جست‌وجوی استاد و پژوهشگر

Lilianne Eningerمشاهده پروفایل

دانشیار

• Developmental Psychology
• Child Development
• Social Emotional Competence
• +۳ مورد دیگر

Lilianne Eninger serves as Associate Professor and Deputy Head of Department at Stockholm University's Department of Psychology within the Faculty of Social Sciences. She is an active researcher specializing in developmental psychology with a particular focus on child development and social emotional competence in educational contexts. Her work spans cross-cultural comparisons, intervention development, and assessment methodologies. Dr. Eninger's research primarily centers on social emotional competence in children, early childhood education interventions, and the cultural adaptation of developmental programs. Her work demonstrates particular expertise in the Promoting Alternative Thinking Strategies (PATHS) program and its implementation in Swedish preschool contexts. She investigates how socioeconomic factors, neighborhood conditions, and cultural contexts influence child development outcomes and intervention effectiveness, with significant contributions to understanding cross-cultural measurement invariance. Analysis of Dr. Eninger's recent publications reveals a strong focus on social emotional learning in preschool settings, with particular attention to cultural adaptation of interventions across different contexts. Her work spans methodological advancements in assessment tools, neighborhood effects on development, and the implementation of evidence-based programs in educational settings. The research demonstrates consistent application of rigorous quantitative methods including structural equation modeling and cluster randomized trials. Dr. Eninger leads and participates in multiple research projects including Parent Web - support for families with adolescents, Internet based parent training in Arab, Farsi and Dari, and Supporting positive youth development. As Deputy Head of Department, she contributes to academic leadership while maintaining an active research agenda focused on practical applications of developmental science.

• Transport
• optics
• fluctuations
• +۹ مورد دیگر

EUGENE MISHCHENKO is a Professor in the Department of Physics & Astronomy at the University of Utah , where he has held academic positions since 2004 (Assistant Professor), 2007 (Associate Professor), and 2010 (Professor). His research spans condensed matter physics , focusing on graphene , topological insulators , low-dimensional systems , and plasmon-mediated phenomena . He has contributed to understanding spin-polarized transport , electron-phonon interactions , and disorder effects in quantum materials. Education : BS in Physics (Chernivtsi National University, 1993), PhD in Theoretical Physics (Russian Academy of Sciences, 1996), JD (University of Utah, 2018). Research Trends : His work combines analytical quantum mechanics , many-body interactions , and nanoscale electrostatics . Key contributions include plasmon instability theories , resonant impurity coupling , and exciton lifetime dynamics in carbon nanostructures. His 2017-2025 studies revealed noise-induced quantum corrections , non-equilibrium thermodynamics , and sublattice-dependent transport anomalies . Scientific Awards : Early Career Teaching Award (University of Utah, 2008). Teaching Activities : He teaches advanced courses including Quantum Theory I (2023-2024) and Quantum Field Theory II (2024). His scientific grants include a DOE-funded project on Spin-Polarized Transport (2006-2021). No formal lab or team is mentioned in the provided texts.

• Social Psychology
• Aggression and Violence
• Moral and Justice
• +۳ مورد دیگر

Gunnar Lemmer is a Researcher at the Philipps-Universität Marburg in the Faculty of Psychology and Psychologische Methodenlehre department (since 2015), with a Vertretungsprofessur (visiting professorship) in Psychological Methods since 2018. He is affiliated with professional societies including the German Psychological Society (DGPs), American Psychological Association (APA), and the Society for Personality and Social Psychology (SPSP). Education: Dr. rer. nat. ("Summa cum laude") in Psychology His research focuses on intergroup relations , aggression and violence , moral and justice , meta-analysis , and methodological innovations in social psychology. His work spans empirical validation of analytical techniques, large-scale intervention evaluations, and theoretical contributions to mediation analysis. Lemmer’s peer-reviewed publications (2009–2019) demonstrate expertise in statistical mediation , prejudice reduction , psychometric validation , and meta-analytic methods . He has contributed to domains like chronic pain treatment , functional somatic syndromes , and preventive programs for children of mentally ill parents. Scientific Awards: Dr. rer. nat. ("Summa cum laude") Lemmer’s applied work includes external evaluations for projects like DB BahnPark GmbH’s anti-parking campaigns, the VIP-School outreach program for immigrant families, and the PiT-Hessen violence prevention initiative. He teaches courses in statistics , multivariate methods , experimental design , and critical reading of scientific work.

