جست‌وجوی استاد و پژوهشگر

Gabriel Lopez-Moctezumaمشاهده پروفایل

استادیار

• Political Economy
• Political Science
• Collective Decision-Making
• +۵ مورد دیگر

Gabriel Lopez-Moctezuma is an Assistant Professor of Political Science at the California Institute of Technology (Caltech) , joining the faculty in 2017. He holds a B.A. from Instituto Tecnologico Automomo de Mexico (2007), an M.A. (2012), and a Ph.D. (2016) from Princeton University. Caltech affiliations: Ronald and Maxine Linde Institute of Economic and Management Sciences Center for Theoretical and Experimental Social Sciences (CTESS) His research focuses on collective decision-making in committees (e.g., Federal Open Market Committee, U.S. Supreme Court) and electoral competition . Methodologically, he employs structural estimation , causal inference , and text analysis to study how sequential decision-making affects policy outcomes and voter behavior. Key contributions include analyzing social learning in judicial deliberations and quantifying the trade-off between ideological preferences and reelection incentives in U.S. Senate elections. Teaching roles include courses on Political Theory (SS 202 abc) and Applied Empirical Methods in the Social Sciences (SS 228 abc). Publications span topics from monetary policy dynamics to democratic representation , with ongoing work on political advertising effects and deliberation mechanisms in electoral systems.

Fu Ouyangمشاهده پروفایل

مدرس ارشد

• Econometrics
• Applied Economics
• Panel Data Analysis
• +۵ مورد دیگر

Dr. Fu Ouyang is a Senior Lecturer (equivalent to Assistant Professor) in the School of Economics at the University of Queensland (UQ), located in Brisbane, Australia. He holds a Ph.D. in Economics from Duke University (2017) and has been affiliated with UQ since 2018. His research focuses on theoretical and applied econometrics, with expertise in semiparametric/nonparametric methods, panel data analysis, causal inference, and discrete choice models. He applies these methodologies to fields like labor, health, and industrial organization economics. Dr. Ouyang is available for academic supervision and actively publishes in top-tier econometrics journals. Education: Ph.D. in Economics, Duke University, 2017 Research Interests: Development of robust econometric methods for causal inference and high-dimensional settings Analysis of longitudinal/panel data, binary choice models, and limited dependent variables Applications in empirical industrial organization, labor, and health economics Recent Research Trends: His work emphasizes semiparametric estimation techniques, dynamic panel models with lagged dependencies, and bundle choice analysis. Notable contributions include addressing heteroskedasticity in high-dimensional binary choice models and improving inference in multinomial response frameworks. Supervision & Grants: Dr. Ouyang is available to supervise graduate students but no specific grants are listed in the provided texts. His contact details include office room 532A in the Colin Clark Building and professional social media profiles (LinkedIn).

Natesh Pillaiمشاهده پروفایل

استاد

• Applied Probability
• Computational Methods
• Markov Chain Monte Carlo (MCMC)
• +۵ مورد دیگر

Natesh Pillai is a Professor in the Department of Statistics at Harvard University and a Distinguished Engineer at LinkedIn, focusing on Responsible AI. He holds a Bachelors from IIT Madras, a PhD from Duke University's Department of Statistical Science (2008), and completed a postdoc at the University of Warwick's CRiSM (2008-2010). His research spans applied probability, computational methods, MCMC theory, algorithmic fairness, and climate science. He serves on editorial boards for journals like SIAM Journal on Mathematics of Data Science and Harvard Data Science Review . Key awards include the 2018 Young Statistical Scientist Award and 2021 Fellowship in the Institute of Mathematical Statistics. His work emphasizes bridging theory and practice, with contributions to statistical methodology, causal inference, and scalable computational techniques. Recent collaborations include industry roles at Amazon (2021-2023) and interdisciplinary climate science projects analyzing agricultural yield predictability. Education: Bachelor's: Indian Institute of Technology (IIT) Madras PhD: Duke University, Department of Statistical Science Research Focus: MCMC mixing times, Bayesian methodology, reinforcement learning, climate data modeling Publications emphasize algorithmic efficiency, fairness in AI, and probabilistic frameworks for complex systems. His lab integrates theoretical rigor with real-world applications, including climate modeling and healthcare analytics.

