جست‌وجوی استاد و پژوهشگر

Evan Peckمشاهده پروفایل

دانشیار

• Information Visualization
• Human-Computer Interaction
• Computing Ethics Education
• +۵ مورد دیگر

Evan Peck is an Associate Professor in the Information Science Department at the University of Colorado Boulder, where he also serves as Associate Chair of Undergraduate Studies. His research focuses on reimagining data communication for diverse communities and integrating computing ethics into technical education. Collaborations include institutions like MIT, Worcester Polytechnic Institute, and UMass Amherst. Education: Ph.D. in Computer Science from Tufts University (2014). Prior Affiliation: Associate Professor at Bucknell University. Research Interests: Evan examines how implicit design assumptions about technology access and user experiences affect data visualization's societal impact. His work bridges Human-Computer Interaction and Information Visualization , emphasizing public engagement and equity. Recent projects analyze barriers in data literacy, ethical implications of visualization tools, and participatory methods for marginalized communities. Publication Trends: His recent articles highlight interactive visualization frameworks (e.g., SurpriseExplora , Charting EDA ), empirical studies on novice user challenges, and ethical design interventions. Collaborations often involve locational biases in data representation and tools for marginalized groups. Scientific Awards: Best Paper Award (ACM CHI 2023) Best Paper Award (ACM CHI 2019) Advising & Grants: Evan mentors Ph.D. and Master’s students in the Information Visions Lab and secured a Stage 2 grant from Mozilla’s Responsible Computing Initiative (2024). He advocates for undergraduate research integration and socially responsible computing curricula. Labs & Teams: He directs the Information Visions Lab (under construction), focusing on public data tools, and collaborates with the MIT Visualization Group and Visualization for Social Good steering committee.

• Medical Risk Literacy
• Evidence-Based Decision Making
• Fast-and-Frugal Decision Support Tools
• +۷ مورد دیگر

Odette Wegwarth is a Professor at Charité – Universitätsmedizin Berlin and Senior Research Scientist at the Center for Adaptive Rationality, Max Planck Institute for Human Development . Her work focuses on medical risk literacy , evidence-based decision making , and fast-and-frugal decision support tools , addressing critical issues like defensive decision making and conflicts of interest in medicine. PhD in Psychology (2007), Humboldt-Universität zu Berlin Habilitation in Medical Sociology (2015), Charité – Universitätsmedizin Berlin Her research bridges health communication and cognitive psychology , examining how statistical illiteracy affects clinical decisions and patient outcomes. Recent projects include evaluating interactive simulation tools for vaccine-hesitant populations and analyzing privacy-utility trade-offs in health data donation. She has received prestigious awards such as the Heisenberg Professorship (DFG, 2020) and appointments as a WHO expert (2021, 2019). Current grants include European Union Horizon Europe and German Research Foundation (DFG) funding for projects like 'Yellow4Flavi' and 'ERONA', emphasizing transparency in medical communication and preventive health strategies . Her leadership in infodemic management and cancer prevention (e.g., FORECEE project) underscores her commitment to empowering patients and practitioners through evidence-based tools and ethical risk communication frameworks.

• Computer Science
• Artificial Intelligence
• Medical Image Analysis
• +۳ مورد دیگر

Xi Wu is a faculty member at Chengdu University of Information Technology , affiliated with the School of Computer Science . He holds a PhD from Sichuan University (2012, College of Electronic and Information Engineering). Current Research Focus: Medical imaging, computer vision, and deep learning applications in healthcare Key Themes: PET image reconstruction, radiotherapy dose prediction, GANs, diffusion models, and facial expression recognition His recent work explores transformer architectures , semi-supervised learning , and uncertainty-aware models for tasks like tumor segmentation and multi-organ analysis. Publications emphasize cross-domain adaptation and multi-modal medical imaging .

