جستجوی استاد راهنمای دانشگاهی

Giuseppe PAGNONI is an Associate Professor at the Department of Biomedical, Metabolic and Neurosciences of Università di Modena e Reggio Emilia . His research spans neuroscience , computational modeling , meditation , and brain network analysis . He teaches courses such as Neural Systems Modeling for Bioengineering students and Human Physiology and Pathophysiology in Medicine programs, with a focus on predictive coding , default mode network , and emotional valence processing . Pagnoni has contributed to ENIGMA-Meditation , a global initiative exploring meditation's neuroscientific basis, and developed sparse Bayesian models for analyzing brain network individual differences. His work on Alzheimer's disease investigates anosognosia mechanisms through resting-state fMRI, while other studies examine ghrelin's role in obesity-related reward processing and oxytocin's effects on social anxiety . He employs dynamic causal modeling and predictive processing frameworks to study cognitive effort, mind wandering, and neuromorphic learning. His methodological expertise includes functional MRI , independent component analysis , and computational simulations .

• Neuroscience
• Computational Modeling
• Meditation
• +۵ مورد دیگر

Cecilio Tamarit is a Professor of Applied Economics at the University of Valencia, where he holds a Jean Monnet Chair and coordinates the Research Group on Economic Integration (INTECO). He is also Vice President of the Free Association of Economics (ALDE) and President of the Economics Committee of the National Commission for the Evaluation of Research Activity (CNEAI). Master's in Economics, College of Europe (Bruges) Extraordinary Doctorate Award, University of Valencia (1992) Schuman Scholar, European Parliament (1988) Visiting Scholar, University of Nottingham (1992) Senior Fellow, Wharton School, University of Pennsylvania (1996) His research focuses on applied economics, macroeconomics, and European economic integration, with strong emphasis on modeling and policy analysis. He has contributed extensively to understanding economic convergence, fiscal policy, and international trade dynamics within the EU framework. The 15 most recent publications reflect a consistent focus on empirical and policy-oriented economics, spanning macroeconomic modeling, international trade, public policy, and financial economics. His work frequently appears in high-impact journals, demonstrating sustained scholarly engagement in applied economic research. Scientific Awards: Extraordinary Doctorate Award in 1992 from the University of Valencia He has advised numerous research projects and led major evaluation initiatives in economic research. While specific grants are not listed, his leadership roles in national evaluation bodies and research groups suggest significant involvement in funded research and policy advising. He has directed the European Documentation Center at the University of Valencia and served as an advisor to the Spanish government. He leads the INTECO research group, focusing on economic integration and policy modeling, contributing to both academic and policy debates in European economics.

• Applied Economics
• Economic Integration
• Macroeconomics
• +۴ مورد دیگر

Judit Vall Castelló is a Full Professor in the Department of Economics at the Universitat de Barcelona and a key researcher at the Institut d’Economia de Barcelona (IEB-UB), where she serves as Director of the Public Policies Research Program. She is also an external affiliated researcher at CRES-UPF and a Research Fellow at IZA. Her academic affiliations reflect a strong commitment to interdisciplinary research in public policy and applied economics. Her research focuses on the evaluation of public policies, particularly in Health Economics and Labour Economics . She employs advanced econometric methods to assess the causal impact of policy interventions on health outcomes, labor market dynamics, and social inequalities. Her work often uses quasi-experimental designs and natural experiments to draw robust conclusions. The trends in her recent publications reveal a consistent engagement with pressing societal issues such as healthcare access, aging populations, unemployment, mental health, and policy reforms. Her studies span multiple countries and leverage large administrative datasets, contributing significantly to both academic literature and evidence-based policymaking. Scientific Awards: ICREA Academia (2022) Fulbright-Schuman Grant Robert Solow Postdoctoral Fellowship Pre-doctoral Marie Curie Research Fellow Judit Vall Castelló has advised numerous PhD and Master’s students in economics and public policy, though specific names are not listed. She has secured competitive research funding through national and international programs, including collaborations with NBER, UNICEF, and Cambridge University. Her research has informed policy debates in Europe and beyond. She is actively involved in research teams at IEB and CRES-UPF, contributing to collaborative projects on health systems, labor markets, and fiscal federalism. Her leadership in the Public Policies Research Program fosters interdisciplinary dialogue and innovation in policy evaluation.

