جست‌وجوی استاد و پژوهشگر

• Multivariate Statistics
• Data Analysis
• Clustering Algorithms
• +۴ مورد دیگر

Laura Bocci has been an Associate Professor of Statistics at Sapienza University of Rome since 2022. Previously, she worked as a researcher at Sapienza University from 1998–2000 in the Department of Communication and Social Research, later moving to the Department of Social and Economic Sciences in 2020. She earned her PhD in Methodological Statistics (XII Cycle, 2000) from Sapienza University, where she also graduated with honors in Statistical and Economic Sciences. Current Department: Department of Social and Economic Sciences Faculty: Faculty of Political Science, Sociology, Communication Office Location: Room 107, Building CU002 (Political Sciences), Sapienza University Campus Teaching: Statistics (A-K) and Data Science for Social Research Her research focuses on Multivariate Statistics , Data Analysis , and Statistical Methods with applications in social sciences, economics, and data clustering. She has developed novel techniques in dimensional reduction , model-based clustering , unsupervised classification , multidimensional scaling , and genetic algorithms . Her work often addresses complex data structures , including three-way data and multilevel models. Publications highlight trends in regional competitiveness , well-being analysis , clustering methodologies , and genetic algorithm applications . Key subfields include three-way data analysis , spatial clustering , and categorical variable optimization . Office hours are held Tuesdays 14:00–16:00 in person or via Google Meet (by appointment). She teaches courses in Statistics and Data Science for Social Research using Sapienza’s e-learning platform.

Justin Kernمشاهده پروفایل

دانشیار

• Educational Measurement
• Psychological Measurement
• Item Response Theory (IRT)
• +۶ مورد دیگر

Justin Kern is an Associate Professor in the Department of Educational Psychology at the University of Illinois, Urbana-Champaign . He earned his Ph.D. in Quantitative Psychology (2017) and holds additional graduate degrees in Psychology and Statistics. His research focuses on educational and psychological measurement with emphasis on item response theory (IRT) , Bayesian statistical methods , and multivariate data analysis . Research Themes Item Response Theory (IRT) and extensions Computerized Adaptive Testing (CAT) Four-parameter IRT models Response time modeling Measurement invariance Bayesian estimation techniques Recent Publications His recent work (2025-2020) spans topics in educational measurement , psychological testing , and family behavior analysis . Key trends include: Development of IRT-based frameworks for health survey data Investigations into extreme response styles using IRTree models Advancements in four-parameter IRT modeling Applications of Bayesian methods in test construction Analysis of father involvement in early intervention Collaborations Kern collaborates across disciplines with researchers in psychology , statistics , healthcare informatics , and special education . His work has been supported by grants from the Institute for Education Sciences and NICHD .

Flavia Fossatiمشاهده پروفایل

استادیار

• Labor Market Integration
• Migration Studies
• Social Policy
• +۲ مورد دیگر

Flavia Fossati is an Assistant Professor at the University of Lausanne 's LIVES Centre, specializing in labor market integration, migration studies, social policy, and employment discrimination. Her research examines structural barriers faced by immigrants in Western economies and the role of cognitive biases in hiring practices. Key Research Areas: Refugee labor market access, gender disparities in employment, policy effectiveness, experimental studies on discrimination. Collaborations: Co-author on projects with Patrick Emmenegger, Giuliano Bonoli, Carlo Knotz, and others. Publications: Focus on Swiss political institutions, welfare deservingness, and comparative labor market dynamics across Europe.

• Statistical Models for Longitudinal Data
• Latent Variable Approaches
• Wage Inequality Trends
• +۹ مورد دیگر