Roberto Limongiمشاهده پروفایل

استادیار

• Neuroscience
• Cognitive Science
• Psychiatry
• +۹ مورد دیگر

Roberto Limongi is an Assistant Professor in the Department of Psychology at Brandon University, located within the Faculty of Arts. His research focuses on neuro-computational mechanisms linking language production to conceptual organization in long-term memory, utilizing fMRI, MRS, computational models, and natural language processing. He leads the Writing-brain Laboratory, investigating effects of writing on academic learning and developmental disorders. Key collaborators include Karl Friston (UCL) and David Galbraith (University of Southampton). Limongi has secured grants including a 2023-2024 New Faculty Grant ($7,052) and a 2024-2026 SSHRC Insight Development Grant ($75,000). He received the 2022 John R. Hayes Award for writing research excellence. Research Themes: Schizophrenia neurobiology, computational linguistics, neurochemical biomarkers, epistemic writing effects Recent Funding: Over $82k in grants Collaborators: Global network including UK & Canadian institutions His lab's current work combines neuroimaging with advanced computational methods to identify neural markers of writing's impact on memory consolidation. Notable findings include glutathione-glutamate interactions in psychosis and syntactic complexity as a schizophrenia biomarker. Training opportunities exist for undergraduates through research participation programs offering $50 payments.

• Substance Use Recovery
• Adolescent Mental Health
• Behavioral Health Systems
• +۳ مورد دیگر

Crystal Reinhart serves as Research Assistant Professor and Senior Research Scientist at the University of Illinois Urbana-Champaign's School of Social Work, with additional affiliation at the Center for Social & Behavioral Science. Her work bridges computational methods and behavioral health interventions, focusing on adolescent substance use recovery systems and community-based solutions. Her research centers on substance use recovery capital among adolescents and emerging adults, examining protective/risk factor patterns through Bayesian networks and machine learning. Key interests include problem resolution frameworks, certified peer specialist integration, and geographic determinants of intergenerational mobility impacting substance use. She investigates how socioeconomic factors and community systems shape recovery pathways, with strong emphasis on methodological innovation in behavioral data collection. Recent publications reveal a clear trajectory toward computational social work, applying advanced statistical techniques to map recovery capital and assess data reliability in adolescent populations. Her work consistently integrates public health, social work practice, and data science to address substance use through recovery-oriented frameworks rather than traditional treatment models. No scientific awards were documented in the provided materials. While specific advising relationships weren't detailed, her research outputs indicate active mentorship through co-authored publications with junior researchers. No grant information was explicitly referenced, though her statewide survey work suggests involvement in large-scale public health initiatives. Her affiliation with the Center for Social & Behavioral Science implies participation in interdisciplinary research teams focused on behavioral health systems, though specific lab structures weren't described.

Maximilian Rabeمشاهده پروفایل

پژوهشگر ارشد

• Computational Modeling
• Eye-Movement Control
• Psycholinguistics
• +۴ مورد دیگر

Maximilian Rabe is a Postdoctoral Researcher in the Department of Experimental and Biological Psychology at the Faculty of Human Sciences, University of Potsdam, with a secondary affiliation at the University of Copenhagen. Currently on parental leave until September 22, 2025, he maintains active research involvement in computational cognitive science, with particular focus on eye-movement dynamics during reading processes and psycholinguistic modeling. His dual institutional appointments reflect his interdisciplinary research bridging German and Danish academic communities. Dr. Rabe completed his academic training with a B.Sc. in Psychology from the University of Potsdam (2016), followed by an M.Sc. in Psychology - Cognition and Brain Science from the University of Victoria, Canada (2018), and earned his Ph.D. in Cognitive Science from the University of Potsdam in 2024 under the supervision of Ralf Engbert and Shravan Vasishth. His research program centers on computational and statistical modeling of cognitive processes, with specific expertise in eye-movement control, psycholinguistics, and memory systems. Dr. Rabe develops integrated cognitive architectures that simulate how humans process language and allocate visual attention during reading. His methodological approach combines experimental psychology with advanced Bayesian statistics and dynamical systems theory to create predictive models of cognitive behavior. Analysis of his publication trajectory reveals a consistent focus on developing sophisticated computational frameworks for understanding reading processes, with increasing emphasis on integrated models that couple syntactic processing with eye-movement control. A distinctive feature of his work is the development of specialized methodological tools, particularly R packages that advance research practices in cognitive science. His publications span high-impact journals in psychology, cognitive science, and methodology, demonstrating strong interdisciplinary reach. Dr. Rabe is actively involved in multiple research projects funded by the German Research Foundation (DFG), including Project B03 of Collaborative Research Center 1287 on eye-movement control and parsing processes, and Project B03 of CRC 1294 on parameter inference in dynamical cognitive models. His work at the University of Copenhagen investigating visual attention in virtual reality environments receives support from Villum Fonden. As a methodological innovator, Dr. Rabe has developed and maintains several important R packages including hypr for hypothesis-driven contrast coding, designr for experimental design, appRiori for Bayesian analysis, and RStanTVA for visual attention modeling. His commitment to open science practices is evident in his software development and preprint sharing. Working within Ralf Engbert's research group at the University of Potsdam, Dr. Rabe contributes to a vibrant interdisciplinary environment that combines experimental psychology, computational modeling, and advanced statistical methods. His research has implications for understanding fundamental cognitive processes and developing more accurate models of human information processing during language comprehension.

