جست‌وجوی استاد و پژوهشگر

Francis L. Huangمشاهده پروفایل

استاد

• Quantitative Methods
• Multilevel Modeling
• School Climate
• +۴ مورد دیگر

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.

• Leadership
• Organizational Behavior
• Work-Family Interface
• +۷ مورد دیگر

Jakob Stollberger is a Professor in Leadership and Organizational Behavior at Durham University Business School, affiliated with the Department of Management and Marketing and the Centre for Leadership and Followership. His research integrates behavioral science with organizational theory to explore leadership, innovation, and the evolving workplace, particularly in the context of artificial intelligence. His research interests include leadership behaviors , the work-family interface , creativity and innovation , and AI and the future of work . He employs advanced quantitative methods such as experience sampling, multilevel modeling, and latent growth modeling to investigate emotional and behavioral dynamics in organizations. His recent publications span top journals including Organization Science , Journal of Applied Psychology , and Human Relations , with themes centered on emotional complexity, team innovation, and human-AI collaboration. His work has been recognized with the Human Relations Reviewer of the Year 2022 and a shortlisting for the Rosabeth Moss Kanter Award . Human Relations Reviewer of the Year 2022 Award Shortlisted for the Rosabeth Moss Kanter Award for Excellence in Work-Family Research Stollberger has secured external funding from bodies like the ESRC, acts as Associate Editor for Human Relations , and regularly contributes to practitioner outlets such as the Harvard Business Review . He is open to PhD supervision in his areas of expertise and is an invited speaker at academic and professional institutions globally. He is actively involved in the Centre for Leadership and Followership , where he contributes to interdisciplinary research on leadership dynamics and organizational behavior.

• Signal Processing
• Electronics
• Biomedical Engineering
• +۵ مورد دیگر

Marek Miśkowicz serves as a Professor at the Department of Metrology and Electronics within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His primary institutional contact is miskow@agh.edu.pl, with physical location in building B-1, room 212. His research spans signal processing, biomedical engineering, and electronics, specializing in event-based sampling methodologies, time-to-digital conversion techniques, and reconstruction of bandlimited signals from nonuniform samples. Key contributions include QRS detection algorithms for ECG monitoring, POCS-based reconstruction frameworks, and event-driven control systems for industrial IoT applications. His work emphasizes resource efficiency in embedded systems and mobile health monitoring through approximate computing and adaptive sampling strategies. Recent publications (2022-2025) demonstrate consistent focus on signal reconstruction from irregular samples, with growing emphasis on spiking neural networks for event classification and industrial IoT optimization. Biomedical applications (particularly ECG analysis) and industrial control systems represent dominant application domains, while methodological innovations center on iterative reconstruction algorithms and temporal accuracy evaluation in noisy environments. No scientific awards were referenced in the source materials. No information regarding student advising or research grants was available in the provided documentation. The source texts contained no details about laboratory facilities, research teams, or collaborative groups associated with Professor Miśkowicz.

• Public Health
• Psychology
• HIV/AIDS
• +۷ مورد دیگر

Syed Noor is an Associate Professor at Louisiana State University Shreveport (LSUS) , affiliated with the College of Business Education since 2020. His research focuses on public health, psychology, and behavioral epidemiology, particularly addressing HIV/STI prevention, substance abuse, mental health, and health disparities among LGBTQ+ populations. University: Louisiana State University Shreveport School: College of Business Education Rank: Associate Professor His work explores social determinants of health outcomes, with a focus on minority stress, drug use patterns, and preventive care strategies among gay, bisexual, and other men who have sex with men (MSM). Recent studies examine PrEP efficacy , substance abuse correlations , and epidemiological trends in HIV/STI transmission across North America. The 15 most recent articles highlight a multidisciplinary approach to public health, integrating psychology for understanding behavioral risk factors and social science for methodological rigor. Key trends include syndemic theory , harm reduction strategies , and cultural competency in addressing health disparities among marginalized communities.

