جست‌وجوی استاد و پژوهشگر

Clément De Chaisemartinمشاهده پروفایل

استاد

• Econometrics
• Economics of Education
• Policy Evaluation
• +۳ مورد دیگر

Clément De Chaisemartin is a Professor of Economics at Sciences Po since 2021, affiliated with the National Bureau of Economic Research (NBER) and J-PAL. His work focuses on econometric methods for policy evaluation and the economics of education. He holds a PhD from EHESS and Paris School of Economics (PSE), and previously taught at the University of Warwick and University of California, Santa Barbara (UCSB). Key research includes the evaluation of educational interventions, such as boarding schools for disadvantaged students, and methodological contributions to difference-in-differences (DID) estimators. Notable awards include the AFSE-Malinvaud Prize (2019) for his work on fuzzy DID. He co-authored influential papers in American Economic Review , Econometrica , and Review of Economic Studies , and developed Stata/R packages like did_multiplegt and twowayfeweights . Current projects include a textbook on DID methods and collaborations on multi-site randomized trials. Education: PhD in Economics (2013), École des Hautes Études en Sciences Sociales (EHESS) and Paris School of Economics (PSE). Research Interests: Causal inference, policy evaluation, educational disparities, and econometric methods. Grants & Projects: Research funded by French health/education authorities, including evaluations of early childhood programs like Parler Bambin. Teams: Maintains a research team and GitHub page for open-source econometric tools. His op-eds advocate for broader use of impact evaluation in public policy, critiquing private-sector dominance in applying such tools.

• Artificial Intelligence
• Neuroscience
• Cognitive Sciences
• +۴ مورد دیگر

Dr. Pedro Mediano is a Lecturer in Computing at Imperial College London's Department of Computing (Faculty of Engineering). His research focuses on complex systems, information theory, and their applications in neuroscience, artificial intelligence, and cognitive science. He is affiliated with the Artificial Intelligence Network and leads interdisciplinary projects exploring synergistic interactions in brain dynamics, psychedelic neurodynamics, and causal emergence. Key research areas include quantifying high-order interactions in complex systems, developing information-theoretic tools for analyzing neural data, and modeling consciousness through integrated information theory. Mediano has pioneered frameworks like the Shannon invariants for scalable information decomposition and developed software tools such as THOI for analyzing higher-order interactions. Recent work examines how psychedelics alter brain entropy, the role of metastability in cognitive processes, and the computational principles underlying causal emergence in machine learning models. His studies integrate mathematical rigor with empirical neuroscience, bridging theoretical and applied domains. Mediano has collaborated on whole-brain models of psychedelic-induced neural complexity and explored the interplay between oxygen metabolism and brain evolution. He holds affiliations with Imperial's AI Network and regularly publishes in top journals across computational neuroscience and complexity science. Current projects include developing open-source tools for information decomposition and investigating the neural correlates of consciousness under altered states.

Andrea Ichinoمشاهده پروفایل

استاد

• Labour Economics
• Economics of Education
• Economics of the Family
• +۷ مورد دیگر

Andrea Ichino is a Full-time Professor in the Department of Economics at the European University Institute (EUI) in Florence, Italy. He previously held positions at the University of Bologna, Bocconi University, and the EUI, where he was a Jean Monnet Fellow. His research focuses on Labour Economics, Education Economics, Family Economics, and Microeconometrics. His work addresses topics like intergenerational mobility, gender norms, judicial efficiency, and policy impacts on education and labour markets. Affiliations: EUI, University of Bologna (on leave), Research Fellowships at CEPR, IZA, and CESifo. Education: PhD in Economics from MIT (1990), Laurea in Economics from Bocconi University (1985). Research Interests: Ichino examines socio-economic policies, including childcare effects, taxation, and education systems. His recent work analyzes cognitive impacts of daycare, regional wage disparities, and pandemic economic strategies. He emphasizes causal inference and experimental methods in his analyses. Publications: Over 40 peer-reviewed articles in top journals like the Journal of Political Economy, American Economic Review, and Journal of Labor Economics. Recent work includes studies on multi-cutoff RD designs and pandemic policy implications. Awards: EALE Fellowship, CEPR Research Fellowship, and grants from the Unicredit Foundation for education research. Advising & Grants: Supervised over 40 PhD students across institutions. Received grants for projects on education policy, judicial productivity, and intergenerational mobility. Active in policy discussions on tax systems, wage equality, and public sector reforms. Labs/Teams: Collaborates with research clusters at EUI on topics like civic engagement and judicial efficiency. Leads projects on educational tracking systems and labour market dynamics.