Tobias Rebholzمشاهده پروفایل

پژوهشگر ارشد

• Quantitative Methods for Behavioral Research
• Social Cognition
• Judgment and Decision-Making
• +۱۲ مورد دیگر

Tobias Rebholz is a quantitative psychologist and econometrician currently serving as a Visiting Research Scholar in the Management and Organizations Area at the Fuqua School of Business, Duke University. Previously, he was a postdoctoral research associate in the Social Cognition and Decision Sciences group at the Department of Psychology, University of Tübingen, Germany. His academic journey includes a PhD in Psychology from the DFG-funded Research Training Group 'Statistical Modeling in Psychology' at the University of Tübingen (2023), an M.Sc. in Economics with a focus on Econometrics and Applied Economics from the University of Konstanz (2017-2020), and a B.Sc. in Business Studies and Economics with a major in Psychoeconomics from the same institution (2014-2017). Dr. Rebholz's research centers on quantitative methods in social cognition and judgment and decision-making (JDM). His methodological expertise spans statistics, machine/deep learning, Bayesian inference, and data science, which he applies to investigate phenomena such as judgment aggregation, wisdom of crowds, joint decision-making, advice taking, multidimensional belief updating, anchoring effects, hindsight bias, and science acceptance. A key theme in his current work is quantifying the informational influence of qualitative information—particularly that generated by large language models (LLMs)—on human judgment processes. His recent publications demonstrate a growing focus on human-AI interaction, examining how people integrate advice from generative AI systems, the reliability of various cognitive biases, and the development of quantitative models for understanding information sampling. His work often employs advanced statistical modeling techniques to address ecological constraints in real-world decision contexts, with increasing emphasis on the intersection of traditional psychological phenomena and modern AI technologies. Dr. Rebholz has taught various courses at the University of Tübingen, including 'Heuristics and Biases,' 'Introduction to Machine Learning,' 'R-Programming,' and 'Machine Learning for Data-Driven Decision-Making.' His teaching reflects his interdisciplinary approach, bridging psychology, statistics, and computational methods to equip students with practical skills for behavioral research in the digital age.