Hans-Georg Müllerمشاهده پروفایل

استاد

• Functional Data Analysis
• Fréchet Analysis
• Longitudinal Studies
• +۳ مورد دیگر

Hans-Georg Müller is a Distinguished Professor of Statistics at the University of California, Davis. His work focuses on functional data analysis, Fréchet analysis of random objects, and applications in longitudinal studies, brain imaging, and aging research. He has contributed to foundational methodologies such as Fréchet regression and Wasserstein-based statistical models. Key research interests include the statistical analysis of non-Euclidean data, covariance structures in high-dimensional spaces, and temporal dynamics of complex systems. Müller has published extensively in top-tier journals like the Annals of Statistics and IEEE Transactions on Information Theory. Awards: 2022 Rietz Lecture & Award (IMS), 2017 Humboldt Research Award, and multiple fellowships from prestigious societies. Advising: Recognized with the 2020 Graduate Program Advising & Mentoring Award at UC Davis. Publications: Over 200 papers, including influential works on Fréchet regression, Cox process regression, and longitudinal brain development studies. His research bridges theoretical statistics with applied domains, particularly in understanding neurodevelopmental processes and the statistical mechanics of aging biomarkers.

Rashelle Jean Musciمشاهده پروفایل

دانشیار

• Mental Health
• Latent Variable Modeling
• Prevention Science
• +۵ مورد دیگر

Rashelle Jean Musci, PhD, MS, is an Associate Professor at the Johns Hopkins University Bloomberg School of Public Health. Her primary affiliations span the Department of Mental Health, Biostatistics, and the Wendy Klag Center for Autism and Developmental Disabilities. She leads the Environmental influences on Child Health Outcomes (ECHO) Data Analysis Center and contributes to national prevention science leadership. Doctorate in Human Development (University of California, Davis, 2011) Master of Science in Child Development (UC Davis, 2008) Bachelor of Science in Neurobiology, Physiology & Behavior (UC Davis) Post-doctoral fellowships in Prevention Science (NIMH T32) and Adolescent Health (LEAH Award) Dr. Musci specializes in latent variable modeling, prevention research, neurodevelopment, and data harmonization. Her work integrates genetics into universal prevention programs, focusing on personalized intervention strategies, developmental processes, and mechanisms of program efficacy across populations. She has pioneered statistical methods for gene-environment interaction studies and discrete-time survival analysis in substance use research. Her article trends include neurodevelopmental disorders, mental health disparities, genetic moderation in prevention, and longitudinal modeling. Scientific awards highlight her teaching, mentoring, and methodological innovations. She serves as Principal Investigator on NIH grants and contributes to the ECHO program's neurodevelopmental research. Dr. Rose and Ali Kawi Professor of Mental Health (2019) Johns Hopkins Discovery Award (2018) AMTRA Award for Advising/Mentoring (2018) NIMH Child Prevention Fellowship (2013) Dr. Musci's methodological seminars and measurement courses train future public health professionals. She collaborates across disciplines, including the Women’s Interagency HIV Study and national studies on adolescent health, while advancing data harmonization techniques for multi-cohort integrative analysis.

Fedor Sandomirskiyمشاهده پروفایل

استادیار

• Microeconomic Theory
• Information Economics
• Economic Design
• +۴ مورد دیگر

Fedor Sandomirskiy is an Assistant Professor of Economics at Princeton University's Department of Economics. His research focuses on microeconomic theory, information economics, and economic design, blending insights from game theory and algorithmic approaches. Previously, he held roles including Associate Research Scholar and Lecturer at Princeton, a Linde Postdoctoral Fellow at Caltech, and postdoctoral positions at the Technion and HSE University. He has served on program committees for the ACM Conference on Economics and Computation (EC) since 2019. His work emphasizes methodological innovations to improve strategic interactions and mechanism design. Notable contributions include characterizing correlated equilibria, analyzing Bayesian persuasion via optimal transport, and developing algorithms for fair division of resources. He has been recognized with the EC'20 Best Paper Award for 'Feasible Joint Posterior Beliefs.' Research interests span game theory, mechanism design, fair division, and stochastic choice, with applications in market design, social choice, and information economics. Recent work explores narrow bracketing in games, stable matching frameworks, and decomposition of stochastic preferences. He teaches advanced courses such as Algorithmic Economics and has contributed to survey materials on topics like competitive resource allocation and information design. His interdisciplinary approach bridges computer science and economics, addressing foundational questions in welfare analysis and mechanism design.