• Queer and Trans People in Education
• Higher Education Administration
• Quantitative Methods in Education Research
• +۵ مورد دیگر

Dr. Jason C. Garvey is the Executive Director for the Office of Institutional Research and Assessment and the Friedman-Hipps Green and Gold Associate Professor of Education at the University of Vermont. He merges data-informed decision making with social change in higher education. Education: Ph.D., College Student Personnel Administration, University of Maryland M.A., School Psychology, Ohio State University B.A., Educational Studies, University of Delaware Dr. Garvey’s research focuses on queer and trans inclusion in education, survey design, and quantitative methods. His work examines campus climate, financial barriers for LGBTQ+ students, identity development, and structural equity in higher education and STEM fields. Recent publications highlight trends in quantitative criticalism, STEM equity for marginalized identities, and institutional data practices. His research often intersects race, gender, and sexual orientation through queer and trans of color critiques. Scientific Awards and Achievements: Body of Work Award, AERA Queer Studies Special Interest Group (2025) ACPA Senior Scholar Award (2024) Fulbright-British Library Eccles Centre Scholar (2023) Joseph A. Abruscato Award for Excellence in Research (2020) Dr. Garvey leads QTPiE, a multi-institutional team addressing LGBTQ+ equity in education. He has secured grants from AAAS, AERA, and NSF to support data infrastructure and structural inclusion initiatives.

Thomas M Braunمشاهده پروفایل

استاد

• Adaptive clinical trials
• Bayesian methods
• Longitudinal data
• +۴ مورد دیگر

Thomas M Braun, PhD is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health. He serves as a Member of the Senate Advisory Committee on University Affairs (SACUA) and teaches graduate-level biostatistics courses. Dr. Braun is an internationally recognized expert in Bayesian adaptive clinical trial design, with primary focus on cancer treatment and prevention research. Dr. Braun's educational background includes: PhD in Biostatistics from the University of Washington, Seattle (1999) MS in Biostatistics from the University of Washington, Seattle (1996) BBA from the University of Wisconsin, Madison (1990) His research expertise spans adaptive clinical trials, Bayesian statistical methods, longitudinal data analysis, oncology, rare diseases, dyadic analyses, and mobile health technology. Dr. Braun has developed novel Bayesian clinical trial designs for discovering safe and effective chemotherapy and immunotherapy regimens for cancer treatment. He is also pioneering methods to incorporate patient preferences into randomized clinical trials and sequential multiple assignment randomized trials (SMARTs). Analysis of Dr. Braun's recent publications (2023-2025) reveals a strong concentration on small n sequential multiple assignment randomized trials (snSMARTs), Bayesian methodology for rare disease research, mobile health interventions for cancer patients and caregivers, and innovative approaches to merging electronic health records with clinical trial data. His work consistently bridges theoretical statistical methodology with practical clinical applications in oncology and transplantation medicine. Dr. Braun has secured significant research funding from: The Food and Drug Administration (FDA) for developing Bayesian clinical trial designs for rare diseases The Patient Centered Outcomes Research Institute (PCORI) for developing Bayesian methods incorporating patient preference in SMARTs He actively collaborates on multiple research projects examining cancer treatment disparities, immunotherapy regimens for melanoma and other cancers, and the Roadmap mobile health platform for improving outcomes in stem cell transplant recipients and their caregivers. His work often focuses on dyadic patient-caregiver relationships and developing innovative statistical methodologies for complex clinical trial designs.

