جستجوی استاد راهنمای دانشگاهی

Prof. Niki Kilbertus is an Assistant Professor at the Technical University of Munich (TUM) in the Department of Informatics, and Group Leader at Helmholtz AI. His research focuses on causal machine learning, ethical AI systems, and applications in healthcare, climate science, and dynamical systems. He earned his PhD from the University of Cambridge (2020) and has held positions at DeepMind, Google, and Amazon during his studies. His research interests include causal discovery, fairness in AI, counterfactual reasoning, and integrating physics-based constraints into neural networks. Key contributions include foundational work on fair machine learning (e.g., avoiding discrimination through causal models) and developing methods for causal inference in complex systems like healthcare and climate modeling. Recent work emphasizes generative models for causal interventions, robust treatment effect estimation, and physically consistent neural differential equations. He leads a large interdisciplinary group with over 20 students and postdocs working on projects funded by Helmholtz Association, ERC, and industry collaborations. Notable awards include the Leopoldina Prize for Young Scientists (2024) and membership in the Junge Akademie. His lab maintains active partnerships with ELLIS, MCML, and the Zuse Institute Berlin.

• Machine Learning
• Causal Inference
• Ethics in AI
• +۵ مورد دیگر

Michal Kolesár is a Professor in the Department of Economics at Princeton University , holding this position since July 2020. Previously, he served as Assistant Professor (2014-2020) with dual appointments in Economics and the Woodrow Wilson School (2018-2020), and as Visiting Assistant Professor at MIT (2016-2017). His research focuses on econometrics , particularly causal inference , instrumental variables , nonparametric regression , and robust statistical methods . His work addresses fundamental challenges in high-dimensional data analysis, treatment effect heterogeneity, and finite-sample inference validity. Current projects include developing bias-aware methods for regularized regression and analyzing dynamic causal effects in nonlinear systems. Kolesár's recent publications reveal a strong emphasis on methodological rigor with practical applications. His work spans instrumental variable techniques (addressing contamination bias, weak identification), regression discontinuity designs (discrete running variables, measurement error), and high-dimensional inference (sparsity fragility, honest confidence intervals). Key recurring themes include finite-sample optimality, coverage probability guarantees, and robustness to model misspecification. Fellow of the International Association for Applied Econometrics (2023) Journal of Econometrics best associate editor award (2023) Sloan Research Fellowship (2019) NSF grants for high-dimensional data inference (2021-2025) and nonparametric regression (2016-2019) Graduate Economics Club teaching awards (2017, 2018) As an educator, Kolesár teaches advanced econometrics courses at both undergraduate (ECO 312, ECO 313) and graduate levels (ECO 517, ECO 519, ECO 539b). He serves as Co-editor of the Journal of Business & Economic Statistics (2024-2027) and sits on editorial boards of Econometrica , American Economic Journal: Applied Economics , and others. His professional activities include extensive peer review for top journals and organization of major econometrics conferences including the 2026 Econometric Society Winter Meeting.

• Econometrics
• Causal Inference
• Instrumental Variables
• +۴ مورد دیگر

Carlos Cinelli is an Assistant Professor in the Department of Statistics at the University of Washington, where he conducts research at the intersection of causal inference, statistical methodology, machine learning, and artificial intelligence. He is also a data science fellow at the eScience Institute and affiliate faculty of the Center for Statistics and the Social Sciences, demonstrating his interdisciplinary approach to causal methodology. Dr. Cinelli received his Ph.D. in Statistics from the University of California, Los Angeles, advised by Chad Hazlett and Judea Pearl, two prominent figures in causal inference. His research focuses on developing new causal and statistical methods for transparent and robust causal claims in empirical sciences, with particular attention to challenges faced by social and health scientists. His work spans theoretical developments in causal identification, sensitivity analysis frameworks, and practical software implementations that enable researchers to assess the robustness of their causal conclusions. Cinelli's research program addresses fundamental questions about how unobserved confounding affects causal estimates and develops tools to quantify how sensitive findings are to potential violations of causal assumptions. His work on omitted variable bias frameworks has been particularly influential across multiple disciplines. Through his publications, Cinelli has established himself as a leading researcher in causal inference methodology, with papers appearing in top journals across statistics, machine learning, epidemiology, and social sciences. His work demonstrates both theoretical rigor and practical relevance, often accompanied by open-source software implementations that make his methods accessible to applied researchers. Best paper award at SBE 2024 in Econometrics Royalty Research Fund (RRF) Award recipient NSF/MMS research support As an advisor, Cinelli has successfully guided PhD students like Nick Irons to dissertation completion. He actively seeks new students with strong interests in causal inference. His research is supported by multiple funding sources including the National Science Foundation and the University of Washington's Royalty Research Fund. Cinelli contributes to the academic community through editorial work for the Journal of Causal Inference and by developing widely used software packages like sensemakr for sensitivity analysis.

