جست‌وجوی استاد و پژوهشگر

• Urban Economics
• Real Estate
• Public Economics
• +۵ مورد دیگر

Fernando Ferreira is the C.F. Koo Professor of Real Estate and Professor of Business Economics and Public Policy at The Wharton School of the University of Pennsylvania, where he also serves as Chair of the Real Estate Department. He has been a faculty member at Wharton since 2004, advancing from Assistant Professor to Associate Professor and currently holding his named professorship since 2019. He maintains significant research affiliations as a Research Associate at the National Bureau of Economic Research (NBER) since 2011 and a Faculty Fellow at the Penn Institute for Urban Research since 2009. His educational background includes: Ph.D. in Economics from the University of California, Berkeley (2004) M.A. in Economics from the Federal University of Rio Grande do Sul, Brazil (1999) B.A. in Economics from the State University of Maringa, Brazil (1996) Ferreira's research focuses on urban economics, real estate, and public economics, with particular expertise in housing market dynamics, racial disparities in homeownership, and urban policy. His work extensively examines how gentrification affects minority neighborhoods, racial differences in mortgage lending, and the vulnerability of minority homeowners during economic downturns. He has published numerous influential papers that employ sophisticated econometric methods to establish causal relationships in urban economic phenomena, contributing significantly to our understanding of housing market behavior and policy implications. His recent publications demonstrate a strong focus on housing market dynamics, with particular attention to racial and ethnic disparities. His work spans theoretical urban economics, empirical housing market analysis, and policy-relevant research on urban fiscal matters. He frequently collaborates with leading scholars including Patrick Bayer, Joseph Gyourko, and Stephen Ross, producing research that bridges academic rigor with practical policy relevance. His notable appointments include: Research Associate, National Bureau of Economic Research (2011-present) Faculty Fellow, Penn Institute for Urban Research (2009-present) Professor Ferreira teaches courses on Urban Fiscal Policy and Real Estate Investments at both undergraduate and graduate levels. His teaching addresses critical urban issues including housing affordability, city finances, infrastructure investment, and the impact of technology on urban development. His courses examine why cities exist, when fiscal policy fails, investments in infrastructure, school quality, and urban realities such as inequality, crime, corruption, high cost of living, congestion, and unfunded pensions, with special attention to recent topics like the impact of the Covid-19 pandemic on city finances. His research has significant implications for housing policy, urban development strategies, and efforts to address racial disparities in homeownership and wealth accumulation. His findings on racial disparities in mortgage lending and the vulnerability of minority homeowners during economic downturns provide valuable insights for policymakers, urban planners, and housing advocates, making his work highly relevant to contemporary housing market issues and public policy debates.

Marc Patrick Brag Klempمشاهده پروفایل

دانشیار

• Economic Growth
• Demographics
• Population Diversity
• +۸ مورد دیگر

Marc Patrick Brag Klemp is an Associate Professor at the Department of Economics, University of Copenhagen, with affiliations at Brown University's Population Studies and Training Center and the Center for Economic Policy Research (CEPR). He holds a PhD in Economics from 2013 and has contributed extensively to long-term economic development, demographics, and health. University of Copenhagen (2022-present): Associate Professor Brown University (2023-present): Visiting Researcher CEPR (2017-present): Research Affiliate Research Themes: Population Diversity: Investigates how ancient migration patterns shape modern economic, political, and social outcomes using genetic diversity metrics from human migration out of Africa. Demographic Dynamics: Studies fertility decisions, quantity-quality tradeoffs in children, and historical population control mechanisms using Quebec and English genealogies (1608-1800). Health Impacts: Collaborates on glaucoma diagnostics, brain responses to sensory stimulation, and male fertility effects from androgen abuse. Economic Transitions: Examines historical shocks, resource access, and market conditions in pre-industrial societies, challenging traditional Malthusian narratives. Publications & Collaborations: His work spans econometrics (Econometrica), ecology (Nature), health policy (PLOS ONE), and historical economics. Key collaborations include Oded Galor (Brown University) and medical researchers in Denmark.