Robyn Mallettمشاهده پروفایل

استاد

• Social Psychology
• Intergroup Relations
• Prejudice Reduction
• +۵ مورد دیگر

Robyn Mallett is a Professor of Social Psychology and serves as the Vice Provost for Academic Programs and Planning at Loyola University Chicago, where she is a core faculty member in the Department of Psychology. Her leadership extends across academic administration and research innovation within the university structure. Her educational background includes: Post-Doctorate Fellowship at the University of Virginia Doctorate in Social Psychology from The Pennsylvania State University Master's in Psychology from The Pennsylvania State University Bachelor's in Psychology from the University of Alaska Anchorage Professor Mallett's research examines how individuals navigate social worlds through the lens of bias confrontation, intergroup relations, and environmental behavior. She investigates why people confront prejudice (particularly sexism), how expectations shape intergroup interactions, and how identity-focused interventions can promote sustainable behavior. Her methodological approach combines high-impact lab studies, daily diary research, and qualitative analysis of real-time interactions. Analysis of her recent publications (2021-2025) reveals three dominant trends: 1) The complex dynamics of bias confrontation—especially how humor mitigates perceived sexism and affects confrontation outcomes; 2) Interventions to foster belonging and retention for underrepresented faculty; 3) Identity-based environmental campaigns that reduce resource consumption without technological fixes. Her work consistently bridges theoretical social psychology with actionable social change. Professor Mallett has secured significant research funding, including a Russell Sage Foundation grant for her intergroup forecasting error research. She has co-edited influential volumes such as Confronting Prejudice and Discrimination (2019) and Moving Beyond Prejudice Reduction (2011), demonstrating her leadership in translating psychological science into practical frameworks. She leads an active research laboratory that conducts field experiments on campus sustainability initiatives and collaborates with Loyola's Institute for Environmental Sustainability. Current projects focus on faculty mentoring frameworks and the civic impact of intergroup dialogue, reflecting her commitment to applying psychological insights to institutional challenges.

BAYRAM ÇETİNمشاهده پروفایل

استاد

• Measurement and Evaluation in Education
• Educational Statistics
• Research Methods in Education
• +۳ مورد دیگر

Professor BAYRAM ÇETİN is affiliated with Gaziantep University , where he serves in the Faculty of Education within the Department of Educational Sciences , specializing in Measurement and Evaluation in Education . His academic career spans multiple institutions, including Gazi University and Sakarya University. Doctorate in Measurement and Evaluation in Education (2005, Hacettepe University) Master’s in Measurement and Evaluation in Education (2002, Hacettepe University) Licence in Classroom Teaching (1993, Çukurova University) His research focuses on educational measurement , psychometric modeling , statistical analysis in education , and assessment equity . He has developed numerous scales and rubrics for educational evaluation, emphasizing SOLO taxonomy , CHAID analysis , and generalizability theory . Professor Çetin has authored 12 books/chapters, including Research Methods in Education and Measurement and Evaluation in Education , and published over 65 peer-reviewed articles. His recent work explores online formative assessment , ADHD diagnostics , and cross-cultural PISA data . He has supervised 25+ theses, including studies on cyberbullying , metacognition , and test development . He has held administrative roles such as Director of Research and Application Center (2022) and Dean (2020). His projects include international collaborations like Erasmus+ KA201 and national grants on virtual reality in medical education .

• Riemannian Optimization
• Matrix Manifolds
• Low-Rank Matrix Completion
• +۴ مورد دیگر

Pierre-Antoine Absil is a Full Professor at the Louvain Polytechnic School (EPL) , part of the Université catholique de Louvain (UCLouvain) , affiliated with the Mathematical Engineering Center (INMA) and the Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM) . His research spans Riemannian optimization , matrix manifolds , and low-rank approximation with applications in astronomical imaging , signal processing , and environmental data imputation . His work emphasizes Riemannian optimization algorithms matrix and tensor completion geometric data analysis applications in astrophysics and bioinformatics Recent publications focus on Stiefel and Grassmann manifolds , variable projection methods , and exoplanet detection via alternating minimization . While no explicit scientific awards are listed, his collaborations with institutions like IEEE and Springer highlight his impact in computational mathematics and engineering. He has supervised researchers such as Simon Vary , Guillaume Olikier , and Valentin Christiaens through projects in direct imaging , economic dispatch , and graph-based data processing . His laboratory, INMA , drives methodological advances in manifold-valued data analysis and nonlinear optimization .

Andrew Jirasekمشاهده پروفایل

استاد

• Medical Physics
• Radiation Oncology Physics
• Raman Spectroscopy
• +۳ مورد دیگر

Andrew Jirasek is a Professor at the Irving K. Barber Faculty of Science , University of British Columbia Okanagan , and serves as the Associate Dean for Graduate and Postdoctoral Training . He leads the Analytics in Medical Sciences (AiMS) Institute with a focus on Medical Physics and Radiation Oncology Physics , particularly utilizing Raman spectroscopy and 3D radiation dosimetry for cancer treatment verification. PhD from University of British Columbia His research involves developing polymer gel dosimeters for 3D radiation dose verification in complex therapies like volumetric modulated arc therapy (VMAT) , collaborating across physics, oncology, and engineering . He also investigates optical technologies to monitor biological responses during radiotherapy using Raman spectroscopy with machine learning for data analysis. Recent publications highlight advancements in 3D gel dosimetry , Raman spectroscopy for metabolic profiling , and iterative image reconstruction algorithms . His work spans dosimeter technology development , radiation therapy quality assurance , and clinical applicability studies for novel treatment verification methods.

