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Brandon VaughnView profile

Associate Professor

• Statistics Education
• Educational Psychology
• Measurement and Assessment
• +4 more

Dr. Brandon Vaughn is an academic researcher specializing in statistics education, educational psychology, and measurement theory. His scholarly work spans over two decades, with recent publications focusing on innovative approaches to data analytics education and statistical pedagogy. As evidenced by dissertation acknowledgments, he serves on doctoral committees, guiding graduate students through their research process. Dr. Vaughn's research interests center on improving statistical education through balanced amalgamation of teaching strategies, incorporating active learning with traditional methods. His work explores multilevel modeling applications in educational assessment, differential item functioning analysis, and the use of technology for enhancing statistical visualization. He has made significant contributions to understanding how contextual factors influence measurement quality and how collaborative learning can be optimized through heedful interrelating. His publication record shows consistent productivity across multiple domains, with recent work examining narrative approaches to data analytics education (2022) and foundational contributions to psychometric modeling. The trajectory of his research demonstrates evolving expertise from traditional statistical classification methods to contemporary applications in educational technology and data science pedagogy. Among his notable contributions are investigations into: The role of dialect in intelligence perception Contextual effects on assessment quality Mobile applications for statistical visualization Integration of social contexts in classification analysis Dr. Vaughn has advised graduate students including Sarah R. Daniel, whose dissertation acknowledged his "full support and unfailing enthusiasm" throughout her research process. His collaborative work spans multiple disciplines, connecting statistical methodology with practical educational applications.

David J. RoseboroughView profile

Associate Professor

• Cognitive Therapy
• Effectiveness Research
• Psychotherapy Outcomes
• +5 more

David J. Roseborough serves as an Associate Professor in the School of Social Work at the University of St. Thomas. His academic work bridges clinical practice and research, focusing on how psychotherapy interventions function in real-world settings rather than controlled laboratory environments. With nearly two decades of clinical experience, he brings substantial practical knowledge to his academic position. His educational background includes a PhD from the University of Minnesota, an MSW from College of St. Catherine & University of St. Thomas, and a BA from Valparaiso University. This comprehensive training foundation supports his dual expertise in clinical practice and academic research. Dr. Roseborough specializes in effectiveness research, employing sophisticated statistical methods including multilevel modeling and survival analysis to determine what therapeutic approaches work best for specific populations. His research particularly examines cognitive therapy applications, longitudinal treatment outcomes, and disorder-specific intervention strategies. He has developed expertise in studying how people sustain major life changes following psychotherapy, with special attention to older adult populations. His publication record demonstrates a consistent focus on psychotherapy effectiveness across multiple settings and populations, with particular emphasis on treatment retention, outcome measurement, and the practical application of therapeutic techniques in community settings. The research shows an evolution from broader psychotherapy studies to more specialized investigations of cognitive and psychodynamic approaches with specific demographic groups. Certified Cognitive Therapist (CCT) Diplomat in the Academy of Cognitive Therapy As an educator, Dr. Roseborough teaches courses including Cognitive Interventions in Clinical Practice and a doctoral-level writing course focused on publication preparation. His teaching philosophy emphasizes relational engagement, intellectual curiosity, and valuing the experience that adult learners bring to the classroom. With nineteen years of outpatient psychotherapy experience, he maintains strong connections between his clinical practice and academic work, ensuring his teaching remains grounded in real-world applications.

• Social determinants of health
• health inequities
• built environment
• +7 more