• Causal Inference
• Statistical Methodology
• Machine Learning
• +۵ مورد دیگر

Myrto Kalouptsidi is a Professor of Economics at Harvard University. She specializes in Industrial Organization, International Trade, and Transportation Markets. Her research explores industrial policy impacts, global trade dynamics, firm investment patterns in volatile industries, and structural modeling of transportation markets. Education: BA from University of Athens, PhD from Yale University Former faculty at Princeton University Foreign Editor at Review of Economic Studies Research Fellow at NBER and CEPR Notable awards include the Frisch Medal (2022) for her work in Econometrica and the Bodossaki Young Scientist Prize (2021) . She has secured multiple NSF grants, including a CAREER grant in 2019. Her research has been widely recognized in outlets like The Economist, LSE Business Review, and Microeconomic Insights. She advises graduate students and contributes to structural modeling advancements through publications in top journals including American Economic Review, Econometrica, and Review of Economic Studies.

• Industrial Organization
• International Trade
• Transportation Markets
• +۲ مورد دیگر

Ruonan Xu is an Assistant Professor in the Department of Economics at Rutgers University, specializing in Econometrics. She joined the department in Fall 2020. Her research focuses on finite population inference, spatial correlation, and causal inference methodologies. Education: Ph.D. in Economics, Michigan State University, 2020 B.A. in Mathematical Economics, Fudan University, 2015 Research Interests: Dr. Xu’s work emphasizes econometric methodologies for addressing complex data structures, including spatial correlation, clustered data, and interference effects. She has contributed to instrumental variable estimation with binary endogenous variables and developed design-based approaches for spatial analysis. Her recent focus includes robustness considerations in econometric models and multidimensional clustering techniques. Publications & Work in Progress: Her published work includes studies in The Econometrics Journal and Economics Letters . Current projects explore distributionally robust average treatment effects and difference-in-differences with interference mechanisms. A working paper on multidimensional clustering has been submitted to the Journal of Econometrics . Advising & Grants: No formal advisees or grants explicitly listed in the provided materials.

• Econometrics
• Applied Econometrics
• Finite Population Inference
• +۳ مورد دیگر

Johannes Zimmermann is Professor of Differential and Personality Psychology at the Institute of Psychology, University of Kassel, Germany. His work integrates personality science, psychopathology research, and advanced assessment methods, with particular expertise in dimensional models of personality disorders and the Hierarchical Taxonomy of Psychopathology (HiTOP). Education and Career Diploma in Psychology, University of Koblenz-Landau (2007) Doctorate, University of Heidelberg (2011) Post-doctoral fellow, German-Chilean Graduate School, Heidelberg University (2007–2010) Research Associate, University of Kassel (2010–2015) Professor for Methodology and Psychological Diagnostics, Berlin School of Psychology (2015–2018) Professor of Differential and Personality Psychology, University of Kassel (since 2018) Research Interests Prof. Zimmermann's research centers on personality assessment , psychopathology , and psychotherapy outcomes . He develops and validates instruments for measuring personality functioning and maladaptive traits, advances the Hierarchical Taxonomy of Psychopathology (HiTOP) framework, and employs ambulatory assessment to capture dynamic processes in daily life. Key themes include: Dimensional classification of personality disorders and psychopathology Ecological momentary assessment of affect and behavior Validation of German-language assessment tools (e.g., LPFS-BF, PID-5, HiTOP-SR) Long-term effectiveness of psychodynamic and cognitive-behavioral therapies Digital mental health and smartphone-based data collection Scientific Awards ISSPD Young Investigator Award (2019) – International Society for the Study of Personality Disorders SITAR Jerry Wiggins Student Award (2010) – Society for Interpersonal Theory and Research Advising & Collaborative Networks Prof. Zimmermann mentors doctoral and post-doctoral researchers through his roles in the German-speaking psychological community. He collaborates with international consortia including the HiTOP consortium, the PsyChange Network, and the London Personality and Mood Disorder Research Consortium, serving as principal investigator or co-investigator on projects funded by the German Research Foundation (DFG) and other bodies. Labs & Teams He leads the Differential Psychology Research Group at the University of Kassel, which focuses on measurement development, ambulatory assessment, and applied psychopathology research. The group maintains active collaborations with clinical centers across Germany and Europe for data collection and intervention studies.