Rui Wangمشاهده پروفایل

دانشیار

• Geriatric Epidemiology
• Neuroscience
• Dementia Research
• +۵ مورد دیگر

Rui Wang is an Associate Professor at the Swedish School of Sport and Health Sciences (GIH) in the Department of Physical Activity and Health, with a concurrent position at Karolinska Institute in the Department of Neurobiology, Care Sciences and Society. Their academic journey includes a PhD from Karolinska Institute (2011-2015) and a postdoctoral fellowship at the University of Wisconsin-Madison (2018-2020). Dr. Wang's research focuses on geriatric epidemiology, particularly examining the connections between physical activity, cardiovascular health, and brain function in aging populations. Their work investigates how lifestyle factors influence dementia risk, cognitive decline, and brain health in older adults. Specific areas include vascular risk factors and structural brain characteristics related to dementia, common risk and protective factors between stroke and Alzheimer's disease, neuroimaging markers in small vessel diseases, and the relationship between physical activity, fitness, and executive function in aging populations. Analysis of Dr. Wang's recent publications (2021-2025) reveals a strong focus on neuroimaging correlates of physical-cognitive relationships, with particular attention to dementia prevention through physical activity and cardiovascular health. Their work spans multiple disciplines including neuroscience, geriatrics, exercise physiology, and public health, with a consistent emphasis on population-based cohort studies and longitudinal analyses. Dr. Wang teaches courses including Physical Activity and Healthy Brain Functions (7.5 credits), Quantitative Methods (5 credits), and Latent Variable Modelling I. Structural Equation Modelling (7.5 credits) across undergraduate, master's, and doctoral programs. They supervise independent projects at both undergraduate and graduate levels. Their collaborative network includes established partnerships with the Division of Clinical Geriatrics at Karolinska Institute, the Stress Research Institute at Stockholm University, and the Department of Medicine at Gothenburg University, as well as international collaborators such as Ozioma Okonkwo at the University of Wisconsin-Madison and Fuzhong Xue at Shandong University.

Andres Marcos Encinas Bachillerمشاهده پروفایل

استاد

• Discrete Mathematics
• Network Analysis
• Linear Algebra
• +۴ مورد دیگر

Andres Marcos Encinas Bachiller is a full Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Faculty of Mathematics and Statistics and the Department of Mathematics. He holds a Licenciado and Doctorado in Mathematics from the same institution. Professional Category: University Titular (equivalent to Professor). Research Groups: MAPTHE (Matrix Analysis and Discrete Potential Theory), OMGRAPH (Optimization Methods on Graphs). His research focuses on discrete vector calculus, boundary and inverse problems on networks, effective resistance, and M-matrix theory. He has contributed extensively to matrix analysis and its applications in network science, particularly in electrical impedance tomography and graph theory. Recent work includes studies on tridiagonal M-matrices, group inverses, and applications in medical imaging through discrete inverse problems. His articles span linear algebra, network analysis, and combinatorial matrix theory, with a focus on theoretical and applied aspects of these fields. He has supervised doctoral research, including Álvaro Samperio Valdivieso's thesis on inverse problems in networks. His projects include developing EIT devices for breast cancer detection and optimizing methods on graphs. Collaborations involve researchers in algebra, engineering, and applied mathematics. He is actively involved in research networks, including the ALAMA network (Linear Algebra and Matrix Analysis), and has led competitive projects funded by Spanish and Catalan agencies.