Isak Roalkvamمشاهده پروفایل

مدرس ارشد

• Stone Age
• settlement patterns
• quantitative methods
• +۳ مورد دیگر

Isak Roalkvam is a Senior Lecturer in Archaeology at the University of Oslo, specializing in Stone Age studies with a focus on quantitative methods and computational approaches. His research integrates geographical information systems, programming (R and Python), and open research practices to investigate settlement patterns along the Norwegian coast during the Mesolithic period, particularly addressing challenges posed by relative sea-level changes over millennia. Dr. Roalkvam's educational background includes: PhD in Archaeology (2024) from the University of Oslo MSc in Computational Archaeology: GIS, Data Science and Complexity (2019) from University College London Master's degree in Archaeology (2015) from the University of Oslo Roalkvam's research centers on the application of quantitative methods to understand Stone Age settlement patterns, particularly along the Norwegian Skagerrak coast. He specializes in shoreline dating techniques, summed probability distributions of radiocarbon dates, and the relationship between Stone Age sites and sea-level changes. His technical expertise includes developing computational tools (such as the shoredate R package), statistical modeling, and GIS analysis. He is also actively engaged in promoting open research practices within archaeology, as evidenced by his recent publications on open access publishing in the field. Analysis of Roalkvam's publication record reveals a clear methodological trajectory from theoretical development to practical application. His work consistently addresses the complex relationship between archaeological site locations and relative sea-level changes, requiring sophisticated computational approaches. The development of the shoredate R package represents a significant contribution to the field, providing researchers with accessible tools for shoreline dating. More recently, his research has expanded to examine broader issues in archaeological publishing and open science practices, demonstrating his commitment to advancing methodological rigor and transparency in the discipline. Dr. Roalkvam is actively involved in two research groups at the University of Oslo: Materialities – past and present PalaeoTECH – Social approaches to Stone Age technology

Ranxiao Frances Wangمشاهده پروفایل

استاد

• Attention and Perception
• Spatial Cognition
• Visual Perception
• +۷ مورد دیگر

Ranxiao Frances Wang is a Professor of Psychology at the University of Illinois, with additional appointment at the Beckman Institute for Advanced Science and Technology. She holds her office in the Psychology Building and maintains an active research laboratory focused on spatial cognition and perception. Dr. Wang earned her Ph.D. from Massachusetts Institute of Technology before joining the University of Illinois faculty. Her research program spans multiple domains within cognitive psychology, particularly focusing on how humans process spatial information and attention. Her primary research interests include mind wandering and cognitive control, human-robot interaction, biologically-informed artificial intelligence, visual perception, and spatial memory. She has developed innovative methodologies to study spontaneous cognitive processes, particularly in meditation contexts, and has pioneered research on human navigation in non-Euclidean spaces. Her work bridges cognitive psychology with robotics, quantum physics, and virtual reality technologies. Analysis of her recent publications reveals a strong emphasis on the cognitive mechanisms underlying attentional states, spatial navigation in both Euclidean and non-Euclidean environments, and the application of cognitive principles to human-robot interaction. Her research consistently employs rigorous experimental methods combined with computational modeling to uncover fundamental principles of human cognition. Dr. Wang has received consistent research funding that has supported numerous graduate students and collaborative projects across disciplines. Her work has been published in top-tier journals across psychology, neuroscience, and human-computer interaction fields. She actively mentors graduate students in the Psychology Department and collaborates with faculty from multiple departments including Engineering, Computer Science, and Physics. Her laboratory maintains cutting-edge virtual reality equipment for spatial cognition research.

Jakub Bijakمشاهده پروفایل

استاد

• Statistical Demography
• Migration Modeling
• Demographic Uncertainty
• +۴ مورد دیگر

Jakub Bijak is Professor of Statistical Demography at the University of Southampton's Department of Social Statistics and Demography, with nearly 25 years of academic and international civil service experience. He actively contributes to the Statistical Sciences Research Institute (S3RI) and Centre for Population Change, leading research at the intersection of demographic theory, statistical methodology, and policy application. His work bridges demographic science with practical migration governance challenges across European institutions. He earned his PhD in economics with specialisation in demography from the Warsaw School of Economics, establishing his foundation in quantitative demographic analysis. His doctoral research directly informs his ongoing focus on methodological innovation in population studies. Professor Bijak's research centers on statistical demography, migration modeling under uncertainty, and demographic forecasting. He pioneers agent-based simulation approaches for migration decision-making and develops Bayesian frameworks for population projection. His work spans from paleodemographic analysis of ancient populations to contemporary migration governance, with particular emphasis on quantifying uncertainty in demographic processes. Recent projects explore digital trace data for real-time migration monitoring and kinship dynamics across the life course. Analysis of his 2024-2025 publications reveals three dominant trends: (1) Methodological innovation in migration forecasting using mixed-frequency data and agent-based modeling, (2) Integration of digital footprint analysis for timely migration estimates, and (3) Development of policy-relevant frameworks for managing demographic uncertainty. His work increasingly connects demographic modeling with robotics and aging populations, demonstrating cross-disciplinary impact. No scientific awards are documented in the provided source materials. Professor Bijak currently supervises two PhD students: Andrew Peter Hind (MPhil/PhD Social Statistics) and Md Sifat Ar Salan (PhD Social Statistics and Demography). He leads or co-leads multiple major research projects including QuantMig (European Union-funded), Bayesian Agent-based Population Studies (BAPS), and ESRC/EPSRC grants on population change. His projects frequently involve international consortia addressing migration scenarios, demographic uncertainty, and generational connections. He maintains active roles in the Statistical Sciences Research Institute and Centre for Population Change, contributing to interdisciplinary teams developing the QuantMig microsimulation platform and kinship modeling frameworks. His collaborative networks span demography, computer science, and policy institutions across Europe.