• Spectral Analysis of Time Series
• Cointegration Methods
• Bootstrap Applications
• +۳ مورد دیگر

Evangelos Ioannidis is an Associate Professor at the Department of Statistics, School of Informatics and Statistics, Athens University of Economics and Business. Born in 1962, he holds a Mathematics PhD from the University of Heidelberg (1993) and has served in his current department since 1999, progressing from Lecturer (1999) to Assistant Professor (2007) and Associate Professor (2023). His expertise spans spectral analysis of time series , cointegration methods , and bootstrap applications in economic data analysis, with additional focus on Official Statistics and sampling techniques . University of Heidelberg: MMath (1987), PhD (1993) Researcher, University of Heidelberg (1987-1991) Visiting Researcher, University of Orsay, Paris Sud (1992-1993) OECD, Paris (1994-1998) National Institute of Labour (1999) His scientific contributions focus on time series econometrics, VAR model spectra, and R&D expenditure analysis. Recent work includes non-parametric spectral estimation and risk-based sampling methodology. He has collaborated with Eurostat on statistical projects (2012-2014). Current affiliations include the Athens University of Economics and Business , where he teaches and conducts research on economic time series analysis and statistical methods.

Yan Xiaمشاهده پروفایل

دانشیار

• Statistical Methods for Structural Equation Modeling
• Item Response Theory
• Categorical Data Analysis
• +۳ مورد دیگر

Dr. Yan Xia is an Associate Professor in the Department of Educational Psychology at the University of Illinois at Urbana-Champaign. He holds a Ph.D. in Educational Psychology and Learning Systems from Florida State University and completed a postdoctoral fellowship at Arizona State University's T. Denny Sanford School of Social and Family Dynamics. Key research areas: Statistical methods for structural equation modeling, item response theory, categorical/nonnormal/missing data analysis, and applications in special education, child development, organizational behavior, and sports psychology. Current appointments: Faculty, College of Education, University of Illinois (since 2025). Recent contributions: 2025 Monte Carlo simulation on developmental research generalizability, 2024 work on parallel analysis improvements, and meta-analysis of autism eye-tracking studies. His methodological work critically examines fit indices like RMSEA, CFI, and TLI in ordered categorical data contexts. Collaborations span autism research, educational interventions, and cross-cultural studies. Publications demonstrate expertise in DWLS/ULS estimation, factor retention, and missing data techniques. Scientific awards: Not explicitly mentioned in provided texts. Advising: Accepts new graduate students (2025). No student list provided.

• Psychopathology
• Eating Disorders
• Non-suicidal Self-injury
• +۵ مورد دیگر

Laurence Claes is a Professor and Head of Clinical Psychology at KU Leuven's Faculty of Psychology and Educational Sciences. His academic career spans multiple research domains with a focus on psychopathology, particularly eating disorders and non-suicidal self-injury (NSSI), examined through the lens of identity formation processes. He maintains strong affiliations with the LC&Y - KU Leuven Institute for Child and Youth and serves on several important committees including the Faculty Doctoral Committee for Psychology and Educational Sciences as ombudswoman for KU Leuven staff. Professor Claes' research primarily investigates the complex relationship between identity development and various forms of psychopathology, with special attention to adolescents and emerging adults. His work consistently demonstrates how identity processes serve as both risk and protective factors across multiple clinical presentations. He employs diverse methodologies including longitudinal studies, ecological momentary assessment, and both community and clinical samples to examine these relationships. Analysis of his recent publications reveals consistent thematic patterns across his work. His research shows strong connections between identity functioning and eating disorders, non-suicidal self-injury, body image concerns, and personality pathology. He frequently examines how identity processes mediate relationships between other psychological constructs and clinical outcomes. His work spans multiple contexts including cancer survivorship, athletic performance, and digital environments. Professor Claes actively supervises numerous doctoral candidates and collaborates extensively with researchers both within KU Leuven (particularly with Koen Luyckx) and internationally. His work appears in high-impact journals across psychology, psychiatry, and related fields, demonstrating the transdisciplinary nature of his research. His teaching responsibilities include courses on clinical diagnostics for adults and the elderly (P0V88A, P0X60A), psychological interventions (P0W42A, P0N41B), single-case research (P0X93A), and clinical psychology practicum (P0Y05A, P0W57A, P0Y08A), indicating his commitment to both research and clinical training.