Leland K. Ackerson is an Associate Professor and Master of Public Health Coordinator in the Department of Public Health at the Zuckerberg College of Health Sciences, University of Massachusetts Lowell, where he investigates health disparities through community-engaged research and advanced epidemiological methods. His academic foundation includes a Sc.D. in Social Epidemiology (2007) from Harvard School of Public Health, an MPH in Community Health Education (2000) from UMass Amherst, and a BS in Health Science and Humanities (1992) from the University of New England. Dr. Ackerson's research examines how social structures, built environments, and communication systems shape health outcomes, with dual expertise in multilevel modeling and community health assessment. His work particularly addresses psychosocial stress pathways, public opinion dynamics around health policies, and health-in-all-policies frameworks across diverse populations including significant studies in India. His publication portfolio reveals evolving methodological sophistication from traditional epidemiology to machine learning applications, consistently focusing on health equity across U.S. and global contexts. Recent work explores statistical anomalies in medical research while maintaining longstanding commitments to HIV/AIDS communication inequalities and intimate partner violence impacts. Recognition for his academic contributions includes: Excellence in Teaching Award (2011) Faculty Engagement and Service Learning Award (2013) Teacher of the Year Award (2019) Research funding spans community health assessments and innovative analytics, highlighted by the 2018 IDEA Leadership Fund project on vulnerable population health outcomes and 2011 collaborations on social networking interventions and Greater Lowell health needs assessments. Early career development was supported by Harvard School of Public Health and Dana-Farber Cancer Institute fellowships. While institutional lab affiliations aren't specified, his work demonstrates extensive interdisciplinary collaboration through community health partnerships and cross-institutional research teams.

Yassir AbdelRazigView profile

Professor

• Construction Engineering & Management
• Resilient Infrastructure
• Sustainable & Green Buildings
• +2 more

Yassir AbdelRazig serves as a Professor in the Department of Civil & Environmental Engineering at Florida State University's College of Engineering. His work bridges construction management, infrastructure resilience, and sustainable systems design, with significant contributions to transportation engineering and smart infrastructure technologies. His academic foundation includes: Ph.D. in Civil Engineering from Purdue University (1999) M.S. in Civil Engineering from Bradley University (1995) B.S. in Architectural Engineering from the University of Khartoum (1992) Professor AbdelRazig's research centers on Construction Engineering & Management, Resilient Infrastructure systems, Sustainable & Green Buildings, and Smart Engineering Systems. He pioneers quantitative frameworks for infrastructure resilience—particularly in transportation networks—using advanced simulation, building information modeling (BIM), and data analytics. His work addresses critical gaps in vulnerable population mobility, hurricane-resistant design, and sustainable construction practices, emphasizing real-world implementation through computer vision and AI-driven optimization. Analysis of his 2022-2024 publications reveals dominant trends in transportation resilience metrics, with 60% focusing on vulnerable communities and infrastructure robustness. Key methodologies include metaheuristic optimization, GIS-based spatial analysis, and multi-objective decision frameworks applied to sustainable site selection and disaster mitigation. His work consistently integrates civil engineering fundamentals with computational innovation across transportation, structural, and environmental domains. While no specific awards are documented in the source material, his prolific publication record in high-impact journals demonstrates recognition within the civil engineering community. Details regarding student advising, grant funding, and laboratory facilities are not provided in the available text. Similarly, information about research teams or collaborative initiatives remains unspecified, though his interdisciplinary publication themes suggest cross-departmental engagement.

Debarchana GhoshView profile

Associate Professor

• Spatial Epidemiology
• Environmental Health
• Public Health
• +4 more

Debarchana Ghosh serves as an Associate Professor in the Department of Geography at the University of Connecticut. Her interdisciplinary research investigates health issues through spatiotemporal modeling, aiming to provide evidence for public health interventions and preventive policies. She employs Geographic Information Systems (GIS), spatial statistics, machine learning, and social network analysis in her work. Her academic credentials include: Ph.D. in Geography, University of Minnesota (2005-2009) M.Phil in Population Studies, Jawaharlal Nehru University (2002-2004) M.A. in Geography, Jawaharlal Nehru University (2000-2002) B.Sc. in Geography, Presidency College, University of Calcutta (1997-2000) Dr. Ghosh's research centers on four main areas: (1) spatiotemporal modeling of infectious diseases and toxic releases linked to unsustainable urban and environmental resource use; (2) public health impacts of food deserts; (3) access to HIV/AIDS intervention programs; and (4) environmental justice. She develops innovative analytical frameworks that integrate GIS, spatial statistics, machine learning, and qualitative techniques to address complex health challenges in urban and environmental contexts. Her recent publications (2022-2025) reveal a consistent focus on health disparities, particularly among vulnerable populations such as transgender individuals and African Americans. Key themes include neighborhood disadvantage and cancer control behaviors, structural racism's impact on substance use, and ethical spatial mapping for public health. Her work increasingly incorporates intersectionality and longitudinal analysis to understand the social determinants of health. As a faculty member, Dr. Ghosh mentors graduate students in the Department of Geography, with research aligned to her expertise in spatial epidemiology and environmental health. While specific grant details are not provided, her research agenda suggests active pursuit of funding in public health and geographic information science.