Hangfeng Heمشاهده پروفایل

استادیار

• Machine Learning
• Natural Language Processing
• Reasoning
• +۴ مورد دیگر

Hangfeng He is an Assistant Professor of Computer Science and Data Science at the University of Rochester , affiliated with the Hajim School of Engineering & Applied Sciences. He holds a PhD from the University of Pennsylvania and focuses on machine learning and natural language processing. His research emphasizes incidental supervision for natural language understanding, interpretability of deep neural networks, and reasoning in natural language. Education : PhD in Computer Science from University of Pennsylvania Research Interests : Machine Learning (especially deep learning theory, generalization, and optimization) Natural Language Processing (incidental supervision, language understanding, and multimodal reasoning) Reasoning (inductive biases, temporal data analysis, and model interpretability) Research Trends : His work spans foundational AI research and applied NLP, with recent focus on large language models, multimodal systems, and constraint-aware methods. He explores how models generalize from limited supervision and mitigate biases in real-world data. Key themes include improving model robustness, understanding inductive mechanisms, and applying AI to financial and social text analysis. Labs/Teams : While specific lab affiliations aren’t listed, his work aligns with the Department of Computer Science’s research clusters in AI, machine learning, and data science.

Joseph G. Altonjiمشاهده پروفایل

استاد

• Labor Economics
• Applied Econometrics
• Labor Market Fluctuations
• +۱۰ مورد دیگر

Joseph G. Altonji is the Thomas DeWitt Cuyler Professor of Economics at Yale University and a Research Associate at the National Bureau of Economic Research (NBER). He previously held faculty positions at Columbia University and Northwestern University, and served as a visiting professor at Princeton and Harvard. His academic affiliations include memberships in the Econometric Society, the American Academy of Arts and Sciences, and the Society of Labor Economists (past president). He received the IZA Prize in Labor Economics (2018) and has advised numerous federal and academic bodies, including the Federal Reserve Bank of Chicago and the President’s Council of Advisors on Science and Technology. Altonji’s research focuses on labor economics, applied econometrics, and inequality dynamics. Key areas include labor market fluctuations, education economics, family income dynamics, and wage determination. Current projects address school choice impacts on inequality, graduate degree returns, and the interplay between marriage, earnings, and family income. His methodological contributions include techniques to address selection bias in observational studies, particularly in evaluating school and neighborhood effects. Publications highlight trends in advanced degree returns, decomposition of earnings into wage and hours effects, and analyses of pandemic-era labor market shifts. His work often bridges econometric rigor with policy relevance, emphasizing empirical strategies to isolate causal effects. Altonji’s advisory roles reflect his engagement with real-world economic challenges, from STEM education to unemployment insurance policy design.

• Disease Ecology
• Spatial Epidemiology
• GIS in Public Health
• +۴ مورد دیگر

Michael E. Emch is a W.R. Kenan, Jr. Distinguished Professor of Geography and Epidemiology at the University of North Carolina at Chapel Hill (UNC-Chapel Hill). He holds a PhD in Medical Geography from Michigan State University (1998) and has been a Fellow at the Carolina Population Center. His research integrates spatial analysis with epidemiological and ecological theories to study infectious diseases in developing regions, focusing on disease ecology, spatial epidemiology, and GIS applications in public health. He directs the Spatial Health Research Group and is affiliated with the UNC Infectious Diseases, Epidemiology, and Ecology Lab (IDEEL). His research explores spatio-temporal patterns of diseases like malaria, hepatitis B, and influenza, emphasizing how social, environmental, and built factors influence disease spread. Notable projects include studies on swine influenza evolution, malaria vaccination efficacy (RTS,S/AS01), and hepatitis B elimination strategies in the Democratic Republic of Congo. He also investigates environmental health issues, such as groundwater contamination and cholera transmission in Bangladesh. Education: PhD in Medical Geography (1998, Michigan State University), MA in Geography (1992, Miami University of Ohio), BA in Biology (1987, Alfred University). Service: Editorial roles in journals like Health & Place and SSM - Population Health . Awards: W.R. Kenan, Jr. Distinguished Professorship, Fellow of the Carolina Population Center. Teaching focuses on spatial components of health, including courses like GIS in Public Health and Infectious Disease Ecology . Recent articles highlight cutting-edge work on machine learning in environmental health, vaccine efficacy analysis, and spatial epidemiology of emerging pathogens. His advocacy for interdisciplinary approaches bridges geography, epidemiology, and ecology to address global health challenges.

