization and cartographic techniques. Interdisciplinary expertise in history, geography, and digital methods Specializes in GIS, narrative cartography, and critical data analysis Current research centers on early colonial Latin America and place-name studies His recent publications in Hispanic American Historical Review (2020) and International Journal for Arts and Humanities Computing (2017) examine how digital tools can reconstruct Indigenous agency during the Spanish conquest. He teaches courses on digital history, spatial analysis, and Indigenous narratives at Dartmouth and previously at USC. Scientific recognition includes the Andrew W. Mellon Postdoctoral Fellowship in Digital Humanities.

Angela P. Murilloمشاهده پروفایل

دانشیار

• Data Curation
• Data Management
• Data Sharing and Reuse
• +۳ مورد دیگر

Angela P. Murillo serves as an Associate Professor at Indiana University's Luddy School of Informatics, Computing, and Engineering in Indianapolis, where she directs the undergraduate Data Science program. She concurrently holds roles as Co-Principal Investigator and Student Fellowship Director for CI Compass, a National Science Foundation-funded initiative (Grant #2127548) supporting cyberinfrastructure advancement. Her academic foundation includes an undergraduate degree in geoscience and a Ph.D. in Information Science from the University of North Carolina at Chapel Hill. Her doctoral research examined data sharing and reuse within scientific cyberinfrastructures, establishing her trajectory in data-intensive scholarship. Murillo's research program critically examines data curation, preservation, and cyberinfrastructure through lenses of workforce development and educational innovation. She develops competency frameworks for data professionals while investigating diversity in LIS education and virtual reality applications for learning. Her work bridges theoretical data management principles with practical implementations across scientific domains, emphasizing FAIR data practices and cross-cultural educational frameworks. Analysis of her recent publications reveals concentrated exploration of data curation competencies (2023-2025), workforce dynamics in data fields, and cyberinfrastructure fellowship impacts. These works demonstrate evolving focus from foundational data sharing studies toward systemic educational frameworks and workforce development strategies, with increasing attention to equity and community-driven standards in data science. As Co-PI for the NSF-supported CI Compass project, Murillo secures critical infrastructure funding while directing student fellowships that cultivate next-generation cyberinfrastructure professionals. Her leadership in developing Indiana University's Data Science undergraduate program since 2017 demonstrates sustained commitment to building academic pathways, though specific student advisee details remain unpublicized. Murillo collaborates within the CI Compass project team and leverages industry experience from her tenure as Research and Development Information Scientist at Novozymes. Her current initiatives focus on expanding data science education through community-curated competency frameworks and addressing workforce gaps via innovative fellowship models, positioning her at the intersection of data infrastructure and educational transformation.

Anna Monrealeمشاهده پروفایل

دانشیار

• Data Analytics
• Data Mining
• Artificial Intelligence
• +۵ مورد دیگر

Anna Monreale is an Associate Professor of Computer Science at the University of Pisa and serves as the Delegate for Masters and Continuing Education. She concurrently holds an Adjunct Professor position at Dalhousie University's Faculty of Computer Science. Her institutional leadership includes vice-coordinating the National Doctorate in Artificial Intelligence for Society and chairing the Internships Committee for the Master's in Data Science & Business Informatics. She earned her Bachelor's and Master's degrees with honors in Computer Science from the University of Pisa in 2007 and completed her PhD there in 2011. Prior to her faculty appointment, she conducted research on European projects from 2011-2014 and has been affiliated with the CNR's Institute of Information Science and Technologies since 2008. Professor Monreale's research centers on ethical AI development, with core expertise in data mining, explainable AI, and privacy-by-design methodologies. Her work bridges technical innovation with societal impact, particularly in healthcare applications, mobility data analysis, and social media systems. She emphasizes transparency and legal compliance in algorithmic decision-making. Her recent publications (2023-2025) reveal a concentrated focus on trustworthy AI frameworks, with significant contributions to federated learning privacy, trajectory data protection, and real-time explainability systems. She actively addresses emerging challenges in content moderation regulation and healthcare AI deployment. As an educator, she teaches Data Mining and Data Science Lab courses while directing major academic initiatives. She leads a PNRR-funded national project and a European research project, with over 90 publications and 3,200+ citations reflecting her scholarly impact. She is a key member of the University of Pisa's KDD Lab and maintains strong ties with CNR research units.