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +۷ مورد دیگر

Kai Wang is a researcher affiliated with Nankai University's Institute of Machine Intelligence under the College of Computer and Control Engineering. His work spans interdisciplinary domains including artificial intelligence, machine learning, and engineering, with a focus on optimization algorithms, neural networks, and biomedical applications. Key research areas: AI, machine learning, biomedical imaging, robotics, environmental science, and optimization. Recent publications address UAV surveillance, medical diagnostics, and tensor-based computational methods. Collaborations include institutions across China, France, Japan, and the USA. His 2025–2026 publications highlight advancements in diffusion models, transformer architectures, and IoT-driven solutions for healthcare and infrastructure. No scientific awards or student advisories are explicitly mentioned in the provided records.

John Antonakisمشاهده پروفایل

استاد

• Organizational Behavior
• Leadership Development
• Charismatic Leadership
• +۴ مورد دیگر

John Antonakis is a Professor of Organizational Behavior at the University of Lausanne’s Faculty of Business and Economics (HEC). He also serves as a Visiting Scholar at ISM. His research focuses on charisma, leadership development, crisis management, and research methodology. Antonakis has secured over $2.45 million in research funding and has been recognized as a Clarivate-Web of Science Highly Cited Researcher (2019) and ranked in the world’s top 2% researchers by PLOS Biology (2020). His work spans prestigious journals like Science, Nature Human Behavior, and Academy of Management Journal. Notable contributions include studies on charismatic leadership’s economic impact, crisis rhetoric effects, and methodological critiques of statistical techniques. Antonakis has held editorial roles including Editor-in-Chief of The Leadership Quarterly and served on top journal boards. Media outlets such as the New York Times and BBC frequently cite his research. Key Research Themes: Charismatic leadership, crisis leadership, leadership measurement, and statistical methodology. Recent Focus: Deep learning applications in leadership detection, gender gaps in venture funding, and pandemic leadership strategies. His publications emphasize rigorous experimental design and transparency in research practices, contributing to methodological advancements in organizational studies.

Michael Zyphurمشاهده پروفایل

استاد

• Quantitative Methods
• Structural Equation Modeling
• Multilevel Modeling
• +۵ مورد دیگر

Michael Zyphur is a Professor of Quantitative Methods at the University of Queensland's UQ Business School, where he also directs the Institute for Statistical and Data Science (Instats). He holds a PhD in Industrial and Organizational Psychology from Tulane University (2006). His career includes academic positions at the University of Melbourne, University of Washington, and National University of Singapore. His research focuses on advanced quantitative methods, including structural equation modeling (SEM), multilevel modeling (MLM), and dynamic systems analysis. He co-developed the EDM software packages for Stata and R, advancing empirical dynamic modeling. Dr. Zyphur has published over 100 peer-reviewed articles, with 10,000+ citations, and trained 10,000+ researchers globally through workshops and courses. Key contributions include methodological innovations in cross-lagged panel models, Bayesian analysis, and measurement invariance. He chairs the Society for Quantitative Methods (SQM) and has led major grants as an ARC Future Fellow. His work spans social, health, and organizational sciences, emphasizing ethical research practices and bridging statistical rigor with real-world applications.

Pascal Kilianمشاهده پروفایل

پژوهشگر

• Machine Learning
• Psychometrics
• Educational Research
• +۷ مورد دیگر

Pascal Kilian is a postdoctoral researcher at the Methods Center within the Faculty of Economics and Social Sciences at the University of Tübingen. His work lies at the intersection of machine learning, psychometrics, and social sciences, with applications in education and sports analytics. PhD in Mathematics, University of Tübingen (2018) BSc/MSc in Mathematics and Physics, University of Tübingen (2007–2012) His research focuses on modeling human behavior through advanced statistical and machine learning techniques. Key areas include latent variable models , variational autoencoders , mixed-effects machine learning , and longitudinal data analysis . He applies these methods to problems such as predicting student dropout in mathematics and identifying talent in youth soccer. The recent publications reflect a strong trend toward integrating deep learning with psychometric theory , particularly in modeling multidimensional performance data. His work increasingly bridges artificial intelligence and behavioral science , with emphasis on non-linear dynamic models and individualized forecasting . He has received recognition for his doctoral work: Doctoral Award for Excellent PhD Thesis, University of Tübingen (Tübingen School of Education) Kilian has been involved in externally funded research projects, including those supported by the Federal Ministry of Education and Research (BMBF) and the Deutsche Telekom Stiftung . While no formal advisees are listed, he collaborates closely with senior researchers such as Prof. Augustin Kelava and Dr. Judith Glaesser. He contributes to the development of the mixedML framework and supports the DFB's Talent Identification and Development (TID) program through scientific analytics. He is affiliated with the Methods Center at the University of Tübingen, a hub for interdisciplinary methodological innovation in the social sciences, where he contributes to both applied research and method development in machine learning and psychometrics.