• Health Economics
• Labour Economics
• Public Policy Evaluation
• +۳ مورد دیگر

Linda Valeri is an Assistant Professor of Biostatistics at the Mailman School of Public Health, Columbia University, and an Adjunct Assistant Professor in Epidemiology at the Harvard T.H. Chan School of Public Health. Education: M.Sc. in Economics and Social Sciences from Bocconi University Ph.D. in Biostatistics from Harvard University Her research focuses on causal mediation analysis, environmental health, and the development of machine learning approaches under Bayesian frameworks to estimate complex health outcomes. She has contributed to software development for mediation analysis in SPSS, SAS, STATA, and R. Dr. Valeri leads NIH-funded studies on causal inference in mHealth and mechanisms underlying Alzheimer’s disease. She serves as Associate Editor for the International Journal of Biostatistics and as Statistical Editor for JAMA Psychiatry and JAMA Network Open .

• Biostatistics
• Epidemiology
• Causal Mediation Analysis
• +۹ مورد دیگر

Prof.dr. R. Arthur Bouwman is a Full Professor at the Electrical Engineering department of the Eindhoven University of Technology and affiliated with the Eindhoven MedTech Innovation Center . His work bridges biomedical engineering and clinical medicine , focusing on physiological monitoring , medical imaging , and biomarker validation for real-time patient care. Education : Not explicitly detailed in the text His research emphasizes non-invasive diagnostics and AI-driven health monitoring , including video-based cardiac arrhythmia detection , sweat-based renal function analysis , and Doppler ultrasound optimization . Recent work explores causal inference in observational studies and automated early warning systems in surgical wards. Key article trends highlight biomedical signal processing , medical device innovation , and integration of wearables in perioperative care . Collaborations span institutions like Catharina Hospital and research centers across cardiovascular and renal domains.

• Biomedical Engineering
• Medical Informatics
• Cardiology
• +۳ مورد دیگر

Jian Tang is an Assistant Professor at HEC Montreal and the Montreal Institute for Learning Algorithms (MILA), as well as an Associate Professor at the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also affiliated with IVADO (Institut de valorisation des données) as a member. His research spans multiple institutions including collaborations with leading biology labs worldwide and access to extensive computational resources through industry partners. Ph.D. in Computer Science, Peking University (2009-2014) Visiting Ph.D. student, University of Michigan (2011.10-2013.8) B.S. in Mathematics, Beijing Normal University (2005-2009) Professor Tang's research focuses on the intersection of deep learning and graph theory, with particular emphasis on geometric deep learning, knowledge graph reasoning, and applications in drug discovery. His work bridges symbolic and neural approaches to create robust reasoning systems that can handle complex structured data. He has pioneered techniques in graph representation learning that have significantly advanced the field of molecular property prediction and protein design. His publication record shows a clear trajectory toward applying geometric deep learning to biological problems, with a growing emphasis on protein design, molecular conformation generation, and multi-omics analysis. Recent work demonstrates sophisticated integration of 3D geometry with deep learning architectures to model complex biomolecular interactions. Canada CIFAR Artificial Intelligence Chairs (CCAI Chair) Tencent AI Lab Rhino-Bird Gift Fund Amazon Faculty Research Award Microsoft-Mila collaboration grant National Research Council Canada (NRC) Collaborative Research and Development Grant Professor Tang actively mentors doctoral and master's students, with six recent graduates working on cutting-edge topics including graph neural networks for reasoning, protein design, and molecular representation learning. His research is supported by substantial funding from industry partners including Microsoft, Amazon, and Tencent, as well as government agencies like NRC. He collaborates extensively with biology labs worldwide, applying AI to solve real-world biomedical challenges. He leads a research group focused on geometric deep learning for drug discovery, with active projects in protein design using geometric-aware models and large language models for multi-omics analysis. The group has access to thousands of GPUs through industry collaborations, enabling large-scale experiments in molecular simulation and generative modeling.

• Graph Representation Learning
• Deep Learning
• Reinforcement Learning
• +۷ مورد دیگر

David Vock is a Professor at the University of Minnesota in the Division of Biostatistics & Health Data Science . His research focuses on statistical methods for electronic health data, causal inference, dynamic treatment regimes, and clinical trial design. PhD in Statistics, North Carolina State University (2012) MStat in Statistics, North Carolina State University (2009) BA in Mathematics and Chemistry, St. Olaf College (2007) His work integrates machine learning with traditional statistical approaches to analyze censored data in healthcare settings. He specializes in SMART clinical trials for adaptive interventions and semiparametric theory applications. Collaborative projects span transplantation , infectious disease , and cardiovascular research . Recent publications emphasize COVID-19 biomarker analysis , monoclonal antibody efficacy , and health data interoperability . These studies appear in high-impact journals like New England Journal of Medicine and Lancet Microbe . Scientific contributions have earned him membership in the Delta Omega Honorary Society in Public Health . Collaborations include the ACTIV-3/TICO Study Group and interdisciplinary teams in data science and clinical research .