Scott Marc serves as Professor of Applied Statistics and Co-Department Chair at New York University's Steinhardt School of Culture, Education, and Human Development. He is also Co-Director of PRIISM (Center for Practice and Research at the Intersection of Information, Society, and Methodology), a research center focused on methodological innovation in social science research. Previously, he co-directed NYU Steinhardt's Masters of Science in Applied Statistics for Social Research (MS-A3SR) program. Dr. Scott's research centers on the development of sophisticated statistical models for longitudinal data analysis. He employs latent variable approaches to create new model classes that examine complex phenomena including wage inequality trends, medical histories, and psychological profiles. His work on longitudinal sequence analysis has significant applications in educational settings, where he examines how entire educational and employment pathways influence outcomes like wages or degree completion. In labor market research, he identifies structural similarities in career histories to map successful career ladders. His publication record demonstrates consistent methodological innovation across diverse application areas. The most recent articles show his growing focus on multi-channel sequence analysis, sensitivity analysis for multilevel models, and Bayesian computational methods. His work bridges sophisticated statistical theory with practical applications in social science, economics, and health research, making methodological advances accessible to substantive researchers. Dr. Scott teaches advanced courses including Multi-Level Models: Growth Curves / Nested Data (APSTA-GE.2040/41/42), Practicum in Statistical Computing (APSTA-GE.2352), Supervised and Unsupervised Machine Learning (APSTA-GE.2011), and Spatial Statistics (APSTA-GE.2015). He has played a significant leadership role in developing NYU's statistical research infrastructure, having co-founded PRIISM in 2008 and helped launch the MS-A3SR program in 2014. At PRIISM, Dr. Scott contributes to a vibrant research community that brings together statisticians, social scientists, and domain experts to tackle complex research questions requiring innovative methodological approaches. His work exemplifies the center's mission to develop and apply cutting-edge statistical methods to pressing social issues.

• Computer Measurement and Control Systems
• Automation of Experimental Research
• Artificial Intelligence
• +۷ مورد دیگر

Balovsyak Serhiy Vasyliovych is an Associate Professor at the Department of Computer Systems and Networks, Faculty of Physics, Yuriy Fedkovych Chernivtsi National University. He holds a Doctor of Technical Sciences degree with expertise spanning computer measurement systems, digital image processing, and X-ray diagnostics. His academic journey began with a Candidate's thesis in solid-state physics (2003) and culminated in a Doctoral dissertation on multilevel methods for processing electron diffraction and X-ray signals (2019). His educational background includes graduation from Chernivtsi State University, Faculty of Physics, Department of Radio Engineering (1995). His professional career evolved from Engineer in Solid State Physics (1995-2003) to Assistant Professor (2004-2006), culminating in his current position as Associate Professor since 2006. Dr. Balovsyak's research interests encompass development of computer measurement and control systems, automation of experimental research, artificial intelligence, pattern recognition, digital image processing, artificial neural networks, and genetic algorithms. His work bridges physics and computer science, particularly in applying computational methods to analyze X-ray and electron diffraction data. His publication record demonstrates a clear progression from fundamental physics research toward increasingly sophisticated computational methods for image processing and analysis. Recent work focuses on Gaussian noise estimation and removal, adaptive image filtering, and computer vision applications, while maintaining connections to his foundational work in materials characterization through X-ray techniques. Certificate of Honor from the Chernivtsi Regional State Administration to the 140th anniversary of the Chernivtsi National University Dr. Balovsyak has developed numerous educational materials including lecture notes and laboratory manuals on Computer Systems of Artificial Intelligence, Digital Image Processing Methods, and other computer science topics. His work demonstrates strong integration between theoretical knowledge and practical implementation, with multiple software systems and patents developed for image processing and materials analysis applications.

• Smart City
• Internet of Things (IoT)
• Semantic Modeling
• +۴ مورد دیگر

Adrian Sorin Barb is an Associate Professor affiliated with the Engineering Division (Great Valley). Their research focuses on Smart City , Internet of Things , and Semantic Modeling , often integrating formal ontologies and machine learning techniques. Key contributions include: NSF-funded project (2022-2025) on Open Educational Resources for Architectural Engineering as CoPI. Collaborations on policy evaluation frameworks using ontologies and IoT analytics. Their work aligns with UN Sustainable Development Goals (SDGs), particularly in urban sustainability. Research outputs emphasize visual pattern recognition , semantic fusion , and curricular coherence .