Kyung-Seok MinView profile

Professor

• Educational Measurement
• Psychometrics
• Multidimensional Item Response Theory (MIRT)
• +3 more

Kyung-Seok Min is a Professor at the Department of Education, Sejong University. His career spans academic and research roles in educational measurement and data analysis. Education Ph.D., Michigan State University (2003) M.S., Yonsei University (1997) B.S., Yonsei University (1995) His research focuses on psychometric theory and multilevel statistical modeling, including: Multidimensional Item Response Theory Test Equating Structural Equation Modeling Bayesian Methods Propensity Score Matching Publications reflect expertise in causal inference, educational reform, and robust statistical techniques. Key contributions include national policy studies for Korea's College Entrance Examination.

Amr DodinView profile

Assistant Professor

• Chemical Reactions at Liquid Interfaces
• Biological Control of Photosynthesis
• Noisy Quantum Devices
• +9 more

Amr Dodin is an Assistant Professor in the Department of Chemistry at Ohio State University, where he leads research at the intersection of theoretical chemistry, quantum physics, and computational modeling. His work combines statistical mechanics, machine learning, and quantum dynamics to address complex problems in chemical reactivity, biological energy regulation, and quantum device design. He received his BSc and MSc in Chemical Physics and Theoretical Chemistry from the University of Toronto, followed by a PhD in Theoretical Chemistry at MIT under Adam Willard. He later pursued postdoctoral research at UC Berkeley and Lawrence Berkeley National Lab with Phillip Geissler and David Limmer. Research Focus 1: Chemical Reactions at Liquid Interfaces – developing computational tools like neural network potentials to study accelerated reactivity at electrochemical and atmospheric interfaces. Research Focus 2: Biological Control of Photosynthesis – modeling multi-scale dynamics of nonphotochemical quenching and biophysics of homeostasis. Research Focus 3: Noisy Quantum Devices – leveraging noise-induced coherences for robust quantum computing and deriving thermodynamic limits for quantum information systems. The 15 most recent publications highlight his expertise in interfacial chemistry, nonequilibrium quantum dynamics, and machine learning applications in molecular simulations. His work spans diverse areas including atmospheric aerosol reactions, photosynthetic energy regulation, and quantum thermodynamics. The Dodin group actively develops thermodynamically consistent multiscale models and machine learning-powered simulations to tackle challenges in nanoscale energy transport, quantum device stability, and environmental chemistry.

Michael BraunView profile

Assistant Professor

• Organizational Psychology
• Leadership Studies
• Team Dynamics
• +4 more

Michael Braun is an Assistant Professor in the Department of Management & Entrepreneurship at DePaul University's Driehaus College of Business. His research focuses on organizational behavior, computational modeling, and multilevel dynamics. Prior to joining DePaul in 2019, he served as an instructor at the University of South Florida. Education PhD, Organizational Psychology, Michigan State University (2012) MA, Organizational Psychology, Michigan State University (2009) BA, Psychology, Purdue University (2006) Research Areas Braun's work spans emergent leadership, team cohesion, and knowledge emergence in dynamic organizational contexts. He employs computational modeling, longitudinal data analysis, and sociometric methods to study multilevel dynamics in teams and decision-making processes. His publications address methodological challenges in organizational research, including spurious relationships in longitudinal data, dominance analysis accuracy, and construct validity in big data studies. Current projects integrate computational models with social theory and examine temporal patterns in team cognition and performance. Scientific Awards 2013 Organizational Research Method Best Paper Award 2015 Owens Scholarly Achievement Award 2017 Journal of Business and Psychology Reviewer of the Year Award Braun also works with Starling Trust Sciences since 2016, focusing on sociometric data analysis and psychometric assessments. His methodological expertise is reflected in contributions to journals like Journal of Applied Psychology , Organizational Research Methods , and Journal of Vocational Behavior .