Andrii Babiiمشاهده پروفایل

دانشیار

• Econometrics
• Causal Inference
• High-Dimensional Data Analysis
• +۲ مورد دیگر

Andrii Babii is an Associate Professor in the Department of Economics at the University of North Carolina. He holds a Ph.D. in Economics from the Toulouse School of Economics and specializes in econometrics with a focus on causal inference, high-dimensional data analysis, and machine learning. His research combines mathematical and statistical foundations with practical applications, and his work has been published in journals such as the Journal of Econometrics, Journal of Business & Economic Statistics, and Econometric Theory. He has extensive teaching experience in econometrics and statistics at both undergraduate and graduate levels. Andrii is the creator of the Pythia.jl package, an open-source Julia library for machine learning time series regressions, which implements methods like LASSO, group LASSO, and sparse group LASSO via proximal gradient descent.

Víctor Elviraمشاهده پروفایل

مدرس ارشد

• Bayesian Inference
• Sequential Monte Carlo Methods
• Importance Sampling
• +۵ مورد دیگر

Víctor Elvira is a Senior Lecturer at the University of Edinburgh, specializing in Bayesian inference, sequential Monte Carlo methods, and computational statistics. He serves as an Associate Editor for IEEE Transactions on Signal Processing and organizes major academic events like the Sequential Monte Carlo Workshop (SMC 2024) and Summer School in Bayesian Filtering (SSBF 2024) . Key research areas: Bayesian filtering, importance sampling, state-space modeling, and machine learning applications Recent work focuses on sparse graphical models, differentiable particle filters, and Monte Carlo optimization Scientific Awards : 2025 EURASIP Early Career Award 2024 ELLIS Fellow 2023 Leverhulme Research Fellow 2021 Alan Turing Fellow 2018 Fulbright & Marie Curie Fellow 2018 IEEE Senior Member He has delivered PhD courses on Monte Carlo methods and Bayesian filtering in Madrid (2025) and Barcelona (2025), and maintains open-access software implementations for publications like Sparse Graphical Linear Dynamical Systems and Graphical Inference in Linear-Gaussian State-Space Models .

Sridhar Mahadevanمشاهده پروفایل

استاد مدعو

• Machine Learning
• Artificial Intelligence
• Robotics
• +۷ مورد دیگر

Sridhar Mahadevan is an Adjunct Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. He has held previous academic positions at Michigan State University, the University of South Florida, and was a visiting scholar at Carnegie Mellon University. His research focuses on machine learning, multi-agent systems, planning, perception, robotics, and probabilistic models, with recent emphasis on hierarchical frameworks for autonomous learning and sequential decision-making using Markov decision processes. Mahadevan is an Associate Editor of Machine Learning and Journal of Machine Learning Research , and has contributed to editorial boards of major AI journals. He co-founded LookAhead Decisions Inc. and has been recognized with the NSF CAREER Award (1995) and best paper awards at international conferences. Education: PhD in Computer Science, Rutgers University, 1990 Research Interests: Mahadevan’s work spans foundational AI topics including reinforcement learning, causal inference, and generative models. His applied research includes robotics navigation, active vision systems, and spectroscopic analysis for space science. He pioneered methods for autonomous learning from perceptual data streams and developed frameworks like manifold alignment for cross-domain data integration. Key Contributions: Developed hierarchical hidden Markov processes and semi-Markov decision processes Advanced manifold learning techniques for spectroscopic calibration under non-stationary conditions Contributed to theoretical foundations of proximal reinforcement learning and variational inequalities Awards: NSF CAREER Award (1995) Best Student Paper Award at Autonomous Agents Conference (2001) Best Paper Award Runner-Up at International Conference on Machine Learning (1999) Advising & Grants: Advised students Rajbala Makar and Mohammad Ghavamzadeh. His NSF-funded projects include research on spectroscopic analysis and reinforcement learning theory. Collaborative grants include work on generative AI for Mars mineral analysis. Labs & Teams: Co-founder of LookAhead Decisions Inc., a company advancing decision-making systems using causal inference and reinforcement learning. Active in interdisciplinary teams applying AI to space science and robotics.