Yan Wangمشاهده پروفایل

دانشیار

• Quantitative Psychology
• Psychometrics
• Finite Mixture Modeling
• +۳ مورد دیگر

Yan Wang is an Associate Professor in the Department of Psychology at the College of Fine Arts, Humanities and Social Sciences, University of Massachusetts Lowell, specializing in advanced quantitative methods for psychological research. Her expertise bridges statistical methodology and applied behavioral sciences through innovative modeling techniques. Her educational trajectory demonstrates interdisciplinary rigor: Ph.D. in Measurement & Research (2018), University of South Florida, with supporting area in Educational Psychology; dissertation: "Covariates in factor mixture modeling: Investigating measurement invariance across unobserved groups" M.A. in Curriculum and Instruction (2013), Boston College Dual B.A. in English Literature and B.S. in Economics (2011), Xiamen University, China Dr. Wang's research centers on quantitative psychology with pioneering work in finite mixture modeling, structural equation modeling, multilevel modeling, and Bayesian analysis . Her methodological innovations address critical challenges in measurement invariance testing, latent variable modeling, and longitudinal data analysis, significantly advancing psychometric theory while enabling robust applications across educational, clinical, and social domains. This dual focus on methodological rigor and practical implementation defines her scholarly impact. Analysis of her 15 most recent publications reveals a distinctive pattern: she consistently develops and validates statistical techniques through Monte Carlo simulations before applying them to substantive problems in cognitive aging, traumatic brain injury outcomes, pediatric injury prevention, and cross-cultural psychopathology. Her signature approach combines simulation-based method evaluation with real-world applications, particularly in educational measurement and developmental psychology contexts. Her scholarly excellence has been recognized through prestigious awards: Teaching Excellence Award (2022) from University of Massachusetts Lowell Leslie C. Robbins Dean’s Excellence Award (2016) from University of South Florida Conference Presentation Travel Grant (2015) from SouthEast SAS Users Group University Graduate Fellowship Award (2013) from University of South Florida Dean’s Scholarship (2011) from Boston College National Scholarship (2008) from Xiamen University As Co-Investigator on the NIH-funded "Virtual Reality-based Rehabilitation for Children with Traumatic Brain Injuries" grant (Eunice Kennedy Shriver NICHD, 2020), she provides critical statistical leadership for clinical translation. Her collaborative publications with graduate students indicate active mentorship in quantitative methods, though formal advisee lists aren't documented. Dr. Wang maintains strong professional engagement through presentations at major conferences including AERA, APA, and Florida Educational Research Association, establishing herself as a leading voice in quantitative methodology development.

Claire S. Leeمشاهده پروفایل

دانشیار

• Cybercrime
• Cybersecurity
• Cyberterrorism
• +۷ مورد دیگر

Claire S. Lee, Ph.D. is an Associate Professor in the School of Criminology and Justice Studies at the University of Massachusetts Lowell, where she serves as Co-Director of the Center for Asian American Studies. She holds multiple affiliations including membership in the Center for Internet Security and Forensics Education and Research (iSAFER), the Center for Terrorism and Security Studies (CTSS), and as a Fellow of the Center for Public Opinion. Dr. Lee also serves on the Executive Board of the Division of Cybercrime within the American Society of Criminology and holds editorial positions at PLOS One and several other academic journals. Dr. Lee's research spans two primary domains: cyber and digital studies (including cybercrime, cybersecurity, cyberterrorism, and digital sociology), and global media and immigration studies. Her work examines mechanisms and networks of deviant behaviors facilitated by cyber-resources using quantitative, qualitative, computational, and mixed methodologies. She has received significant funding from federal agencies including the Department of State, National Security Agency, National Science Foundation, and Korea Foundation for research on cyberattacks, human trafficking, and cybersecurity education. Her publication record reveals a strong focus on cybercrime victimization, hate crimes, and the intersection of technology and social behavior. Recent work analyzes employment scams using machine learning (2025), hate crimes against racial minorities (2024), and cyberterrorism networks. Her research demonstrates increasing sophistication in methodology, incorporating natural language processing, latent class analysis, and social media analytics to understand complex cyber phenomena. Publications - Most by College (March 2022; November 2022; November 2024) U.S.-Korea NextGen Scholars Program, CSIS Korea Chair and USC Korean Studies Institute (2022-2023) Outstanding Teaching Award (2022) Young Faculty Award, Korean Society of Criminologists in America (KOSCA) (2021) Dr. Lee actively supervises research and has secured multiple federal grants including Department of State funding (2023-2025) and Korea Foundation support (2023-2024). She has co-led NSA/NSF-funded GenCyber Summer Camps at UMass Lowell (2020-2022) and serves as Vice Chair of the Division of Cybercrime at the American Society of Criminology. Her interdisciplinary approach bridges criminology, sociology, and computer science to address emerging cyber threats while emphasizing the importance of gender diversity in cybersecurity fields. Dr. Lee leads the Cybercrime Investigation & Cybersecurity Lab (CIC) initiatives and collaborates extensively across disciplines to study the social implications of technology. Her work connects online and offline behaviors, with particular attention to vulnerable populations and the ethical dimensions of digital surveillance and datafication.