• Leadership
• Organizational Behavior
• Virtual Work Environments
• +3 more

Dr. Fuhe Jin is an Assistant Professor of Management at the School of Business , The College of New Jersey (TCNJ) , where she joined in July 2023 after earning her Ph.D. in Leadership & Organizational Science from Binghamton University (SUNY), 2023 . Her expertise spans leadership, team dynamics, and virtual work environments with a focus on multilevel intra-/inter-personal dynamics in digital teams. Education: Ph.D. in Leadership & Organizational Science, Binghamton University, 2023 M.B.A. in Strategic Management, Shandong University, 2013 B.Sc. in Information and Computational Science, Ocean University of China, 2008 Research Interests include: Green Human Resource Management and its impact on employee environmental behavior Machine learning applications in analyzing leadership emergence and communication patterns Temporal dynamics in virtual team efficacy and conflict resolution Cross-level moderating effects of cultural values and emotional regulation Network-based leadership theories and multilevel organizational frameworks Publications span high-impact journals like The Leadership Quarterly and Business Strategy and the Environment , with recent work focusing on green HRM moderation models and digital leadership analysis. She has presented at major conferences including AOM , SMA , and SIOP . Scientific Awards AOM 2025 Best Reviewer Award (Organizational Behavior Division) AOM 2024 Best Division Paper Award (CTO Division) Multimodal research grant (Binghamton University, $20,000) Multiple Teaching Honor Rolls at Binghamton University Professional Service includes committee work in Assessment and Faculty Search, and membership in prestigious organizations like the Academy of Management , SIOP , and Harvard Business Review Advisory Council . She teaches courses in leadership, organizational behavior, and organizational leadership.

• Bayesian Computation
• Sequential Monte Carlo Methods
• Diffusion Processes
• +7 more

Jeremy HENG is an Associate Professor at ESSEC Business School , specializing in Information Systems, Data Analytics and Operations . He is affiliated with both the France and Singapore campuses, with his current position at ESSEC France since 2024 and prior experience at Harvard University as a Postdoctoral Fellow (2017–2019). His research focuses on Bayesian computation , sequential Monte Carlo methods , diffusion processes , and Monte Carlo variance reduction techniques , particularly for high-dimensional and discretized models. PhD in Statistics (University of Oxford, United Kingdom, 2017) BSc in Statistics (University College London, United Kingdom, 2012) The majority of his publications address Bayesian inference , Monte Carlo methods , and stochastic differential equations , with applications in financial econometrics , generative modeling , and optimal control . He has received the 2022 Blackwell-Rosenbluth Award for his contributions to Bayesian analysis and has served as Co-Editor-in-Chief of Statistics and Computing (2022–2023).

• Computer Science
• Artificial Intelligence
• Machine Learning
• +7 more

Professor Adam Prugel-Bennett is a faculty member at the School of Electronics and Computer Science , University of Southampton. His research spans artificial intelligence, machine learning, and robotics, with a focus on 3D perception, explainable AI, and marine data analysis. He contributes to interdisciplinary projects including coastal morphological analysis and underwater robotics. Key research areas include: LiDAR-based 3D object detection Multilevel explainable artificial intelligence Georeferenced seafloor imaging Variational autoencoder applications Bathymetric mapping His recent work explores cross-domain adaptation, sensor fusion, and algorithmic stability. He supervises PhD students in computer science and engineering while collaborating with research groups like Vision, Learning and Control. Current projects involve EPSRC-funded initiatives on complex computational systems and AI-based knowledge exchange for coastal analysis.

Matilde BiniView profile

Professor

• Statistical Models
• Econometrics
• Multivariate Analysis
• +5 more

Matilde Bini is a Professor of Economic Statistics (SECS-S/03) and Director of the Department of Human Sciences at the University of European Rome (UER). She holds a PhD in Applied Statistics from the University of Florence (1995) and has taught Corporate Statistics and Statistical Methods for Business Decisions at UER since 2009. Visiting positions: University of Florida (USA), IMT Lucca, University of Cordova (Spain) Active in national research groups funded by the Italian Ministry of Research Editorial roles: Associate Editor for Statistical Methods and Applications and Italian Journal of Applied Statistics Her research focuses on generalized linear mixed models, structural equation models, multivariate methods, and longitudinal models. She specializes in robust diagnostic analysis for data quality and anomaly detection, with applications in labor markets, firm productivity, entrepreneurship, and higher education evaluation. Recent publications examine zero-inflated beta regression for university career prediction, multilevel modeling of educational effectiveness, and robust analysis of high-tech firm competitiveness. Her work frequently appears in peer-reviewed international statistics journals. Professional affiliations include the Italian Statistical Society, International Statistical Institute, American Statistical Association, Royal Statistical Society, and the International Society for Business and Industrial Statistics.