• Bioinformatics
• Genomics
• Machine Learning
• +۳ مورد دیگر

Wanwen Zeng is a Postdoctoral Scholar in the Department of Statistics at Stanford University, advised by Wing H Wong. Their research focuses on computational biology, integrating genomics, epigenomics, and machine learning to address problems in genetics and drug discovery. Key research interests include gene regulatory networks, epigenetic prediction using deep learning (e.g., transformers), and developing bioinformatics tools for analyzing single-cell data. Their work spans databases like HiChIPdb (regulatory interactions) and SilencerDB, alongside methodological advancements such as CREATE and scGraph. Recent publications highlight applications of graph neural networks in drug discovery (DeepDrug), causal modeling of genotype-phenotype relationships, and improving polygenic predictions through epigenomic features. This reflects a strong emphasis on bridging computational methods with biological systems. No scientific awards are listed. Advising and grants sections remain unspecified in available data. Their contributions are primarily technical, centered on advancing genomic data analysis frameworks and databases.

Trevor Hastieمشاهده پروفایل

استاد

• Statistical Modeling
• Bioinformatics
• Machine Learning
• +۳ مورد دیگر

Trevor Hastie is the John A. Overdeck Professor of Statistics at Stanford University, with a joint appointment in the Department of Statistics and the Department of Biomedical Data Science in the Stanford School of Medicine. His research focuses on statistical modeling, bioinformatics, and machine learning. He has authored six books, including influential works like Statistical Learning with Applications in Python and Computer Age Statistical Inference , and has published over 200 research articles. Prior to Stanford, he worked at AT&T Bell Laboratories (1985–1994), contributing to the development of the R programming environment. He holds a B.Sc. (Hons) from Rhodes University (1976), an M.Sc. from the University of Cape Town (1979), and a Ph.D. from Stanford University (1984). Key contributions include the glmnet package for regularization paths in generalized linear models and the softImpute algorithm for matrix completion. His work has been recognized with prestigious awards, including election to the U.S. National Academy of Sciences (2018) and the 2025 C.R. and Bhargavi Rao Prize. Hastie collaborates extensively, co-authoring seminal papers and developing open-source software tools for statistical computing. His research spans computational statistics, with applications in biomedicine, genomics, and large-scale data analysis. Current projects include developing scalable algorithms for high-dimensional data and advancing methods for causal inference and genomic studies. He is also actively involved in education, teaching courses on statistical learning and contributing to online learning platforms.

Jean-François Planteمشاهده پروفایل

استاد

• Statistical Inference
• Statistical Learning
• Sports Analytics
• +۳ مورد دیگر

Jean-François Plante is a Professor at the Department of Decision Sciences, HEC Montréal. He holds roles such as Member of the Group for Research in Decision Analysis (GERAD), Professorship in Pedagogical Innovation in Gamification for Data Science, and Associate Director of Pôle sports Education. His research focuses on Statistical Inference, Statistical Learning, Sports Analytics, Applied Artificial Intelligence, and Dependence Models (copulas). He actively supervises Master’s theses and projects, contributing to over 40 academic works in the last five years. Education: B.Sc. in Mathematics from Université Laval, M.Sc. and Ph.D. in Statistics from University of British Columbia. Research interests emphasize statistical methodologies applied to diverse fields including sports analytics, environmental modeling, and machine learning optimization. His recent publications explore topics like spatial interpolation in mining, classification tree capabilities, and migrant labor dynamics in professional sports. He has advised numerous students on projects ranging from fraud detection to AI-enhanced chatbots for autism support. Plante teaches courses such as Statistical Learning and Multidimensional Analysis, reflecting his expertise in statistical education and pedagogical innovation. His work bridges academia and industry through collaborations with organizations like CDPQ and Mouvement Desjardins.