Jason Hiltonمشاهده پروفایل

دانشیار

• Demographic Forecasting
• Bayesian Statistics
• Human Mortality
• +۴ مورد دیگر

Jason Hilton is an Associate Professor in Social Statistics at the University of Southampton's Faculty of Social Sciences, specializing in demographic methods and data science. His work bridges academic research and practical applications through collaborations with government bodies and international organizations. His research focuses on probabilistic forecasting of demographic components (mortality, fertility, migration) and kinship modeling , employing Bayesian statistics and agent-based simulation techniques. Key interest areas include demographic forecasting, human mortality analysis, and modeling intergenerational relationships. Hilton's publications reveal strong thematic continuity in demographic methodology, with recent work emphasizing kinship dynamics (2024-2025), migration flow prediction (2021), and Bayesian mortality modeling (2021). His research consistently integrates computational approaches with real-world demographic challenges. As a supervisor, he currently guides four PhD students in Social Statistics and Demography: Andrew Peter Hind (MPhil/PhD), Md Sifat Ar Salan, Shu Li, and Clara Renee Girault. His research is supported by major grants including ESRC's 'Connecting Generations' project and the Bill & Melinda Gates Foundation's WorldPop initiative. Hilton contributes to the Centre for Population Change and Demographic and Migration Modelling research groups, focusing on projects like 'Modelling kinship and quantifying connections between generations' and 'FuturePop' for high-resolution population projections.

Kenneth Malmbergمشاهده پروفایل

دانشیار

• Cognitive Psychology
• Human Memory
• Episodic Memory
• +۷ مورد دیگر

Kenneth Malmberg is an Associate Professor in the Department of Psychology at the University of South Florida, within the College of Arts and Sciences. His research lies at the intersection of cognitive psychology, mathematical modeling, and human memory systems. He leads a lab focused on episodic memory, decision-making, and computational theories of cognition. PhD in Cognitive Psychology, University of Maryland (2000) NIH NRSA Postdoctoral Fellow, Indiana University (2000–2003) Dr. Malmberg's research investigates the cognitive and computational mechanisms underlying human memory. His work emphasizes mathematical and computational models to explain phenomena such as context-dependent memory, encoding and retrieval dynamics, aging effects on memory, and episodic decision-making. He integrates empirical data with formal modeling to develop unified theories of memory and cognition. His recent publications span foundational reviews, theoretical models, and empirical studies in top journals like Psychological Review , Cognitive Psychology , and Journal of Mathematical Psychology . Themes include recognition memory, frequency judgment, sequential dependencies, and the role of attention and perception in memory formation. His 2024 book, Human Memory: The General Theory and Its Various Models , represents a major synthesis of the field. Scientific recognition includes: American Psychological Association's Division 3 New Investigator Award (2004) Dr. Malmberg has been consistently supported by research funding, though specific grants are not listed. He advises graduate students in the Cognitive & Neural Sciences program and contributes to the training of future cognitive scientists. His lab continues to explore how memory representations are formed, maintained, and retrieved under varying cognitive conditions.