Luke Sloanمشاهده پروفایل

استاد

• Social Media Research
• Quantitative Methods
• Data Linkage
• +۳ مورد دیگر

Professor Luke Sloan, based at Cardiff University's School of Social Sciences , serves as the Academic Partner for Student Voice and Engagement and Deputy Director of the Social Data Science Lab . With a career spanning 2010–present at Cardiff (Lecturer to Professor), he leads institutional programs like Module Enhancement and CardiffPulse while chairing the Student Voice and Partnership Committee . His work focuses on social media analytics , quantitative methods , and data linkage between surveys and Twitter. Education: PhD (Political Science, 2010) MSc (Social Research, 2007) BSc (Politics, 2006) Research Highlights: Internationally recognized for developing demographic proxies for Twitter , Sloan's research addresses representation in social media, data linkage methodologies , and ethics in big data social science . His ESRC-funded project (£906k) investigates survey-Twitter linkage, while prior work includes crime-sensing algorithms and election prediction models using social media. Scientific Contributions: Dillwyn Medal (Learned Society of Wales, 2019) Most Effective Teacher Award (Cardiff, 2013) Co-author of Bryman's Social Research Methods (6th ed, 2021) Teaching Innovation: As Co-Director of the Q-Step Centre , he developed the BSc Social Analytics program (UCAS code J3G5) in collaboration with the Office for National Statistics. His roles include Director of Learning and Teaching (2020–2022) during pandemic educational continuity. Labs & Collaborations: Leads the Social Data Science Lab and sits on the Social Media Analytics Review and Information Group (SMARIG) . Collaborates with the Office for National Statistics and Food Standards Agency.

• Biostatistics
• Epidemiology
• Machine Learning
• +۲ مورد دیگر

Yue Zhang, PhD, is a faculty member at the University of Southern California's Keck School of Medicine in the Department of Internal Medicine. With expertise in statistical methods applied to epidemiology and clinical trials, Dr. Zhang's research focuses on clustered and longitudinal data analysis, Bayesian inference, survival analysis, and stochastic processes. Dr. Zhang's recent publications highlight trends in cardiovascular device longevity, social determinants in infectious disease modeling, and environmental health impacts on pediatric and adult populations. Research spans biostatistical modeling, healthcare cost analysis, and predictive frameworks for chronic disease management. Key research collaborations include studies on antimicrobial resistance, diabetes screening interventions, and environmental toxin effects on patient outcomes. Dr. Zhang contributes methodological innovations in quantile regression and multilevel modeling across multiple medical disciplines.

