جستجوی استاد راهنمای دانشگاهی

Sebastian Engelkeمشاهده پروفایل

دانشیار

• Extreme Value Theory
• Spatial Statistics
• Graphical Models
• +۵ مورد دیگر

Sebastian Engelke is an Associate Professor at the Research Center for Statistics at the University of Geneva. His research focuses on extreme value theory, spatial statistics, graphical models, and machine learning applications in climate science and risk assessment. He holds an SNSF Eccellenza Grant and has contributed to foundational work on extremal graphical models and causal inference in heavy-tailed systems. Affiliations: University of Geneva, Research Center for Statistics Grants: SNSF Eccellenza Grant (2020–2025), Ambizione Fellowship (2015–2018) Education: PhD in Mathematics (2013, Georg-August-University of Göttingen), studies at UC Berkeley and University of Göttingen. Research Interests Engelke’s work bridges extreme value theory with modern machine learning, addressing challenges in climate extremes, risk quantification, and graphical model structures. Key areas include: Statistical modeling of multivariate extremes Causal discovery in heavy-tailed distributions Applications in environmental science and climate informatics Key Contributions Recent work includes advancements in extremal graphical models (via matrix completions), neural network approaches for extreme quantile regression, and validation methods for deep learning weather models. His research is published in top journals like Annals of Statistics and Journal of the American Statistical Association . Awards & Recognition Lambert Award (2021) Gumbel Lecture (2023) Fields Research Fellowship (2019) Teaching & Supervision Engelke teaches courses on machine learning, probability, and advanced topics in statistics at the University of Geneva. He supervises PhD students and postdocs in machine learning and extreme value theory. His students include Edoardo Vignotto (extremal random forests) and Nicola Gnecco (causal discovery in heavy-tailed models).

Xinghua Luمشاهده پروفایل

استاد

• Biomedical Informatics
• Translational Bioinformatics
• Systems Biology
• +۵ مورد دیگر

Dr. Xinghua Lu is a Professor in the Department of Biomedical Informatics at the University of Pittsburgh. He specializes in computational methods for identifying signaling pathways in biological processes and diseases, and statistical methods for knowledge extraction from biomedical literature. His work focuses on translational bioinformatics, systems biology, and natural language processing for text mining. Education: PhD from University of Connecticut Health Center Research Interests: Dr. Lu develops computational models to simulate biological signaling systems and applies latent variable models to analyze genomic and transcriptomic data. His recent projects include establishing a Center/Institute in Translational Bioinformatics and advancing NLP techniques for biomedical text analysis. Publications: His research emphasizes genomic alteration analysis, causal discovery from big data, and integrative modeling of gene expression. Key themes include cancer mechanism identification, systems biology applications, and machine learning-driven biomedical informatics. Affiliations: Department of Biomedical Informatics School of Computing and Information McGowan Institute for Regenerative Medicine (implied via research context)

Piotr Zwiernikمشاهده پروفایل

دانشیار

• Graphical Models
• Covariance Matrix Estimation
• Convex Analysis
• +۷ مورد دیگر

Piotr Zwiernik is a Professor Agregat in the Department of Economics and Business at Universitat Pompeu Fabra (UPF), Barcelona, and a member of the BSE Data Science Center. He is currently on leave from the Department of Statistical Sciences and the Department of Mathematics at the University of Toronto. His research bridges statistics, algebraic geometry, and machine learning, focusing on graphical models, covariance estimation, tensors, and algebraic methods in statistics. Research Interests: Graphical Models Covariance Matrix Estimation Convex Analysis Tensors Algebraic and Combinatorial Methods in Statistics Statistical Learning Theory His recent publications span high-impact journals such as the Annals of Statistics , Biometrika , and Journal of the Royal Statistical Society . The work demonstrates a consistent trend in developing mathematically rigorous frameworks for understanding dependence structures, latent variable models, and high-dimensional inference, often leveraging tools from algebraic geometry and convex optimization. Key themes include total positivity, tensor methods, and the geometry of statistical models. Scientific Recognition: Editorial Board Member, Journal of the Royal Statistical Society Series B (JRSS-B) Editorial Board Member, Biometrika Editorial Board Member, Scandinavian Journal of Statistics Editorial Board Member, Algebraic Statistics Zwiernik actively mentors students and is seeking PhD candidates with strong mathematical backgrounds at UPF and the Institute of Mathematics of UPC. He has led the development of several open-source R packages, including golazo , MTP2binary , and StructuralEM , facilitating research in graphical models and latent structures. His work is supported by a network of collaborations with leading statisticians and has significant theoretical and applied implications in data science. Laboratories and Research Groups: Statistics@UPF BSE Data Science Center