Dominique Makowskiمشاهده پروفایل

استادیار

• Clinical Neuropsychology
• Reality Perception
• Emotion Regulation
• +۱۴ مورد دیگر

Dominique Makowski is an Assistant Professor in Psychology at the School of Psychology, University of Sussex, where he leads the Reality Bending Lab (ReBeL) and teaches advanced courses in Bayesian statistics and psychophysiology. He has previously held research positions at Nanyang Technological University in Singapore, including as a Presidential Research Fellow. Education: PhD in Psychology, Université de Paris (2014–2018) MSc in Clinical Neuropsychology, Université de Paris (2012–2014) BSc in Psychology, Université de Paris (2009–2012) His research focuses on the neuropsychology of reality perception, including deception, fake news, visual illusions, and altered states of consciousness. He also investigates the role of the body and emotions in cognition, particularly interoception and emotion regulation, and is deeply committed to methodological innovation. He develops open-source software tools such as easystats , NeuroKit , Pyllusion , and Neuropsydia to enhance accessibility and reproducibility in psychological science. His recent publications reflect a strong trend in methodological development, neurophysiological signal processing, and the cognitive neuroscience of belief and perception. These works integrate Bayesian statistics, open science practices, and computational modeling to advance both theory and application in psychology. Scientific Awards: School of Psychology Award Winner 2024: Outstanding Student Supervision SIPS Commendation (2024): for NeuroKit University of Sussex Research Culture Awards Winner 2024: for engagement in Open Science SIPS Mission Award (2023): for easystats SIPS Commendation (2021): for BayestestR Best Article Award (Psychonomic Society, 2020): for main PhD paper Dr. Makowski has supervised numerous research assistants and students, particularly during his postdoctoral and faculty roles, and has secured research funding (e.g., $200k during his Presidential Fellowship). He has delivered workshops on psychophysiological methods, Bayesian statistics, and programming at institutions including the University of Basel, University of Zurich, and NTU Singapore. He leads the Reality Bending Lab (ReBeL) , which integrates neuroscience, psychology, and computational methods to study how humans perceive and interpret reality. The lab emphasizes open science, reproducibility, and interdisciplinary collaboration.

• Service Marketing
• Service Management
• Consumer Behavior
• +۱۱ مورد دیگر

Volker Georg Kuppelwieser is a Full Professor in Marketing at NEOMA Business School, France. His research focuses on service experiences , aging consumers’ behavior , customer inclusion , and methodological advances in marketing . With 12 years of industry experience in the service sector, he teaches services marketing , international services marketing , and quantitative research methods across PGE, MSc, and PhD programs. He serves as Associate Editor for the Journal of Services Marketing and has guest-edited multiple journals. Education: PhD in Service Management His research interests span service marketing , organizational behavior , group service dynamics , and customer experience . Recent work explores ethical tourism , CSR authenticity , AI-driven marketing tools , and healthcare services in Latin America. He integrates eye-tracking technology into service research and investigates virtual CSR engagement . Publications appear in journals like Journal of Service Research , Journal of Services Marketing , and Tourism Management . Key themes include service innovation , consumer behavior , aging demographics , and inclusive service design . He actively contributes to academic discourse through editorial roles and conference presentations.

• Meta-Analysis
• Bayesian Data Analysis
• Nonparametric Statistics
• +۴ مورد دیگر

CHRISTOPHER THOMPSON is an Assistant Professor in the Department of Educational Psychology at Texas A&M University, part of the College of Education & Human Development. He directs the Educational Research and Evaluation Laboratory (EREL). His expertise lies in methodological advancements, particularly meta-analysis and Bayesian data analysis, with applications in educational technology and public health. Education: Ph.D. in Measurement and Statistics, Florida State University (2016) M.S. in Measurement and Statistics, Florida State University (2011) B.S. in Secondary Mathematics Education, Florida State University (2009) Research focuses on video games in education, large-scale testing trends, and statistical education integration. Methodological work includes Bayesian meta-analysis, multivariate synthesis, and nonparametric statistics. Recent publications emphasize meta-analytic techniques applied to health, education, and social behaviors. His grants include Bayesian Methods Training for Educational Research ($800,000 pending) and a TAMU-funded project on bridging meta-analysis and applied research. No awards listed, but grants highlight his impact. Advised two doctoral students: Ashley Majzun (2023) and Brandie Semma (2020). The EREL lab focuses on quantitative research methodologies.