• Differential Psychology
• Personality Psychology
• Psychopathology
• +۵ مورد دیگر

Associate Professor Seojeong Jay Lee is a faculty member at the UNSW Business School , specializing in Econometrics and Statistical Methods . Joining the University of New South Wales (UNSW) in 2012 after completing a PhD at the University of Wisconsin-Madison, they focus on developing robust statistical tools for economic models where traditional experimental controls are impractical. Research Interests Generalized Method of Moments (GMM) Instrumental Variables (IV) and Two-Stage Least Squares (2SLS) Robust inference under model misspecification Handling invalid/many/weak instruments in econometrics Heterogeneous treatment effects and clustered sampling analysis Grants & Funding Australian Research Council (ARC) Discovery Project DP210101440 (AUD 336,939), 2021-2023 Health@Business UNSW Grant (AUD 3,500), 2020 ARC Discovery Early Career Researcher Award (DECRA) DE170100787 (AUD 331,000), 2017-2019 UNSW Business School Special Research Grant (AUD 17,699), 2013-2014 Awards & Recognition Zellner Thesis Award (Honorable Mention), American Statistical Association, 2014 Non-Professorial Research Achievement Award, UNSW Business School, 2017 Academic Leadership Currently supervising PhD candidates Wei Tian and Fangzhou Yu in econometric theory and applications.

• Econometric methods
• Statistical inference
• Causal econometrics
• +۷ مورد دیگر

Dr Hector Gutierrez Rufrancos is a Senior Lecturer in Economics at the University of Stirling School of Management , where he serves as the Undergraduate Economics Programme Director, PhD Convenor, and Summer School Director for the Scottish Graduate Programme in Economics (SGPE). He is also a Research Fellow at the Global Labor Organization . Education: PhD in Economics (2017), University of Sussex MSc in Development Economics (2010), University of Sussex BA in Economics with Development Studies (2009), University of Sussex His research focuses on the interplay between policy, institutions, and individual well-being, particularly in the context of living standards, inequality, and informality. Key areas include: Living Standards & Well-being: Historical analysis of nutrition, poverty, and inequality (19th–20th centuries) in Britain, Europe, and the USA. Institutions & Political Economy: Studies on unions in Mexico, geopolitical risk spillovers, and informal economies in Sub-Saharan Africa and Latin America. Methodological Expertise: Applied econometric techniques such as causal inference, instrumental variables (IV), regression discontinuity design (RDD), and differences-in-differences (DiD). His recent publications address topics like pollution’s effect on labor supply, informality’s impact on welfare, and historical inequality patterns. He emphasizes empirical rigor in both research and teaching, offering workshops in Stata programming, LaTeX, and causal inference methods. Academic Service: Organized SGPE Summer Schools (2022–present), co-directed SGPE PhD programs (2022–2023), and coordinates MSc dissertations. He actively presents at conferences including the Scottish Economic Society, Royal Economic Association, and UNU-WIDER seminars.

• Economics
• Political Economy
• Labour Economics
• +۶ مورد دیگر

Associate Professor Seojeong Lee is a faculty member at the University of New South Wales (UNSW) Business School, School of Economics, specializing in advanced econometric theory. She joined UNSW in 2012 after completing her PhD at the University of Wisconsin-Madison and has established herself as a leading researcher in robust inference methods under complex data conditions. Her educational background includes: Ph.D. in Economics, University of Wisconsin-Madison (2008-2012) M.A. in Economics, Seoul National University (2006-2008) B.A. in Economics and Political Science (dual major), Seoul National University, summa cum laude (2000-2006, with military service 2002-2004) Professor Lee's research centers on developing theoretically rigorous methods for econometric inference, with primary focus on generalized method of moments (GMM), instrumental variables (IV), and two-stage least squares (2SLS) under model misspecification. Her work addresses critical challenges including invalid/many/weak instruments, heterogeneous treatment effects, and clustered sampling, contributing foundational advances to statistical inference in economics. Analysis of her recent publications reveals a strong trajectory in refining methods for many-instrument settings and misspecified models, with increasing emphasis on computational implementations (e.g., Stata packages) and applications to causal inference. Her work bridges theoretical econometrics with practical policy-relevant analysis. Her scientific achievements include: Australian Research Council DECRA Fellowship (2017-2019) UNSW Dean's Research Fellowship (2020-2022) Zellner Thesis Award Honorable Mention from American Statistical Association (2014) Multiple competitive UNSW research awards Professor Lee actively supervises PhD candidates Wei Tian and Fangzhou Yu, and has secured over AUD 700,000 in research funding including ARC Discovery Projects. She teaches undergraduate and postgraduate econometrics courses, integrating her research into pedagogy. Her ongoing work continues to push boundaries in robust econometric methodology for modern data challenges.