• Survival Analysis
• Nonparametric Regression
• Semiparametric Regression
• +۴ مورد دیگر

Ingrid Van Keilegom is a Full Professor at the Faculty of Economics and Business (FEB) at KU Leuven. She is a member of the LISS – KU Leuven Institute of Sports Science and serves as a program director for the POC Master in Statistics. Senior academic staff member of FEB Council and Campus Council Observer in Faculty of Science Council Member of LStat General Assembly Her research focuses on Survival Analysis and Nonparametric Regression , with expertise in: Dependent Censoring Cure Models Measurement Error Correction Quantile Regression Multivariate Statistical Inference Causal Surrogacy Metrics Recent publications emphasize copula-based methods, quantile regression for censored data, and applications to biomedical and economic forecasting. Key projects include instrumental variable approaches for endogeneity in cure models, bankruptcy prediction via machine learning, and measurement error modeling in multivariate settings. Notable affiliations: Centre for Efficiency and Productivity Analysis Collaborations in pharmaceutical statistics and actuarial science

Yinqiu Heمشاهده پروفایل

استادیار

• High-dimensional statistical inference
• Large-scale data analysis
• Mediation pathway analysis
• +۵ مورد دیگر

Yinqiu He is an Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison. They hold affiliations with the School of Computer, Data & Information Sciences and the Data Science Institute at Columbia University (2021-2022 postdoc). Their research focuses on developing statistical methodologies for high-dimensional and complex data, with applications in genomics, metabolomics, and network analysis. Key areas include mediation pathway analysis, asymptotic theory for U-statistics, and functional connectivity modeling. Education includes a B.S. in Statistics from the University of Science and Technology of China (2016) and a Ph.D. in Statistics from the University of Michigan-Ann Arbor (2021), advised by Professors Gongjun Xu and Xuming He. They were awarded the ProQuest Distinguished Dissertation Award (2022) and received multiple travel grants from the Institute of Mathematical Statistics and ASA. Teaching includes core Ph.D. courses like STAT 849 (Regression Analysis) and applied courses like STAT 456 (Multivariate Statistics). Current mentoring includes MS student Yuhan Zheng (now pursuing UW-Madison Ph.D.) and Xiangyi Liao (Ph.D. in Educational Psychology). Research outputs include foundational work on adaptive U-statistics testing frameworks and scalable methods for large-scale genomic data analysis. Active in methodological contributions to biostatistics, their work bridges statistical theory and computational efficiency. Recent projects involve dynamic functional connectivity estimation from fMRI data and latent space modeling in heterogeneous networks. GitHub repository 'Adaptive-U-stats' hosts open-source implementations of high-dimensional testing algorithms developed in their research.

David Jensenمشاهده پروفایل

استاد

• Machine Learning
• Causal Modeling
• Artificial Intelligence
• +۳ مورد دیگر

David Jensen is a Professor in the College of Information and Computer Sciences (CICS) at the University of Massachusetts Amherst. He directs the Knowledge Discovery Laboratory and the Computational Social Science Institute. His research focuses on machine learning, causal modeling, and analyzing large social, technological, and computational systems. Jensen's work is supported by organizations like the National Science Foundation and DARPA. Education: DSc in Engineering and Policy, Washington University in St. Louis (1992) MS in Engineering and Policy, Washington University in St. Louis (1988) BS in Mechanical Engineering, University of Nebraska (1986) Research Interests: Causal inference in relational and dynamic systems Machine learning applications in security and privacy Computational social science Large-scale network analysis Achievements: Recipient of teaching awards from UMass College of Natural Sciences (2011) and CICS (2022) 2017 IEEE INFOCOM Test of Time Paper Award Leadership roles in conferences and journals, including action editor for the Journal of Machine Learning Research Labs and Affiliations: Founder of the Knowledge Discovery Laboratory (2000) Director of the Computational Social Science Institute (2018-2022) Member of the Computing Community Consortium (CCC) Council

• Artificial Intelligence
• Biomedical Informatics
• Drug Design
• +۳ مورد دیگر

Marinka Zitnik is an Associate Professor of Biomedical Informatics at Harvard Medical School, Associate Faculty at the Kempner Institute for the Study of Natural and Artificial Intelligence, and Associate Member at the Broad Institute of MIT and Harvard. Her research focuses on advancing artificial intelligence for medicine and science, including AI-driven drug design, genomic medicine, and multimodal data fusion. She leads initiatives such as the Therapeutics Data Commons and the International AI4Science initiative, emphasizing AI's role in scientific discovery and personalized healthcare. Research Interests: Dr. Zitnik's work integrates AI with biomedical challenges, developing geometric deep learning and multi-scale models to address therapeutic design, disease prediction, and precision medicine. Her lab pioneers approaches for contextualized AI systems and large pretrained models to analyze genetic, cellular, and molecular data for clinical insights. Labs/Teams: Zitnik Lab (focusing on AI in Biomedicine) and collaborations across institutions like the Broad Institute and Harvard Data Science Initiative. She also contributes to global initiatives like the AI4Science network.