Didong Liمشاهده پروفایل

استادیار

• Biostatistics
• Machine Learning
• Geometric Data Analysis
• +۷ مورد دیگر

Dr. Didong Li is an Assistant Professor in the Department of Biostatistics at the Gillings School of Global Public Health , University of North Carolina at Chapel Hill. His research focuses on statistical methods for complex data, including manifold learning, nonparametric Bayesian inference, and spatial statistics, with applications in healthcare, genomics, and environmental health. He holds a PhD in Mathematics from Duke University (2020), and degrees from Beijing Institute of Technology (BS 2012, MS 2015). His research interests include dimension reduction, Gaussian processes, geometric data analysis, and network analysis. He has developed novel methods for analyzing electronic health records, single-cell RNA sequencing data, and spatial omics datasets. Notable contributions include the spherelet manifold approximation framework and the STimage-1K4M dataset. Dr. Li has received the Inaugural IMS Lawrence D. Brown PhD Student Award (2019) . He serves on the editorial board of the Journal of Machine Learning Research and organized sessions at JSM 2021-2022 on geometric Bayesian methods. His lab supports interdisciplinary collaborations, with ongoing projects in spatial transcriptomics, healthcare access analysis, and AI-driven medical informatics. Advising highlights include guiding students through successful PhD defenses (e.g., Dr. Sam Hawke, Dr. Jiawen Chen) and postdoctoral placements. His team secured a NVIDIA Academic Grant (2024) for multi-modal AI in spatial transcriptomics. The Li Lab maintains active partnerships with institutions worldwide, advancing both statistical theory and real-world applications.

• Child Health and Development
• Epidemiology
• Public Health Surveillance
• +۳ مورد دیگر

Associate Professor Catherine Chittleborough is an epidemiologist and Deputy Head of the School of Public Health at the University of Adelaide, affiliated with the BetterStart Health and Development Research initiative. She holds a joint appointment in the Faculty of Health and Medical Sciences. Previously, she worked at SA Health for a decade and completed a postdoctoral position at the University of Bristol. Her research focuses on reducing health inequalities in child development, leveraging causal epidemiological methods and population data from longitudinal cohorts. She teaches undergraduate public health and epidemiology courses. Education: Postdoctoral training at the University of Bristol (2009-2011) Extensive prior experience at SA Health (1990s-2010) Research Interests: Child health equity, causal epidemiology, data linkage studies, early childhood development, and interventions targeting family support programs. Her work emphasizes identifying at-risk children to improve preventive strategies. Grants & Funding: EMPOWER Project (NHMRC AUD 2.4M, 2016-2020) SA Health Data Linkage Studies (NHMRC AUD 453K, 2013-2016) Child Maltreatment and School Outcomes (Channel 7 AUD 69K, 2016-2018) Teaching: Coordinator for PUB HLTH 1001, HLTH SC 3103, and other public health modules. Active in curriculum design and delivery across undergraduate programs. Professional Activities: Chair, School of Public Health Learning and Teaching Committee Member of multiple advisory boards and review panels Labs/Teams: Lead researcher in BetterStart Health and Development Research, collaborating on interdisciplinary projects to improve child health outcomes through policy and practice.

Anders Skrondalمشاهده پروفایل

استاد

• Psychometrics
• Statistics
• Biostatistics
• +۳ مورد دیگر

Anders Skrondal is a Professor II at the University of Oslo's Faculty of Educational Sciences, affiliated with the Centre for Educational Measurement (CEMO). He also serves as a Senior Scientist at CEFH (Research Council of Norway Centre of Excellence) at the Norwegian Institute of Public Health and Co-Principal Investigator at CREATE, another Norwegian Centre of Excellence. His academic journey includes roles as Head of the Biostatistics Group at the Norwegian Institute of Public Health and Professor of Statistics at the London School of Economics (LSE), where he directed the Methodology Institute. Skrondal's research focuses on psychometrics, statistics, biostatistics, and econometrics, with a major contribution being the development of the GLLAMM framework. He has authored 14 books and over 200 peer-reviewed papers, achieving an h-index of 63 and 30,000+ citations. His awards include the 1997 Psychometric Society Dissertation Prize and leadership roles in prestigious organizations like the Psychometric Society and Royal Statistical Society. Research Interests: Skrondal specializes in statistical methodologies including latent variable modeling, multilevel modeling, and missing data analysis. His work bridges theoretical advancements and practical applications in medicine, psychology, and social sciences. He is renowned for integrating latent variable and mixed model frameworks to address complex data structures. Recent trends in his publications emphasize methodological solutions for missing data, non-ignorable mechanisms, and psychometric model validation. His articles span statistical theory, medical applications, and educational measurement. Awards: President, Psychometric Society (2016–2017) Elected Member, International Statistical Institute Outstanding Academic Title for 'The Cambridge Dictionary of Statistics' (2011) Fulbright Professor at UC Berkeley (2013–2014) Advising & Grants: Skrondal has led major research initiatives such as CEFH and CREATE, funded by the Research Council of Norway. While no specific advisee list is provided, his collaborations span international institutions. His work on GLLAMM software is used in over 750 journals, reflecting widespread academic impact. Labs/Teams: Active in CEMO and CEFH, he contributes to interdisciplinary teams advancing educational measurement and public health research. His involvement in CREATE focuses on equality in education through statistical innovations.