Søren Højsgaardمشاهده پروفایل

دانشیار

• Statistics
• Graphical Models
• Bayesian Networks
• +۷ مورد دیگر

Søren Højsgaard is an Associate Professor in the Department of Mathematical Sciences at Aalborg University, Faculty of Engineering and Science. His work bridges statistical theory, computational methods, and pedagogical innovation, with a strong focus on graphical models, Bayesian networks, and computer algebra systems in R. His research interests lie at the intersection of statistics, machine learning, and software development. He is particularly known for developing R packages such as caracas , gRbase , and sparta , which facilitate symbolic computation and efficient inference in probabilistic models. His work supports both advanced research and accessible teaching in data science. The recent publications highlight a consistent trend toward integrating symbolic mathematics with statistical computing, improving scalability in Bayesian network predictions, and enhancing statistics education through tools like Quarto and R. His contributions span theoretical algorithms, software implementation, and educational applications. Active contributor to open-source statistical software Focus on model-based prediction and symbolic computation Emphasis on teaching innovation using computational tools He has been involved in academic outreach through conference presentations and media engagement, discussing topics ranging from R programming to workplace safety modeling. His leadership role in the Department of Mathematical Sciences was recently highlighted in university communications. Søren Højsgaard leads and contributes to projects that combine rigorous statistical methodology with practical implementation, supporting both research and education in modern data science.

John Wixtedمشاهده پروفایل

استاد

• Episodic Memory
• Recognition Memory
• Signal Detection Theory
• +۳ مورد دیگر

Dr. John Wixted is a Professor in the Department of Psychology at the University of California, San Diego (UCSD). His research focuses on understanding episodic memory, particularly recognition memory mechanisms using signal detection theory. He investigates hippocampal representation of memory through single-unit recordings in epilepsy patients and applies this work to real-world issues like eyewitness reliability. Dr. Wixted directs the Wixted Memory Lab, emphasizing the intersection of basic cognitive science and legal applications. His work critiques traditional models like Atkinson-Shiffrin's (1968) and advocates for evidence-based reforms in criminal justice, such as optimizing lineup procedures to reduce wrongful convictions. He collaborates with legal experts and institutions like the International Association of Chiefs of Police to improve forensic practices. Notable contributions include explaining the confidence-accuracy relationship in eyewitness identifications and challenging assumptions about lineup designs. Dr. Wixted’s research spans neural mechanisms (e.g., hippocampal coding) to applied policy (e.g., jury instructions). Despite prolific publications, his profile does not highlight specific awards or grants. His lab’s website emphasizes translational research bridging psychology and law, with active engagement in high-profile cases like Charles Don Flores’ exoneration advocacy.

Ben Williamsمشاهده پروفایل

دانشیار

• Econometric Theory
• Identification and Estimation
• Latent Variable Models
• +۳ مورد دیگر

Ben Williams is an Associate Professor of Economics in the Department of Economics at the George Washington University, part of the College of Arts and Sciences. He joined the department in August 2012 following the completion of his PhD at the University of Chicago. Education: Ph.D. in Economics, University of Chicago His research focuses on econometric theory , particularly issues of identification and estimation in models with latent variables, and the application of such models to uncover causal effects. He also conducts applied research in labor economics and industrial organization , bridging theoretical econometrics with real-world economic analysis. While no recent publications are listed in the provided text, his methodological focus suggests engagement with structural modeling, microeconometrics, and causal inference frameworks. Scientific Awards: There is no public information available in the provided text regarding student advising, research grants, or formal leadership in labs or research teams. However, his long-standing position since 2012 suggests active participation in the academic and research life of the department.