Tarek Alexandar Hassanمشاهده پروفایل

دانشیار مهمان

• International Finance
• Macroeconomics
• Economic Growth
• +۳ مورد دیگر

Tarek Alexandar Hassan is a Visiting Associate Professor of Economics at Boston University. His research focuses on international finance, macro-finance, and economic growth, with a particular emphasis on the impact of uncertainty, migration, and ethnic diversity on economic outcomes. He holds research fellowships at the National Bureau of Economic Research (NBER) and the Center for Economic Policy Research (CEPR), and has taught at the University of Chicago, Harvard University, and held visiting positions at Princeton, Stanford, London Business School, and the London School of Economics. His work examines how political and economic uncertainties influence firm behavior, capital allocation, and global financial markets. Key research areas include the transmission of country risk, the effects of immigration on innovation and growth, and the role of social networks in shaping investment patterns. Recent studies address the global economic impact of Brexit, pandemic-related firm vulnerabilities, and the diffusion of new technologies. Dr. Hassan has been recognized for his contributions with awards such as the Austrian Central Bank’s Klaus Liebscher Award and the Leo Melamed Prize for Outstanding Research in Finance. His interdisciplinary approach bridges macroeconomics, finance, and political economy, offering insights into policy design and market dynamics under uncertainty.

Mary D. Willisمشاهده پروفایل

استادیار

• Environmental Epidemiology
• Spatial Exposure Assessment
• Applied Data Science
• +۶ مورد دیگر

Mary D. Willis is an Assistant Professor in the Department of Epidemiology at the Boston University School of Public Health , with core faculty affiliation at the Institute for Global Sustainability . Her research integrates environmental epidemiology , spatial exposure assessment , and econometric-based causal inference to analyze energy sector and built environment impacts on reproductive health . Education: PhD in Environmental & Occupational Health, Oregon State University MPH in Epidemiology, University of Rochester BA in Epidemiology & Environmental Studies, University of Rochester Research Focus: Dr. Willis examines how oil and gas development , traffic-related air pollution , and neighborhood characteristics affect fertility , pregnancy outcomes , and mental health , with emphasis on policy-relevant frameworks . Recent work includes a 20-year Texas cohort study on traffic pollution disparities and a 2025 high-resolution Boston study on transportation emissions. Scientific Awards: NIH Director’s Early Independence Award (2022) International Society for Environmental Epidemiology Travel Awards Warren & Frederica Schad Fellowships Best Oral Presentation Awards (Cascadia, CARTEEH) Grants: PI of NIH-funded Preconception Cohort Study on Oil and Gas Development (2022-2027), co-investigator on Health Effects Institute TRANSIT Accountability Study of vehicle emission regulations. Her work spans climate change , energy equity , and urban health interventions.

• Applied Microeconomics
• Microeconometrics
• Labor Economics
• +۸ مورد دیگر

Mattia Albertini is a Post-Doctoral Researcher at the Institute for Economic Research (IRE) within the Faculty of Economics at Università della Svizzera italiana (USI). He previously completed his Ph.D. at the Institute of Economics (IdEP), USI, and participated in the Swiss Program for Beginning Doctoral Students in Economics. He holds a Master of Science in Economics with a minor in Data Science from USI and a Bachelor in Economics from the University of Pavia. Bachelor in Economics, University of Pavia Master of Science in Economics (minor in Data Science), Università della Svizzera italiana Ph.D. in Economics, Università della Svizzera italiana Swiss Program for Beginning Doctoral Students in Economics, Swiss National Bank His research focuses on applied microeconometrics with applications in health, labor, public, urban, and environmental economics. A recurring theme in his work is the spatial dimension of economic outcomes, including residential integration, real estate market dynamics, and geographic proximity to borders or hazards. He integrates data science techniques, particularly programming in Python and STATA, to develop tools for empirical analysis and reproducible research. The collection of articles reflects a strong trend in applied econometrics, causal inference, and spatial analysis. His work spans health economics (e.g., benzodiazepine prescribing), labor and urban economics (e.g., migration, job accessibility), and environmental and insurance economics (e.g., natural hazard impacts). Methodologically, he engages with difference-in-differences, event study designs, spatial modeling, and text analysis, often building or applying computational tools to address research questions. He has contributed to teaching at both the Bachelor's and Master's levels, particularly in microeconomics, macroeconomics, and data-intensive courses such as Textual Analysis and Spatial Data for Economists. Teaching Assistant, Microeconomics A (Bachelor) Teaching Assistant, Macroeconomics B (Bachelor) Teaching Assistant, Textual Analysis and Spatial Data for Economists (Master) Mattia Albertini is actively involved in open science, sharing code for econometric methods, data visualization, and application development (e.g., a gluten-checking app) on GitHub. His interdisciplinary approach combines economic theory, statistical modeling, and computational tools to investigate real-world policy-relevant questions.