• Operations Research
• Data Analytics
• Political Redistricting
• +۳ مورد دیگر

Douglas M King is a Teaching Assistant Professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign's Grainger College of Engineering. He has been with the department since 2012, progressing from Visiting Lecturer to his current position as Teaching Assistant Professor. His work focuses on applying operations research and data analytics to practical problems including political redistricting, transportation systems, and public health applications. Dr. King earned his educational degrees entirely from the University of Illinois at Urbana-Champaign: Ph.D. in Industrial Engineering (May 2012) M.S. in Systems and Entrepreneurial Engineering (May 2006) B.S. in General Engineering (May 2002) Dr. King's primary research interests lie at the intersection of operations research, data analytics, and real-world applications. His most significant contributions have been in developing optimization frameworks for political redistricting, where he has pioneered algorithms that create geographically compact and politically competitive districts. His work integrates geographic information systems with mathematical optimization to address the complex constraints of redistricting. Beyond redistricting, Dr. King has made notable contributions to understanding the relationship between transportation systems and public health outcomes, particularly examining how public transit usage relates to obesity rates. His methodological expertise spans stochastic modeling, graph partitioning, and multi-objective optimization. Dr. King's publication record demonstrates a clear progression toward increasingly complex optimization problems in political redistricting. Starting with foundational work on geo-graph modeling for congressional district creation, he has advanced to developing sophisticated multi-stage optimization frameworks that address multiple constitutional criteria simultaneously. His recent work employs local search algorithms with recombination methods that significantly outperform traditional approaches. Beyond redistricting, his publications show interdisciplinary reach into sports analytics (NCAA tournament modeling) and public health research examining connections between transportation patterns and obesity. Dr. King's research has been recognized with several prestigious awards: 2019 INFORMS Service Science Best Paper Award (First Place) 2018 INFORMS Public Sector Operations Research Best Paper Award (Finalist) 2013 IISE Pritsker Doctoral Dissertation Award (Second Place) 2012 INFORMS Innovation in Analytics Award (Semifinalist) 2012 INFORMS Section on Public Programs, Services and Needs Best Paper Competition (Finalist) Dr. King serves as a faculty advisor for student organizations, having received the IISE Outstanding Faculty Advisor award for the North Central Region in 2013 and 2016. He was also honored with the Engineering Council Outstanding Advising Award in 2016 and 2017. His teaching portfolio includes core industrial engineering courses such as Analysis of Data (IE 300), Industrial Quality Control (IE 330), Design & Analysis of Experiments (IE 400), and OR Models for Manufacturing Systems (IE 412). He has developed innovative teaching methods, including using case studies to enhance critical thinking skills of industrial engineering students. Dr. King collaborates extensively with Professor Sheldon H. Jacobson and other researchers including Kiera W. Dobbs, Ian G. Ludden, and Rahul Swamy. Together, they have developed the "Geo-Graph" framework and other computational approaches for redistricting that have been published in top journals. Their work has been covered in university news for developing new algorithms for congressional redistricting. They maintain an active research program that bridges theoretical optimization with practical applications in political science, transportation, and public health.

Mårten Schultzbergمشاهده پروفایل

پژوهشگر

• Statistics
• Causal Inference
• Experimental Design
• +۳ مورد دیگر

Mårten Schultzberg is a Researcher at the Department of Statistics at Uppsala University , Sweden. His work spans causal inference, experimental design, and applied statistics, with a focus on methodological advancements and interdisciplinary applications in energy economics and behavioral science. Research Interests : Causal inference in observational and experimental studies Optimal experimental design and rerandomization strategies Statistical methods for energy demand analysis and behavioral modeling Recent Publications highlight his contributions to balancing covariates in randomized experiments, causal mediation analysis, and energy policy evaluation. His work appears in journals such as Journal of Causal Inference , Statistical Papers , and Energy Research & Social Science . Contact: marten.schultzberg@statistik.uu.se