• Biostatistics
• Health Data Science
• Causal Inference
• +۷ مورد دیگر

Michael Roberts is the William H. Lawrence Professor and Professor of Finance at the Wharton School of the University of Pennsylvania. He serves as a Research Associate at the National Bureau of Economic Research and is affiliated with the Institute for Law and Economics and the Wharton Financial Institutions Center. His academic career spans teaching roles at UC Berkeley, Duke University, and Wharton, with a focus on corporate finance, banking, investments, and financial education. Education: B.A. in Economics, University of California, San Diego M.S. in Statistics, University of California, Berkeley Ph.D. in Economics, University of California, Berkeley Roberts's research bridges corporate finance, banking, and investments, with recent emphasis on AI and machine learning in financial decision-making. His work on financial contracts, capital structure, and payout policies has significantly advanced empirical finance. He has authored influential studies on debt covenants, credit supply effects, and financial literacy. His publications demonstrate expertise in empirical corporate finance , debt structuring , financial econometrics , and financial education . Key topics include collateralized loan obligations , interest rate impacts on contracts , and capital structure persistence . These works reflect his commitment to integrating rigorous methodology with practical financial challenges. Scientific Recognition: Jensen Prize (2016) Marshall E. Blume Prize (2015) Brattle Prize nominations (2008, 2005) Multiple teaching awards at Wharton and Duke Geewax, Terker, and Co. Prize (2010, 2006) As an educator, Roberts has taught undergraduates, MBAs, PhDs, and executives for over 25 years. He founded the Wharton Financial Analytics initiative and the Financial Proficiency Initiative (FPI), which provides financial education to high school students. His courses include Corporate Finance , Data Science for Finance , and Empirical Methods in Corporate Finance .

• Corporate Finance
• Banking
• Investments
• +۶ مورد دیگر

Professor Gesa Hartwigsen is a leading cognitive neuroscientist holding dual appointments as Professor for Cognitive and Biological Psychology at Leipzig University and Lise Meitner Research Group Leader at the Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS). She leads the Research Group Cognition and Plasticity, focusing on neural networks for higher cognitive functions, adaptive plasticity in cognition, and language network reorganization after stroke. Her educational background includes: Studies of Psychology (Dipl.-Psych., 2001-2006) at Kiel University PhD in Neurology (Dr. phil., 2007-2010) at Kiel University Habilitation in Psychology (2018) at University of Potsdam Hartwigsen's research spans cognitive neuroscience with particular emphasis on language processing, neural plasticity, and brain stimulation techniques. Her work investigates how brain networks support semantic cognition, how they adapt following neurological damage, and how they change across the lifespan. She employs advanced methodologies including transcranial magnetic stimulation (TMS), functional MRI, and concurrent TMS-fMRI to establish causal relationships in neural processing. Analysis of her recent publications reveals a strong focus on language networks, executive control mechanisms, and neural plasticity. Her work increasingly integrates advanced computational approaches with neurostimulation to understand semantic processing, speech comprehension in challenging environments, and recovery from language disorders. A significant trend is her development of methodological frameworks for concurrent TMS-fMRI, establishing new standards in the field. Her notable scientific recognition includes: ERC Consolidator Grant (2023-2027) for "The Flexible Brain: (Re-)shaping Adaptation in Semantic Cognition" NVIDIA GPU grant (Titan Xp Donation) Hartwigsen has secured substantial research funding, including multiple DFG projects as Principal Investigator. Her current DFG Research Unit (2023-2026) focuses on "Modulation of brain networks for memory and learning by transcranial electrical stimulation," where she serves as PI. She has successfully led multiple interdisciplinary projects examining language networks, neural plasticity, and brain stimulation effects. At the MPI CBS, she leads the Cognition and Plasticity research group, which investigates how brain networks support cognitive functions and adapt to challenges. Her team employs a multimodal approach combining neurostimulation, neuroimaging, and behavioral methods to understand the causal architecture of cognitive networks, particularly in language processing and semantic cognition.