• Econometrics
• Agricultural Economics
• Resource Economics
• +۳ مورد دیگر

Zheng Li is an Assistant Professor in the Department of Agricultural and Resource Economics at North Carolina State University. His research focuses on econometric methodologies with applications in agricultural economics, resource management, and policy analysis. He holds expertise in nonparametric estimation, quantile regression, and structural econometric modeling. Key research interests include analyzing agricultural production risks, evaluating policy impacts on housing markets, and developing advanced statistical techniques for mixed data types. His work bridges econometric theory with practical applications in environmental, urban, and transportation sectors. Recent publications explore topics such as lung cancer detection via biomedical sensing technologies, ridesharing platform incentives, and pandemic effects on real estate markets. Methodologically, his contributions span kernel-based specification tests, bootstrap methods for heavy-tailed data, and monotonicity-constrained estimation techniques. No scientific awards or formal advisees are listed. His research often intersects with interdisciplinary challenges, reflecting a commitment to innovative solutions in applied economics and data science.

Mariya Tonevaمشاهده پروفایل

پژوهشگر ارشد

• Computational Neuroscience
• Natural Language Processing
• Brain-Language Alignment
• +۱ مورد دیگر

Mariya Toneva is a C.V. Starr Postdoctoral Fellow at Princeton Neuroscience Institute researching computational models of language processing in the brain. Her work bridges machine learning, natural language processing, and neuroscience to understand how humans comprehend language. She develops methods to align artificial language models with brain activity, using fMRI and MEG to study neural representations. Honored with NSF and C.V. Starr fellowships, her research has been recognized by the Society for Neurobiology of Language. Starting 2022, she joins Max Planck Institute as tenure-track faculty.

Qiongshi Luمشاهده پروفایل

دانشیار

• Biostatistics
• Genetic Epidemiology
• Human Genetics
• +۳ مورد دیگر

Qiongshi Lu is an Associate Professor at the University of Wisconsin–Madison, affiliated with the Department of Biostatistics and Medical Informatics within the School of Medicine and Public Health. He also holds affiliate faculty positions in the Department of Statistics, Computer, Data & Information Sciences at the College of Letters and Science. His research focuses on developing statistical methods for human genetics, including genome-wide association studies (GWAS), gene-environment interactions, and genetic risk prediction. Lu’s work bridges computational biology, epidemiology, and clinical genetics, with a particular emphasis on understanding complex trait variability and resilience mechanisms in neurodegenerative diseases like Alzheimer’s. His lab (qlu-lab.org) explores innovative statistical frameworks such as PIGEON for gene-environment interaction analysis and has contributed to tools like the R package 'ipd' for predictive data inference. Recent studies highlight his interdisciplinary approach, addressing topics from congenital heart disease genetics to socioeconomic health gradients using large-scale genomic datasets. While no formal advisees are listed, his research collaborations span multiple institutions and disciplines.

• Biostatistics
• Public Health
• Epidemiology
• +۹ مورد دیگر

Dr. Shuvo Bakar is a Senior Lecturer in the Sydney School of Public Health at the University of Sydney, within the Faculty of Medicine and Health. He holds a PhD in Statistics from the University of Southampton, UK, and has prior experience as an Assistant Professor at Yale University, Lecturer at the Australian National University, and Scientist at Data61 (CSIRO). His research focuses on statistical methods applied to public health challenges, including Bayesian hierarchical modeling, machine learning, spatio-temporal analysis, and their applications in epidemiology, clinical trials, and environmental health. Dr. Bakar's research interests span statistical methodologies such as Bayesian adaptive designs, small area estimation, and spatial risk modeling, alongside applications in child health, infectious diseases, and extreme weather impacts on health. He is an active member of academic communities, including the Royal Statistical Society (RSS Fellow), Statistical Society of Australia, and the Australian Trials Methodology Research Network. His work also involves collaborations on grants totaling millions in funding, addressing topics like climate change impacts on health inequity and cardiovascular disease prevention in remote regions. Education: PhD in Statistics (University of Southampton, UK) Key Research Themes: Obesity, Diabetes, Cardiovascular Disease; Reproductive, Maternal & Child Health Grants/Projects: Includes NHMRC-funded trials on respiratory infections in First Nations children and MRFF grants for cardiovascular risk reduction in regional Australia. Dr. Bakar's contributions extend to editorial roles for Nature Scientific Reports and Discover Public Health , and his research has been published in journals like PloS One , Climatic Change , and Journal of the Royal Statistical Society .