Christian Schunnمشاهده پروفایل

استاد

• Scientific Reasoning and Discovery Learning
• Computational Modeling of Strategy Choice
• Categorization and Causal Reasoning
• +۷ مورد دیگر

Christian Schunn is a Professor in the Department of Psychology at the University of Pittsburgh, with a focus on Learning Sciences and cognitive processes. He holds a PhD from Carnegie Mellon University. His research spans scientific reasoning, peer assessment, STEM education, and computational modeling. He leads a lab with active members including Liwei Chen and Jie Cao (PhD students) and former advisees such as Yaron Doppelt and Matt Mehalik. Key research interests include the efficacy of peer feedback in education, design-based learning in engineering, and cognitive strategies in STEM. He has contributed to over 150 publications, emphasizing peer assessment systems like Peerceptiv and their impact on learning outcomes. His work often bridges cognitive science and educational practice, addressing equity in STEM pathways and motivational factors in student performance. Lab activities include studies on team dynamics in design, computational thinking, and the role of analogical reasoning. He collaborates internationally with institutions like Northeast Normal University and the Harbin Institute of Technology. Current grants focus on improving peer feedback mechanisms and their scalability in diverse educational settings. Awards include recognition for contributions to educational technology and learning analytics. His lab is part of the Learning Research and Development Center (LRDC), fostering interdisciplinary research in cognitive and educational sciences.

Bertram F. Malleمشاهده پروفایل

استاد

• Moral Psychology
• Social Cognition
• Human-Machine Interaction
• +۳ مورد دیگر

Bertram F. Malle is a Professor of Cognitive and Psychological Sciences at Brown University, holding a joint appointment in the Department of Cognitive and Psychological Sciences. He is affiliated with the Humanity-Centered Robotics Initiative and has collaborated with institutions like the Army Research Laboratory and the Office of Naval Research. His academic journey includes a Master’s in Philosophy/Linguistics (1987) and Psychology (1989) from the University of Graz, Austria, followed by a Ph.D. in Psychology from Stanford University (1995). Before joining Brown in 2008, he was at the University of Oregon. His research focuses on moral psychology, social cognition, and human-robot interaction. Key interests include moral judgments, blame attribution, social norms, and the cognitive foundations of human-machine collaboration. He has been funded by NSF, DARPA, and the Department of Defense, among others. Malle’s work bridges psychology, philosophy, and AI, emphasizing moral competence in robots and the societal implications of human-robot interaction. His awards include the SESP Outstanding Dissertation Award (1995), NSF CAREER Award, and the 2019 SESP Scientific Impact Award. He is also a Fellow of multiple prestigious societies, including APS and SPSP. His teaching spans courses like Social Psychology, Moral Psychology, and Multivariate Statistics. Collaborators include experts in computer science and robotics, reflecting his interdisciplinary approach to advancing human-centric AI systems.