Felix Elwertمشاهده پروفایل

استاد

• Social Inequality
• Causal Inference
• Social Demography
• +۳ مورد دیگر

Felix Elwert is the Vilas Distinguished Achievement Professor of Sociology at the University of Wisconsin-Madison, with a joint appointment in Biostatistics. He specializes in causal inference methodologies and investigates social inequality, demography, and health disparities through large-scale experiments, population registers, and survey analyses. His work bridges sociology, statistics, and public health, with notable contributions to understanding peer effects, neighborhood effects, and methodological innovations in causal analysis. Elwert holds a Ph.D. in Sociology (2007) and M.A. in Statistics (2006) from Harvard University. Before his current position, he served as Karl W. Deutsch Professor and Acting Director of Social Inequality and Social Policy at the WZB Berlin Social Center (2014–2016). He currently leads the Sociological Methods & Research journal as Editor-in-Chief. His research focuses on: Contextual drivers of inequality in income, education, and health Causal mediation analysis Experimental designs for estimating peer effects Bias detection in statistical models Elwert has received prestigious awards including the ASA's Causality in Statistics Education Award (first recipient) and the ASA's Leo Goodman Award. His collaborations span institutions globally, emphasizing interdisciplinary approaches to causal questions. He teaches advanced causal inference courses at UW-Madison and international universities, covering topics like graphical models, mediation analysis, and experimental design. His campus affiliations include the Center for Demography and Ecology, Center for Demography of Health and Aging, and Institute for Diversity Science.

• Computational Neuroscience
• Perception
• Decision Making
• +۲ مورد دیگر

Ulrik Beierholm is an Associate Professor in the Department of Psychology at Durham University, serving as Department Representative and Fellow of the Durham Research Methods Centre, and holding an Associate Professor position on the Executive Board of the Biophysical Sciences Institute. His research employs computational frameworks to investigate uncertainty processing in perception, decision making, and learning through interdisciplinary methodologies. His core research spans Computational Neuroscience, Perception, Decision Making, Neuroeconomics, and Machine Learning, with emphasis on Bayesian inference and reinforcement learning models validated via psychophysics, fMRI, and pharmacological techniques. Recent work demonstrates how the nervous system optimally handles sensory uncertainty across multisensory contexts, developmental stages, and cognitive tasks. Analysis of his publication trajectory reveals consistent innovation in Bayesian modeling of perceptual processes, with growing emphasis on computational tools development, neuroeconomics applications, and educational initiatives. His work bridges theoretical neuroscience with experimental validation across diverse cognitive domains. Honors include: Facebook Faculty Award for Virtual Reality (2016) Associate Editor for PLoS Computational Biology Editor for Bayesian methods section in Springer's Encyclopedia of Computational Neuroscience Dr. Beierholm supervises postgraduate researchers including Denise Foresteire and has co-organized influential workshops such as the Durham Computational Biology Symposium (2018) and Probabilistic Brain Workshop (2018), establishing himself as a key collaborator in computational neuroscience communities. His dual affiliations with the Biophysical Sciences Institute and Durham Research Methods Centre facilitate cross-disciplinary integration of psychological theory, neural data, and advanced computational modeling to address fundamental questions about human cognition.