• Cognitive Neuroscience
• Neural Plasticity
• Language Processing
• +۵ مورد دیگر

Emily Rauscher is a Professor in the Sociology Department at Brown University and a faculty affiliate of the Population Studies and Training Center, the Annenberg Institute for School Reform, and the Taubman Center for American Politics and Policy. Her research focuses on education and inequality, particularly examining how school spending impacts child well-being and equality. BA from Wesleyan University MPhil from Trinity College Dublin MS from University of Southern California PhD in Sociology from New York University Rauscher investigates the social benefits of educational investments, including equal achievement, improved occupational opportunities, health outcomes, and intergenerational mobility. Her recent work explores infant health disparities, Medicaid policy, and the impact of school funding reforms. She employs quantitative and mixed methods, causal inference techniques, and natural experiments in her research. Her publications reveal trends in educational equity, infant health inequality, and policy impacts. Key subfields include school funding efficiency, environmental health risks, and demographic disparities in health outcomes. Federation of Associations in Behavioral and Brain Sciences (FABBS) Early Career Impact Award NAEd/Spencer Foundation Postdoctoral Fellowship IPUMS Research Award Craig Anthony Arnold Faculty Research Award Rauscher has received funding from the Spencer Foundation, American Educational Research Association, William T. Grant Foundation, Gilead Foundation, Russell Sage Foundation, and National Academy of Education. She teaches courses on social inequality, statistics, sociology of education, and infant health disparities.

• Education
• School Spending
• Infant Health
• +۴ مورد دیگر

Modhurima Dey Amin is an Assistant Professor at Texas Tech University's Department of Agricultural and Applied Economics within the College of Agricultural Sciences & Natural Resources. Her work bridges econometric methodologies with contemporary issues in food and agricultural markets, emphasizing precision agriculture, energy economics, and industrial organization. PhD in Economics (Econometrics & Quantitative Economics) – Washington State University MSc in Statistics – Washington State University MSc in Applied Financial Economics – Illinois State University BSc in Economics – University of Dhaka, Bangladesh Dr. Amin's research explores: Food safety in retail environments and equitable access to nutritious food Market structures of specialty crops, particularly apple varieties Structural change detection in high-dimensional datasets using statistical methods Machine learning applications in agricultural and environmental economics Key trends in her publications include: Machine learning for causal inference and predictive modeling in agricultural economics Food retail dynamics, including store closures and consumer behavior Environmental economics and sustainable agricultural practices Precision agriculture and technology-driven farm management Her teaching focuses on: Econometrics Agribusiness Finance Corporate Finance Economic theory and policy analysis Contact: modhurima.amin@ttu.edu | Personal Website

• Applied Econometrics
• Statistics
• Data Science
• +۸ مورد دیگر

Hua He is a Professor in the Department of Biostatistics and Data Science at Tulane University's School of Public Health and Tropical Medicine, specializing in advanced statistical methodologies for public health research. Her work bridges theoretical biostatistics with practical applications in infectious disease diagnostics and causal inference frameworks. Her academic credentials include: PhD in Statistics, University of Rochester MA in Statistics, University of Rochester BS in Mathematics, Southwest Normal University, China Dr. He's research focuses on mixture population modeling, causal inference, longitudinal data analysis, and ROC analysis, with significant applications in tuberculosis diagnostics and molecular epidemiology. She develops novel statistical approaches for social media data analysis, drinking outcome modeling, and zero-modified datasets, emphasizing methodological rigor in public health contexts. Her textbook Applied Categorical and Count Data Analysis and edited volume Statistical Causal Inferences and Their Applications in Public Health Research establish her as a leading methodological authority. Recent publications (2023-2025) demonstrate a concentrated focus on CRISPR-based diagnostic technologies and next-generation sequencing for tuberculosis detection, with strong emphasis on point-of-care applications, diagnostic accuracy validation, and low-resource setting adaptations. These works integrate biostatistical innovation with molecular diagnostics to address critical gaps in infectious disease control. Her scientific recognition includes: Teaching Excellent Award (2020) Dr. He directs the Methodology/Biostatistics Unit at Tulane University Translational Science Institute (TUTSI) and serves as Principal Investigator for an NIH R01 grant Moving beyond description: statistical and causal inference for social media data , alongside multiple pilot studies and foundation-funded projects. She has contributed biostatistical expertise to dozens of NIH-funded investigations, particularly in tuberculosis and infectious disease research. As Co-director of TUTSI's Clinical Research Core and Statistical Deputy Editor for the American Journal of Public Health , she leads institutional research infrastructure while advancing methodological standards in public health science.