Amy Dengمشاهده پروفایل

استادیار

• Machine Learning
• Data Mining
• Artificial Intelligence
• +۶ مورد دیگر

Amy Deng is an Assistant Professor at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e). Her research focuses on machine learning and data mining applications in social sciences and e-commerce, emphasizing explainable and robust AI techniques for temporal data analysis. She leads the Data Mining research group, contributing to advancements in graph neural networks, domain generalization, and societal event forecasting. Her work integrates causal reasoning, spatiotemporal modeling, and graph-based approaches to address challenges in event prediction and dynamic knowledge representation. Recent projects include developing adaptive normalization techniques for non-stationary time series and contrastive learning methods for graph homophily analysis. Research Interests: Machine Learning, Data Mining, Explainable AI, Temporal Data Analysis, Social Event Forecasting Labs/Teams: Data Mining Group at TU/e Education: Not explicitly listed in available texts, but affiliated with TU/e's academic programs in computer science and data science. Publications highlight contributions to graph neural networks (e.g., SimGCL), causal event modeling, and robust forecasting frameworks. She currently teaches courses on Generative AI Models, aligning with her research in advanced AI techniques.

Anne-Kristine Schankeمشاهده پروفایل

استاد

• Neuropsychology
• Rehabilitation psychology
• Health psychology
• +۳ مورد دیگر

Professor Anne-Kristine Schanke holds a Professor II position at the Institute of Psychology, University of Oslo, with clinical affiliation at Sunnaas Rehabilitation Hospital. She earned her Cand. psychol. (1980) and PhD (2003) from the University of Oslo, specializing in neuropsychology and clinical rehabilitation. Her research focuses on neuropsychology, rehabilitation psychology, and health psychology, particularly in traumatic brain injury, post-polio syndrome, and fatigue management. She has led numerous national studies, including projects on symptom validity assessment and quality of life in rare diseases. Schanke has been honored with the Handicap Prize (2000), Honorary membership in the Norwegian Neuropsychological Association (2017), and the King’s Medal of Merit (2019). She has served on regional research and ethics committees and collaborates across institutions via initiatives like the CHARMS rehabilitation center. Her teaching includes clinical practice at Sunnaas Hospital through the PSYC6300 course. Education: PhD (2003) on coping with polio late effects; Cand. psychol. (1980), University of Oslo Employment: Head Psychologist at Sunnaas Hospital (1991–present); Associate Professor at the Institute of Psychology (2009–present) Research interests span neuropsychological assessment, cognitive rehabilitation efficacy, and resilience in chronic illnesses. Over 100 peer-reviewed articles and book chapters highlight her contributions to traumatic brain injury outcomes, fatigue trajectories, and clinical interventions for executive dysfunction. Collaborations include multi-center studies on traumatic brain injury and symptom validity testing.

Helene Amundsen Nissen-Lieمشاهده پروفایل

استاد

• Clinical Psychology
• Psychotherapy Research
• Therapist Effects
• +۵ مورد دیگر

Helene Amundsen Nissen-Lie is a Professor of Clinical Psychology at the University of Oslo's Department of Psychology. She holds a PhD (2011) and Cand. psychol. (2002) from the University of Oslo, with a BSc in Psychology from the University of London (1997). Her research focuses on therapist effects, therapeutic relationships, and mechanisms of change in psychotherapy, particularly in clinical populations like personality disorders and depression. She leads and collaborates on projects such as Modum Bad Therapist Lab, CAMP, and the Nordic Psychotherapy Training Study (NORTRAS). Key roles include co-supervising PhD candidates and contributing to longitudinal studies like the BAP study on clinical trainee development. Her teaching includes courses on psychological treatment, research seminars, and clinical practicums. Awards include the 2014 Young Researcher Award from the International Federation for Psychotherapy. Research emphasizes therapeutic alliance, psychotherapy outcomes, and clinician competence. Notable work includes studies on emotion-focused therapy for parents and equine-facilitated psychotherapy. She is also involved in evaluating residential couple therapy and depression treatment initiatives like DFront.