• Machine Learning
• Knowledge Discovery
• Data Mining
• +۶ مورد دیگر

Manuel Filipe Vieira Torres Santos is an Associate Professor with Habilitation at the School of Engineering of the University of Minho, Portugal. He serves as a Senior Researcher at Centro ALGORITMI (Associate Laboratory in Intelligent Systems) where he coordinates the Intelligent Data Systems research lab and the Information Systems and Technologies research group. His academic career combines teaching responsibilities with extensive research activities focused on data science applications in healthcare settings. Dr. Santos' research interests center on applying advanced computational techniques to healthcare challenges. His primary areas include Machine Learning, Knowledge Discovery from Databases, Data Mining, Agent-based Systems, and Intelligent Decision Support Systems. He has pioneered work in Pervasive and Adaptive Business Intelligence specifically tailored for healthcare environments. His research spans both theoretical advancements in data science methodologies and practical implementations in real-world medical settings, particularly in intensive care units, hospital management, and precision medicine applications. Analysis of his recent publications reveals a strong trend toward integrating emerging technologies like blockchain, Internet of Things, and openEHR standards into healthcare information systems. His work demonstrates a progression from foundational data mining techniques to increasingly sophisticated architectures that support real-time decision making, predictive analytics, and personalized patient care. The publications show particular emphasis on standardization efforts, interoperability challenges, and the development of maturity models for digital transformation in healthcare institutions. Coordinator of Intelligent Data Systems research lab Coordinator of Information Systems and Technologies research group Principal Investigator for multiple funded projects including: Intelligent hospitalization management (2021-present) Data Science applied to diabetes (2021-present) Intelligent Decision Support for response times optimization (2020-present) Intelligent Hospital Infection Control (2020-present) Dr. Santos has secured significant research funding for healthcare analytics projects, demonstrating the practical value of his work. His approach combines technical expertise in data science with deep understanding of healthcare workflows and challenges, resulting in solutions that address real clinical needs while advancing the state of the art in health informatics.

• Information Systems
• Business Intelligence
• Data Mining
• +۴ مورد دیگر

Maria Isabel Calapez Cabrita Leal Seruca serves as an Associate Professor at Portucalense University's Science and Technology Department in Porto, Portugal, where she teaches Information Systems and coordinates the Informatics Engineering and Information Systems for Management programs. She additionally holds the role of Department Coordinator for the Erasmus+ Programme. As a Senior Researcher at Universidade do Minho's Centro Algoritmi, she contributes to the IST R&D Group and ISTTOS R&D Lab, bridging academic and research domains across institutions. Her academic foundation includes a degree in Applied Mathematics and Computation from Portucalense University, complemented by an MSc and PhD in Computation from the University of Manchester Institute of Science and Technology (UMIST). This technical background underpins her research trajectory in digital systems and organizational transformation. Her research focuses on Information Systems with specialized expertise in Business Intelligence, Data and Web Mining, Digital Transformation, and Reuse and Patterns. She has pioneered models like E-swim (Enterprise Semantic Web Implementation) and ToOW (Training of Organizations in the Web) to address organizational challenges through technology adoption. Her work consistently integrates practical applications in educational settings and industry contexts, particularly through international Erasmus collaborations. Analysis of her 2019-2025 publications reveals a dominant emphasis on Digital Transformation in educational mobility systems (notably Erasmus+ projects), alongside applied Data Mining in domains including tourism, pharmaceutical distribution, and waste management. Her scholarship demonstrates a trajectory from foundational semantic web research toward contemporary organizational digitalization challenges, with increasing focus on sustainable development frameworks. While no major individual scientific awards are documented in the source material, her research output includes 41 publications with an h-index of 7 and 164 citations, reflecting steady scholarly impact in Information Systems journals and conferences. She actively leads Erasmus+ initiatives including the KA220 project 'Data Science We Teach Data' and has coordinated multiple Erasmus Intensive Programmes on Sustainable Digital Transformation (2022-2023), Business Intelligence (2008-2010), Web Mining (2011-2013), and Data Science (2014). Her grant work consistently targets curriculum development for digital competencies in higher education. Within research infrastructure, she contributes to the Digital Transformation and Innovation in Organisations (DTIO) Group at REMIT (Portucalense University) and the ISTTOS Lab at Centro Algoritmi (Universidade do Minho), focusing on organizational agility through technology adoption and pattern-based solutions.

• Sports Science
• Exercise Physiology
• Environmental Physiology
• +۲ مورد دیگر

Takashi Ichinose is a Professor at Waseda University's Center for Research Strategy and Faculty of Sport Sciences. He has held progressive roles at Waseda since 2004, including his current position in the Center for Research Strategy since 2008, where he manages research strategy while maintaining his academic focus on sports science. His educational background includes: Doctor of Philosophy in Medical Science from Shinshu University Graduate studies in Life Sciences from Waseda University's Graduate School of Human Sciences Undergraduate degree in Sport Sciences from Waseda University's School of Human Sciences Professor Ichinose's research spans exercise physiology, environmental physiology, and sports science, with particular focus on substrate metabolism during exercise, thermoregulation, and plasma volume changes. He has made significant contributions to understanding how nutritional interventions like tea catechins affect fat metabolism. He is also a leading figure in Tee-ball development, a modified baseball sport officially adopted into Japan's elementary school curriculum. His publication record shows an evolution from fundamental exercise physiology research to broader applications in sports education and inclusion. Early work focused on metabolic responses to exercise, while more recent publications address sports education, disability inclusion, and international sports initiatives like the Olympics. Professor Ichinose has received research funding from the Japan Society for the Promotion of Science and collaborated with industry partners including Kao Corporation. His presentations at numerous research meetings demonstrate his active role in promoting research funding opportunities at Waseda University. He has been deeply involved with the Japanese Tee-ball Association, contributing to Tee-ball's adoption in Chinese and Korean educational systems. His work bridges academic research with practical applications in sports education and physical activity promotion across Asia.