• Urban Sociology
• Social Area Analysis
• Food Desert Studies
• +۳ مورد دیگر

Tatsuto Asakawa is a Professor at Waseda University's Faculty of Human Sciences , specializing in Urban Sociology with a Ph.D. from Tokyo Metropolitan University (2021). His career spans institutions like Meiji Gakuin University (1996-2020), where he held roles from Associate Professor to Professor. Education: Ph.D. in Sociology (Tokyo Metropolitan University, 2021) Key Projects: Food Desert Analysis, Post-Disaster Urban Reconstruction, Socio-Spatial Inequality Studies Asakawa's research fuses spatial analysis with social theory , focusing on: Food Deserts : Quantifying urban food insecurity using GIS and multilevel modeling Disaster Recovery : Examining social capital and housing dynamics post-Great East Japan Earthquake Urban Polarization : Tracking spatial reorganization of social classes in Tokyo and other metropolises His 8 Scopus-indexed publications (h-index:4) include studies on: Elderly nutrition accessibility (2024) Children's health hazards in multicultural environments (2023) Commercial restructuring after disasters (2021) Awarded by Japan's Minister of Education (2025) and twice by the Japanese Association of Urban Sociology (2021, 2005), he leads projects like: 2024-2029 : Foreign-rooted children's health environments 2024-2028 : Disparity expansion in aging populations 2019-2024 : Regional optimal solutions for disaster recovery As Director of Waseda's Institute for Sustainable Communities, he integrates quantitative GIS with qualitative community studies , emphasizing policy applications for carbon-neutral urban planning and socially vulnerable groups .

• Bayesian Statistics
• Spatial Statistics
• Disease Mapping
• +۳ مورد دیگر

Andrea Ingeborg Riebler is a Professor in the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU). Her research focuses on Bayesian statistics, spatial modeling, disease mapping, and statistical software development. She is a leading contributor to the R-INLA framework for Bayesian hierarchical modeling and has developed packages like SUMMER and makemyprior for spatial-temporal analysis and prior elicitation. Her work bridges methodological advancements with applications in public health, demography, and epidemiology. Key research interests include spatial epidemiology, meta-analysis, and robust statistical modeling. She collaborates widely, particularly with researchers in geostatistics, computational statistics, and health sciences. Notable contributions include methods for small-area estimation in low/middle-income countries, cancer burden projections, and addressing positional uncertainty in geostatistical surveys. Her recent publications (2023–2025) emphasize scalable disease forecasting, mortality-based cancer incidence prediction, and improving DHS data analysis through positional adjustment. Teaching includes advanced computational statistics, survival analysis, and statistical modeling for biologists. Education: Doctoral dissertation work at NTNU (2021) focused on robust hierarchical models. Grants/Projects: Active in international health surveys (e.g., European Social Survey Health Inequalities Module). Labs/Teams: Collaborates with the NTNU Statistics Group and global health data initiatives.

Muhammad Akramمشاهده پروفایل

پژوهشگر ارشد

• Biostatistics
• Applied Statistical Modelling
• Time-Series Analysis
• +۷ مورد دیگر

Muhammad Akram is a Research Fellow (Biostatistician) at the Mary MacKillop Institute for Health Research (MMIHR), Australian Catholic University, Melbourne. He is affiliated with the Behaviour, Environment and Cognition Research Program, contributing to major international and national health research initiatives. His work bridges statistical methodology with public health applications, particularly in physical activity, built environment, and injury prevention. Research Interests: His expertise lies in applied statistical modelling , with a focus on time-series analysis and forecasting , predictive modelling , and multilevel modelling in health and environmental sciences. He has developed methods for short interrupted time series, cluster randomized crossover trials, and case-crossover designs, which are widely used in epidemiological and behavioral research. The recent publications reflect a strong trend in environmental and behavioral epidemiology , particularly in understanding how built environments influence physical activity across different populations and countries. His work often involves large-scale, multi-country datasets and advanced statistical techniques to model complex health behaviors and outcomes. Scientific Awards: Travel grant from the Faculty of Medicine, Nursing and Health Sciences, Monash University (ASC-2012) Advising and Grants: Muhammad Akram is provisionally accredited as an HDR Supervisor. He has contributed to several major funded projects, including the International Physical Activity and the Environment Network (IPEN) Adolescent Study (funded by NIH, US$2.8M), active travel research in Hong Kong (GRF, HK$744K), preschool physical activity (HK$118K), and the KIAMA PROJECT on underage drinking (ARC Future Fellowship, $931K). He collaborates with leading investigators such as Prof. Ester Cerin, Prof. Jim Sallis, and Prof. Sandra Jones. Labs and Teams: He is an integral member of the Behaviour, Environment and Cognition Research Program at MMIHR and maintains an ongoing adjunct affiliation with the Department of Epidemiology and Preventive Medicine at Monash University since 2015, where he previously worked from 2009 to 2014.