• Mixture population modeling
• Causal inference
• Longitudinal data analysis
• +۵ مورد دیگر

Peter Grünwald is full professor of Statistical Learning at Leiden University's Mathematical Institute and senior researcher in the Machine Learning group at CWI (Centrum Wiskunde & Informatica) in Amsterdam. His pioneering work on e-values establishes a transformative framework for statistical inference that overcomes critical limitations of classical p-values, enabling flexible experimental designs while maintaining rigorous error control. His research centers on e-values and e-processes as a unifying paradigm between Bayesian and frequentist statistics, with core innovations in safe testing, anytime-valid inference, and optional continuation. These methods allow researchers to gather additional data after initial analysis without inflating Type I errors and to determine significance levels post-hoc—addressing longstanding rigidity in Neyman-Pearson hypothesis testing. His publication trajectory reveals rapid adoption of e-values across disciplines: from foundational theory in PNAS and JRSSB to clinical applications in survival analysis (NEJSDS) and epidemiology (medrxiv meta-analysis). The 2022–2024 publications demonstrate methodological maturation, with implementations in R (safestats package) and growing use in social sciences (PsyArXiv) and causal inference (JASA). Scientific recognition includes: ERC Advanced Grant (2024) for developing flexible statistical inference theory via e-values His ERC-funded project drives current research, while his internship policy restricts non-Dutch master’s/bachelor’s students but welcomes advanced international PhD candidates. Collaborative work spans statisticians (Ly, de Heide, Koolen), machine learning researchers (Ramdas, Shafer), and medical scientists (van Werkhoven). As core member of CWI's Machine Learning group, he advances theoretical foundations with practical impact—evidenced by the first live deployment of e-values in a BCG vaccine meta-analysis. His work redefines statistical practice for adaptive data collection in clinical trials, AI, and social science research.

• Statistics
• Machine Learning
• Statistical Inference
• +۳ مورد دیگر

Erin C. Strumpf is affiliated with the Department of Economics at McGill University in Montréal, Canada. She is also associated with the Centre Interuniversitaire de Recherche en Analyse des Organisations (CIRANO) and the Centre Interuniversitaire de Recherche en Économie Quantitative (CIREQ) , both in Montréal. Her research primarily focuses on health economics, public policy, and the intersection of labor markets with health care systems. Key Research Areas: Health economics, public policy, primary care systems, Medicaid impacts on labor supply, maternal and infant health, econometric methods (particularly difference-in-differences and quasi-experimental designs). Notable Trends in Publications: Recent work examines breast cancer data reliability (2024), patient enrollment policies (2025), and the economic impacts of health policies on labor markets (2017, 2010). Earlier studies (2016–2018) analyze maternal leave, chronic disease in aging populations, and health disparities linked to socioeconomic factors. Labs/Teams: Collaborates with interdisciplinary teams at CIRANO and CIREQ, focusing on policy evaluation and health systems analysis. Her work often involves quasi-experimental methods and longitudinal data from OECD countries, Canada, and the U.S.

• Health Economics
• Public Policy
• Primary Care
• +۳ مورد دیگر

Filippo Gioachin is a Research Fellow at the Department of Sociology and Social Research, University of Trento, specializing in longitudinal analysis and causal inference. His work bridges social classes, income dynamics, and educational inequalities. PhD in Sociologia e Ricerca Sociale (2019-2024), Cum Laude M.A. in Sociology and Social Research (2017-2019), University of Trento B.A. in Sociology (2014-2017), University of Trento Erasmus exchange at University of Kent (2018) His research focuses on social stratification , intergenerational mobility , and life course inequalities , particularly analyzing the impact of institutional transformations on poverty risks and gendered economic disparities. Recent projects include Equality of Opportunity and Social Mobility (PRIN-funded), AGE-IT (PNRR-funded), and technological change’s influence on labor productivity and income inequality. Recognized for award-winning publications, Filippo has contributed to debates on gender inequality in domestic responsibilities during the pandemic, earning accolades from Premio Nicola Negri and Research in Social Stratification and Mobility. He actively participates in academic conferences (e.g., ECSR, RC28, PAA) and serves on the editorial board of Social Science Research . Co-supervised research on parental education and offspring wealth attainment in Europe Teaching roles include modules on educational inequality and quantitative methods Member of the Center for Social Inequality Studies (CSIS) and Life Course Research Laboratory (LC3)

• Social Stratification
• Intergenerational Mobility
• Education
• +۷ مورد دیگر