Ziping Xuمشاهده پروفایل

استادیار

• Statistical Machine Learning
• Reinforcement Learning
• Adaptive Experimental Design
• +۴ مورد دیگر

Ziping Xu is an Assistant Professor at the University of North Carolina at Chapel Hill's School of Data Science and Society. His primary research focuses on statistical machine learning, reinforcement learning (RL), and their applications in digital interventions, particularly in mobile health. He joined UNC in July 2025 after a postdoc at Harvard University with Susan Murphy and a PhD from the University of Michigan under Ambuj Tewari. His educational background includes a B.S. in data science from Peking University, advised by Song Xi Chen. Xu is actively involved in designing RL components for real mobile health clinical trials, such as ADAPTS-HCT targeting medication adherence in adolescents and young adults undergoing bone marrow transplants. Ziping’s academic journey includes: Bachelor of Science in Data Science, Peking University (2018), advised by Prof. Song Xi Chen. PhD in Statistics, University of Michigan (advisor: Prof. Ambuj Tewari). Postdoctoral Fellow in Statistics at Harvard University (2023-2025, advised by Prof. Susan Murphy). His research explores data-driven decision-making approaches, particularly reinforcement learning (RL) and its applications in digital interventions. Key areas include: Sample-efficient algorithms leveraging structural information for mobile health. Statistical inference for adaptively collected data in clinical trials. Transfer learning methodologies addressing domain shifts in healthcare data. Designing RL-based solutions for real-world mobile health trials, such as ADAPTS-HCT. Ziping Xu advises students in his research areas but no specific advisees are listed. Details on grants are not provided in the text. He leads the RL algorithm design for the ADAPTS-HCT clinical trial, collaborating with healthcare teams to enhance medication adherence through digital interventions.

Mark Packardمشاهده پروفایل

استاد

• Neurobiology of Memory
• Multiple Memory Systems
• Basal Ganglia Function
• +۳ مورد دیگر

Mark Packard is a Professor at Texas A&M University, affiliated with the Institute for Neuroscience. His research focuses on the neurobiological bases of memory, particularly investigating multiple memory systems involving the hippocampus, basal ganglia, and amygdala. He examines the roles of neurotransmitters like dopamine, acetylcholine, and glutamate in memory formation and the modulation of these systems by emotional and hormonal factors. Current projects explore how stress hormones and amygdala activation influence memory storage processes. Research Interests: Neurobiology of memory systems Dissociation of hippocampal and striatal memory functions Neurochemical mechanisms (dopaminergic/cholinergic systems) Amygdala’s modulatory role in memory Hormonal impacts on cognition Publications highlight advancements in understanding extinction learning processes, NMDA receptor roles in habit memory, and the implications of multiple memory systems for psychopathology. His work bridges animal models and human studies, with recent focus on cocaine dependence’s effects on memory systems. He currently accepts graduate students for 2024-2025 enrollment.

• Marketing
• Consumer Behavior
• Brand Loyalty
• +۶ مورد دیگر

Marcel Paulssen is a Full Professor of Marketing and Director of the Institute of Management at the University of Geneva's Geneva School of Economics and Management. He previously served as Assistant Professor at Humboldt University Berlin and held visiting roles at the University of Michigan. His academic journey includes doctoral research under Prof. Rick Bagozzi and professional experience in strategic marketing at Daimler AG's Society and Technology Research Group. Education: Ph.D. in Business Administration, Technical University of Berlin Prior professional roles included four years as a strategic marketing manager at Daimler AG, and a Japanese-German Center scholarship at Matsushita in Tokyo. Research Interests: Focuses on the intersection of psychology and marketing, particularly consumer behavior dynamics. Current topics include: Brand loyalty and choice mechanisms Fair trade consumption patterns Transport mode selection behavior Product defect perceptions and recovery strategies Boundary spanners in business relationships Norms in customer-company interactions Awards: Best Paper Award at IMP-Conference (2007) Runner-up for German Marketing Association's Best Dissertation Credit Suisse Award for Best Teaching Shortlisted for Reimagine Education Award Collaborations & Grants: Secured funding from Credit Suisse, EADS, Daimler, Volkswagen, and German Federal institutions. Active in cross-company research partnerships. Entrepreneurial Contributions: Co-founded FactWorks (research analytics) and SofaTutor (edtech), demonstrating industry-academia integration.