Lorenzo De Stefaniمشاهده پروفایل

مدرس

• Algorithms
• Big Data
• Statistical Learning
• +۳ مورد دیگر

Lorenzo De Stefani serves as an Assistant Teaching Professor (formally titled Lecturer) in Brown University's Department of Computer Science, teaching core courses including Theory of Computation (CSCI1010), Design and Analysis of Algorithms (CSCI1570), and Data Science (CSCI1951A). His academic credentials feature dual doctoral degrees: a PhD in Computer Science from Brown University (2020) under Eli Upfal, and a PhD in Computer Engineering from the University of Padova (2016) under Gianfranco Bilardi. Additional qualifications include a Master of Science (2012) and Bachelor of Science (2009) in Computer Engineering from the University of Padova. PhD, Brown University, 2020 PhD, University of Padova, 2016 MSc, University of Padova, 2012 BSc, University of Padova, 2009 De Stefani's research centers on algorithmic innovation for large-scale data challenges, with emphasis on memory-efficient graph stream processing, statistical learning for multiple hypothesis testing, and Byzantine-resilient computation. His work bridges theoretical computer science and practical data analysis through rigorous probabilistic frameworks. Publication trends reveal consistent contributions to graph algorithm optimization, particularly in single-pass triangle counting for dynamic networks and I/O complexity bounds for fundamental operations like matrix multiplication. His methodologies prioritize computational efficiency while maintaining statistical validity in massive datasets. Scientific recognition includes: KDD Best Student Paper Award (2016) for Tríest streaming algorithms While advising PhD candidates during his doctoral studies at Brown and Padova, current student mentorship details remain unspecified. No major grant awards are documented in available materials. He actively participates in Brown's computer science research ecosystem through collaborative networks focused on algorithmic foundations and data science applications.

Zehang Richard Liمشاهده پروفایل

استادیار

• Statistical Methods
• Demography
• Epidemiology
• +۷ مورد دیگر

Zehang Richard Li is an Assistant Professor in the Department of Statistics at the University of California Santa Cruz. His research focuses on statistical methods for demography, epidemiology, and global health, with expertise in latent variable modeling, space-time models, survey sampling, data integration, and weakly supervised learning. He develops workflows and pipelines for complex statistical analysis and real-world data-driven decision-making. Ph.D. in Statistics from the University of Washington (advisor: Tyler McCormick) Postdoctoral Researcher at Yale School of Public Health (Department of Biostatistics, advisor: Forrest Crawford) His work emphasizes high-dimensional data and uncertainty quantification in prevalence mapping. He leads the development of the SUMMER R package and SAE4Health platform, including ShinyApps for subnational health indicator analysis. Current projects address farmworker health under climate change through a UCOP grant and Bayesian frameworks for integrating verbal autopsy data via NIH funding. Recent publications highlight methodological advances in small area estimation, verbal autopsy analysis, and pandemic modeling. Key article themes include: Bayesian hierarchical modeling and tensor decomposition (2025) Domain adaptation for cross-population cause assignment (2024) Verbal autopsy validation and diagnostic accuracy (2023) Bayesian multilevel poststratification for disease prevalence (2022) Respiratory virus transmission dynamics and immune mechanisms (2021) Grants supporting his research include: National Institutes of Health Melinda and Bill Gates Foundation Vital Strategies Hellman Fellows Program University of California Office of President (UCOP) UCSC Committee on Research He advises Ph.D. students Yu (Zoey) Zhu, Qianyu Dong, Sho Kawano, and Toshiya Yoshida, all of whom have received academic recognition for their work on Bayesian models, small area estimation, and prevalence mapping.