• Computational Geometry
• Discrete Geometry
• Convex Optimization
• +۶ مورد دیگر

Francisco Criado is a researcher with significant contributions to computational geometry, discrete mathematics, and applied algorithm design. His work spans theoretical and applied domains, including convex optimization, Voronoi diagrams, and geometric modeling in gas dynamics and earthquake decision-making frameworks. Collaborators : Francisco Santos, Sebastian Pokutta, Michael Joswig Key Venues : Discrete & Computational Geometry , Foundations of Computational Mathematics , NeurIPS Research Interests focus on geometric algorithms, polyhedral complexes, and applications of fuzzy logic. His 2025 papers explore convex hulls, zonotopes, and tropical geometry, while earlier works address gas dynamics and decision-making models. Article Trends reveal expertise in high-dimensional geometry, combinatorial optimization, and numerical methods. He frequently employs randomized and linear convergence algorithms in 2022–2025. Advising and Grants : No explicit data provided, but his extensive co-authorship network suggests collaborative research leadership.

• Applied Mathematics
• Partial Differential Equations
• Fluid Dynamics
• +۳ مورد دیگر

Jian-Guo Liu is a Professor in the Department of Mathematics and Department of Physics at Duke University. His research focuses on applied mathematics, partial differential equations, fluid dynamics, and kinetic theory. He has made significant contributions to areas including computational fluid dynamics, numerical analysis, and mathematical modeling of physical systems. His work often bridges theoretical analysis and practical computational methods, addressing challenges in fluid dynamics, plasma physics, and biomathematics. Key research interests include the analysis of Navier-Stokes equations, magnetohydrodynamics, chemotaxis models, and particle-based methods. He has developed novel numerical schemes for incompressible flows and contributed to the understanding of nonlinear dynamics in self-organized systems. Notable publications include studies on Hall-Magnetohydrodynamics, consensus-based optimization in machine learning, and random batch methods for particle systems. His work emphasizes both theoretical rigor and computational efficiency, with applications spanning fluid mechanics, materials science, and biological systems.

Kaylee Litsonمشاهده پروفایل

استادیار

• Quantitative Psychology
• Structural Equation Modeling
• Latent Variable Modeling
• +۷ مورد دیگر

Kaylee Litson, Ph.D., is an Assistant Professor in the Department of Psychology at the University of Houston, affiliated with the College of Liberal Arts and Social Sciences and the Industrial-Organizational Program. She is the Director of the Interdisciplinary Quantitative Methods Collaboratory, where she leads research in advanced statistical modeling and psychological measurement. B.S. in Psychology, Utah Tech University (2012) Ph.D. in Quantitative Psychology, Utah State University (2019) Postdoctoral Fellowship in Biostatistics, Temple University (2020) Dr. Litson's research lies at the intersection of quantitative psychology, education, industry, and health. She specializes in structural equation modeling (SEM), latent variable development, and the analysis of longitudinal, nested, and multi-method data. Her work emphasizes both theoretical and statistical rigor in measuring complex psychological constructs such as creativity, cognitive load, and soft skills. Her recent publications highlight trends in methodological innovation, particularly in latent interaction effects, finite mixture modeling, ergodicity in cognitive models, and the measurement of black-box psychological phenomena. She actively develops frameworks for integrating theoretical meaning with statistical best practices in latent variable specification. Dr. Litson has applied her methods to study graduate students' research self-efficacy, working memory models, and the impact of soft skills on career trajectories. While no specific awards are listed, her research program reflects a strong commitment to advancing quantitative methods in psychology. She is currently accepting PhD student applications for the 2025–26 academic year, indicating active mentorship and research supervision. Her lab focuses on improving the capture of variability in complex datasets, with future work likely extending into AI-assisted modeling, real-time cognitive assessment, and cross-domain applications of quantitative methods.