Brent A. Johnsonمشاهده پروفایل

استاد

• Biostatistics
• Semi-parametric methods
• Missing data analysis
• +۹ مورد دیگر

Brent A. Johnson is a Professor of Biostatistics, Ophthalmology, and Neurology at the University of Rochester Medical Center. He holds joint appointments in the School of Medicine and Dentistry and leads the Biostatistics and Computational Biology Department. His research focuses on methodological advancements in semi-parametric methods, causal inference, and adaptive treatment strategies, with substantive applications in HIV/AIDS, environmental health, and clinical epidemiology. Johnson collaborates widely across disciplines, including cardiology, neurology, and global health, particularly in Sub-Saharan Africa. He directs the Biostatistics Core for the University of Rochester's Center for AIDS Research and serves as an on-call statistician for the Flaum Eye Institute. Education: PhD in Statistics from North Carolina State University (2003), MS in Biostatistics from University of Minnesota (1997), BA in Mathematics from St. Olaf College (1995). Awards include the NSF VIGRE Fellowship and Best Student Paper Award from the American Statistical Association. Research Interests: Dynamic treatment regimes, personalized medicine, missing data methods, biomarker development, and HIV prevention strategies. His lab develops statistical tools for observational studies and clinical trials, with recent work on machine learning approaches for censored outcomes and causal inference. Grants and Funding: NIH grants for HIV/AIDS research, environmental health projects, and methodological development. Collaborations include the AIDS Clinical Trials Group (ACTG), the South African TDF study, and the El Paso Asthma Cohort. Labs/Teams: Directs the Brent Johnson Lab, focusing on statistical methodology for biomedical applications. Key lab members include postdoctoral fellows like John Rice and alumni Li Li (Eli Lilly) and Xin Lu (Sanofi).

Steve Gibbonsمشاهده پروفایل

استاد

• Economic Geography
• Urban Economics
• Transport Infrastructure
• +۵ مورد دیگر

Professor Steve Gibbons is the Professor of Economic Geography at the Department of Geography and Environment, London School of Economics. He served as Head of Department from 2019-2023. His research focuses on spatial dimensions of education, transport infrastructure, environmental amenities, housing markets, labor markets, health, and crime, emphasizing causal analysis and policy impact. He collaborates with government bodies like the Department for Transport and the National Infrastructure Commission. His teaching includes urban economics, GIS, and spatial analysis methods. He co-edits the Journal of Economic Geography. Key research areas include transport connectivity impacts and valuation of rail access. His work bridges economic theory with applied policy analysis, addressing geographical disparities and economic performance. Recent studies explore the social costs of infrastructure like power lines and mental health interventions. He actively engages with policymakers to translate academic findings into actionable strategies. Professor Gibbons has advised on numerous transport and urban development projects, contributing to infrastructure policy evaluations. His research highlights the interplay between spatial factors and socioeconomic outcomes, particularly in urban contexts.

• Construction Engineering
• Simulation Science
• Information Technology in Construction
• +۳ مورد دیگر

Simaan Abourizk, PhD, PEng, is the Dean of the Faculty of Engineering at the University of Alberta and holds the rank of Professor in Construction Engineering and Management. He also served as the NSERC/Alberta Construction Industry Research Chair (1997–2011) and Canada Research Chair in Operations Simulation (2001–2008). His roles include Executive Board membership at the Construction Research Institute of Canada. Education: Doctor of Philosophy in Construction Engineering and Management from Purdue University (1990), Master of Science and Bachelor of Civil Engineering (Honors) from Georgia Institute of Technology (1985 and 1984). Research focuses on advancing simulation technologies for construction and natural resource industries, including: Development of the Simphony simulation environment and COSYE framework Integration of AI, visualization, and scheduling tools (e.g., SmartEst, MTRACK) Optimization of tunneling, modular assembly, and industrial fabrication processes Key awards include the E. Whitman Wright Award (2002), E.W.R. Steacie Memorial Fellowship (2001), and Walter Shanly Award (2001). Current projects emphasize synthetic environments, safety analytics, and data-driven decision support systems in construction. He leads initiatives on risk simulation, resource allocation, and industrial automation.

Laura Acostaمشاهده پروفایل

استادیار

• Political Sociology
• Cultural Sociology
• Civil War
• +۸ مورد دیگر

Laura Acosta is an Assistant Professor in the Department of Sociology within the School of Social Sciences at the University of California, San Diego. Her research focuses on civil war, political deception, and the cultural barriers to social change in Latin America, particularly in Colombia and Mexico. As a cultural and political sociologist, she explores the contradictions between public beliefs and material realities in conflict-affected societies. Her work combines historical, quantitative, and ethnographic methods to analyze how political leaders fabricate threats and how such narratives become self-fulfilling prophecies that deepen social divisions. Her research has been published in leading journals such as Theory and Society , Social Forces , and Quality & Quantity . The publications reveal a strong thematic focus on political culture, conflict dynamics, and methodological rigor across qualitative and quantitative paradigms. Scientific Awards: Awards and honors from the American Sociological Association Laura Acosta mentors graduate students and conducts interdisciplinary research on conflict and social change. Her book project, The Enemy Foretold , represents a significant contribution to understanding the cultural roots of civil war. She previously held postdoctoral and teaching positions at Brown University and Trinity College Dublin.