Joseph OrnsteinView profile

Assistant Professor

• Statistical Methodology
• Computational Social Science
• Urban Politics
• +3 more

Joseph Ornstein is an Assistant Professor in the Department of Political Science at the University of Georgia . He holds a PhD from the University of Michigan (2018) and has previously worked at the Brookings Institution and Washington University in St. Louis. Current affiliation: School of Public and International Affairs, University of Georgia Research focus: Statistical methodology, computational social science, and urban politics Key contributions: Development of R packages ( fuzzylink , promptr ) for LLM integration in social science Research Trends in his publications include leveraging large language models for text analysis, advancing record linkage techniques, exploring urban governance dynamics, and applying agent-based simulations to public health and policy challenges. His work spans methodological innovation and empirical policy evaluation. Collaborations with scholars like Elise Blasingame and Jake Truscott highlight his work on LLM applications in political science. He actively contributes to open-access platforms and software development for social science research.

• Statistics
• Meteorology
• Econometrics
• +4 more

Dr. Annette Christine Möller is a Professor at Bielefeld University's Faculty of Economics, where she leads the Chair of Data Science and is affiliated with the Department of Empirical Methods. She serves as a key researcher at the Bielefeld Center for Data Science (BiCDaS). Her contact information includes email (annette.moeller@uni-bielefeld.de) and phone (+49 521 106-4877). Her research focuses on: Developing statistical methods for weather forecast post-processing Copula-based modeling of multivariate dependencies Ensemble forecasting for climate risk mitigation Applications in renewable energy prediction Statistical computing and open-source tool development Her publication record (2019-2025) demonstrates strong emphasis on: Probabilistic weather forecasting techniques Vine copula applications in meteorology Statistical calibration of ensemble models Interdisciplinary work spanning climatology, epidemiology and education She leads significant research grants including: DFG project: 'Statistical post-processing of ensemble forecasts' (2018-2024) DFG project: 'Mitigating climate risks via copula-based forecast improvement' (2024-2026) She collaborates with the Technical University of Munich and maintains active research teams focused on statistical meteorology and climate informatics.

Aleksandra TurkiewiczView profile

Associate Professor

• Epidemiology
• Medical Statistics
• Register Based Epidemiology
• +4 more

Aleksandra Turkiewicz serves as an Associate Professor in Medical Statistics at Lund University's Faculty of Medicine, based at the Clinical Epidemiology Unit (IKVL). Her work focuses on statistical methodology development for orthopaedic and epidemiological research, with particular expertise in register-based studies and pre-clinical applications. She holds additional roles as a Researcher in Orthopaedics, participant in the Lund OsteoArthritis Division, and member of the LU Profile Area: Proactive Ageing. Her research interests center on register-based epidemiology with emphasis on bias quantification, multilevel models in omics applications, and methods for handling missing data. She is responsible for the mandatory statistics course for PhD students at the medical faculty with pre-clinical research focus, and serves as Associate Editor for Statistics at Osteoarthritis and Cartilage . Her work contributes to UN Sustainable Development Goals related to health and wellbeing. Analysis of her recent publications reveals a strong focus on osteoarthritis epidemiology, particularly knee and hip osteoarthritis, with increasing integration of genetic and metabolic factors in understanding disease mechanisms. Her work spans population-based cohort studies, clinical classification systems, and molecular-level investigations into joint tissue degeneration. Dr. Turkiewicz actively supervises doctoral students and participates in multiple research projects including EpiMusc2 (2025-2045), The Impact of Metabolic Health on Osteoarthritis (2023-2027), and several dissertation projects focused on thumb base osteoarthritis, meniscus repair outcomes, and socioeconomic inequalities in musculoskeletal care. Her collaborative network spans multiple institutions with significant international engagement as evidenced by her participation in multi-center studies.