Kevin Reuningمشاهده پروفایل

دانشیار

• Political Parties
• Social Movements
• American Politics
• +۷ مورد دیگر

Kevin Reuning is an Associate Professor in the Department of Political Science at Miami University, affiliated with the Center for Analytics and Data Science. His work bridges political science and data science, focusing on American political parties, social movements, and advanced quantitative methods. Research Interests: His research investigates partisan networks, protest dynamics, and political activism, particularly during major electoral events like the 2016 conventions. He employs sophisticated methodological tools including latent variable modeling and social network analysis to understand political behavior and organizational structures. His recent and forthcoming publications reflect a strong trend in analyzing media influence, public opinion formation, and the strategic behavior of political actors and activists. The integration of computational methods with substantive political inquiry defines the core of his scholarly output. Scientific Awards: No awards listed in the provided text. Advising and Grants: While specific advisees and grant awards are not mentioned, Dr. Reuning encourages academic collaboration, especially regarding state-level party network data. His virtual appointment system suggests active mentorship and engagement with students and researchers. Labs and Teams: He is affiliated with the Center for Analytics and Data Science at Miami University, indicating participation in interdisciplinary data science initiatives and collaborative research environments.

Anne Herrmannمشاهده پروفایل

استاد

• Business Psychology
• Behavioral Economics
• Consumer Decision-Making
• +۳ مورد دیگر

Prof. Dr. Anne Herrmann is a Professor and Head of the Institute for Market Supply and Consumer Decision-Making at the University of Applied Sciences Northwestern Switzerland (FHNW), School of Applied Psychology. She holds a PhD from Leuphana University (2013) and has extensive experience in research roles across academic and corporate settings, including OPP Ltd. and Kalaidos Switzerland. Her work focuses on behavioral economics, healthcare technology adoption, and consumer decision-making in healthcare contexts. Education: Business Psychology (2001–2007, University of Applied Sciences Lüneburg), followed by research roles in the UK and Germany before joining FHNW in 2014. Appointed full professor in 2017 and Institute Head in 2017. Research interests include the intersection of technology and healthcare (e.g., telepresence robots in elderly care), antibiotic resistance communication, and consumer health behavior. She leads projects funded by Swiss Federal Office of Public Health (BAG) and has published widely in peer-reviewed journals like Risk Analysis and Journal of Vocational Behavior . Her research integrates quantitative and qualitative methods to address public health challenges, with recent emphasis on aging technologies and market entry strategies for AgeTech solutions in care facilities.

Lale Khorramdelمشاهده پروفایل

پژوهشگر

• Psychometrics
• Large-Scale Assessment Methodologies
• Educational Measurement
• +۳ مورد دیگر

Dr. Lale Khorramdel is a Research Director at the TIMSS & PIRLS International Study Center, Boston College, leading research in digital assessments and psychometric modeling. She holds a PhD in Psychology from the University of Vienna (2010) and has extensive experience in international large-scale assessments (PISA, PIAAC, TIMSS). Her work focuses on psychometric methodologies, response style analysis, and cross-country comparability. Current roles include adjunct professorships in large-scale assessment methodologies and leadership in projects involving adaptive testing and process data analysis. Education: PhD in Psychology (2010), University of Vienna; M.Sc. in Psychology (2004) Key Roles: Research Director, Boston College (2022–present) Associate Director, Boston College (2020–2022) Senior Scientist, National Board of Medical Examiners (2018–2020) Research Interests: Dr. Khorramdel specializes in advanced psychometric models (IRT, mixture distributions), measurement invariance, and the analysis of process data from digital assessments. She has pioneered work on automated scoring of graphical response items and improving cross-cultural comparability in international studies like TIMSS and PISA. Publications Overview: Her 15 most recent articles emphasize innovations in psychometric modeling, including response style differentiation, multistage adaptive testing, and applications of machine learning in educational assessments. Key contributions include methodologies for handling careless responding and improving PISA/PIAAC measurement precision. Awards: ETS SPOT Award 2017 (PIAAC) NBME Spot Award 2019 (BJMSP editorial work) BJMSP Top Downloaded Paper 2018-2019 Advising & Grants: She has led major projects funded by UNESCO and OECD, including PIAAC scaling and TIMSS/PIRLS concordance analyses. Her work often involves collaborative teams with researchers like Dr. Matthias von Davier and Dr. Kentaro Yamamoto. Labs/Teams: Core member of the TIMSS & PIRLS Center, part of Boston College's MESA Department, collaborating internationally on large-scale assessment frameworks.