• Applied Microeconomics
• Health Economics
• Education Economics
• +۴ مورد دیگر

Gregor Pfeifer is a Senior Lecturer in the School of Economics at the University of Sydney, affiliated with CESifo and IZA. Previously, he was a Senior Research Officer at University College London's Department of Economics. His research focuses on Health, Education, and Public Economics, emphasizing applied microeconometrics for policy evaluation. He holds a Ph.D. from Saarland University. **Education**: Ph.D., Saarland University (Germany). **Research Interests**: Applied Microeconomics, with a focus on causal inference in Health, Education, and Public Policy contexts. His work explores topics like school smoking bans, fuel price policies, and gender wage expectations. **Grants**: Received funding from the Baden-Wuerttemberg Stiftung (€118k, 2017–2019) for STEM education research, the German Research Foundation (€17k, 2017–2018) for school tracking effects, and seed grants for junior researcher networks. **Media**: His work on school smoking bans and gender wage expectations has been covered in outlets like *The Sunday Times* and *Frankfurter Allgemeine Zeitung*. **Affiliations**: Serves as an Associate Editor at *Empirical Economics*. Active in policy evaluation, synthetic control methods, and labor market dynamics.

Serkan Ozbeklikمشاهده پروفایل

دانشیار

• Economics of Education
• Labor Economics
• Public Economics
• +۳ مورد دیگر

Serkan Ozbeklik is an Associate Professor of Economics at the Robert Day School of Economics and Finance, Claremont McKenna College. He holds a Ph.D. from The Ohio State University, an M.A. from the University of Guelph (Canada), and a B.A. from Marmara University (Turkey). His research focuses on the economics of education, labor economics, and public policy, with a particular interest in topics such as teacher gender effects, peer influences in education, and the impact of policy interventions like right-to-work laws and Medicaid expansions. Key research areas include analyzing how socioeconomic factors shape educational and career choices (e.g., STEM field selection), evaluating the causal relationships between leadership activities and future earnings, and assessing the effects of labor market policies using advanced econometric methods. His work often employs randomized experiments and synthetic control methods to ensure rigorous policy analysis. Notable publications explore topics such as gender bias in teacher evaluations, risk-taking behavior in competitive environments, and the distributional impacts of active labor market programs like Job Corps. Ozbeklik’s research has been published in journals such as the Journal of Labor Economics , Journal of Human Resources , and Journal of Policy Analysis and Management . His contributions to education economics include studies on peer effects in disadvantaged primary schools and the role of noncognitive abilities in earnings determination. While no specific awards are listed, his extensive publication record reflects significant academic engagement in policy-relevant research.

Matt McGueمشاهده پروفایل

استاد

• Behavioral Genetics
• Substance Use Disorders
• Aging
• +۲ مورد دیگر

Matthew McGue is a **Regents Professor** in the Department of Psychology at the **University of Minnesota**, with a guest professor position at the **University of Southern Denmark's Department of Epidemiology**. He specializes in **behavioral genetics**, focusing on **substance use disorders** and **aging processes**. His research leverages long-term studies like the **Minnesota Twin Family Study (MTFS)** and **Danish Studies on Aging**. **Educational Background**: Ph.D. in Psychology from the University of Minnesota (1981). **Research Interests**: Behavioral genetics, individual differences, quantitative psychology, aging, and substance abuse. He investigates genetic contributions to substance use disorders, aging mechanisms, and the interplay between genes and environment in human traits. **Publications**: Over 150 articles on topics like cannabis use effects, personality genetics, and twin studies. Recent work examines recreational cannabis legalization impacts and genetic influences on educational achievement. **Awards/Grants**: Not explicitly listed in provided texts. **Labs/Teams**: Co-director of the **Minnesota Center for Twin and Family Research**. Collaborates internationally on aging studies in Denmark.

Wenqi Shiمشاهده پروفایل

استادیار

• Artificial Intelligence in Healthcare
• Precision Medicine
• Translational Medicine
• +۳ مورد دیگر

Dr. Wenqi Shi serves as an Assistant Professor at the Peter O’Donnell Jr. School of Public Health at UT Southwestern Medical Center. Her research focuses on the integration of artificial intelligence with healthcare, particularly advancing algorithms and systems for precision medicine. She specializes in working with multi-modal patient data including EHRs, medical notes, imaging, and genomics, with dedicated applications in pediatric healthcare, cancer, and rare diseases. Her research interests include developing large language models for translational medicine, creating agentic AI and generative models for biomedical discovery, and establishing responsible AI practices to enhance clinical outcomes. Publication trends show extensive work in explainable AI, clinical decision support systems, and multi-modal data integration, with recent emphasis on retrieval-augmented language models and causal inference methodologies. Dr. Shi obtained her Ph.D. from the Georgia Institute of Technology prior to joining UT Southwestern.