• Econometrics
• Statistical Inference
• Generalized Method of Moments (GMM)
• +۸ مورد دیگر

Stefan Hoderlein is a Professor in the Department of Economics at Emory University. His expertise lies in econometrics, with a focus on nonparametric methods, panel data analysis, and structural models. He holds a PhD from Bonn University and the London School of Economics (2002), and a Diplom Volkswirt from Bonn University (1997). His research interests include advanced econometric techniques such as instrumental variable estimation, demand analysis, and random coefficient models. He has contributed to methodologies addressing unobserved heterogeneity, endogeneity, and identification challenges in economic data. His work often explores applications in consumer behavior, market structure, and policy evaluation. Recent research trends in his publications emphasize nonparametric identification strategies, panel data methodologies, and the integration of big data into econometric frameworks. His technical contributions include Stata modules for statistical testing and frameworks for analyzing aggregate demand and welfare effects. While no specific awards are listed, his extensive publication record reflects sustained scholarly impact in econometric theory and applied economics. Advising details and grant information are not explicitly provided in the sources, though his work often involves collaborative research teams. His office is located in the R. Rollins Building (R428), and he maintains an active academic website.

• Econometrics
• Big Data
• Panel Data Analysis
• +۳ مورد دیگر

Rodrigo González is an Assistant Professor at the Department of Mechanical Engineering, Eindhoven University of Technology, since 2022. His research focuses on data-driven modeling, estimation, and control methods for high-tech precision systems, with applications in motion control and continuous-time system identification. Education: Ph.D. in Electrical Engineering (KTH Royal Institute of Technology, 2022) M.Sc. in Electronic Engineering (Universidad Técnica Federico Santa María, 2016) His work emphasizes continuous-time system identification, state-space modeling, and Bayesian estimation techniques. Key research themes include motion control tuning, multivariable systems, and noise/disturbance modeling in precision engineering applications. Rodrigo has received the Best Electronic Engineering Student Award (2016) and Best Thesis Award from Universidad Técnica Federico Santa María. He has active collaborations with institutions like Universidad Técnica Federico Santa María through visiting researcher appointments. Scientific awards include: Best Electronic Engineering Student Award (2016) Best Thesis Award (Universidad Técnica Federico Santa María)

• Data-driven modeling
• System identification
• Control systems engineering
• +۴ مورد دیگر

Adam M. Rosen is a Professor of Economics at Duke University's Department of Economics, where he has served since 2019. Previously, he held academic roles at University College London (UCL), including Associate Professor (2013–2016) and Assistant Professor (2006–2013). He is an affiliated researcher with the Centre for Microdata Methods and Practice (CeMMAP) and the Institute for Fiscal Studies (IFS). His research focuses on econometric theory and applications, particularly in instrumental variable methods, partial identification, and structural models. Rosen earned his Ph.D. in Economics from Northwestern University (2006) and a B.A. in Economics and Mathematics with Computer Science from Cornell University (1999). His work has been published in top journals such as the Review of Economic Studies, Econometrica, and the Journal of Econometrics. He has received numerous awards, including Fellow of the Journal of Econometrics (2024) and the Best Associate Editor Award (2024). Rosen's research interests span econometric theory, partial identification, instrumental variables, and applied microeconometrics. His recent work includes advancements in IV methods for Tobit models, discrete choice models, and counterfactual analysis. He has advised over a dozen PhD students and contributes actively to professional service, including editorial roles at the Journal of Econometrics and Econometrics Journal. He has held grants from the European Research Council and ESRC, among others, and his teaching spans advanced econometrics courses at both UCL and Duke. His affiliations with CeMMAP and IFS reflect his commitment to advancing microdata methods and policy analysis.

• Econometrics
• Microeconometrics
• Partial Identification
• +۳ مورد دیگر

Andrew Chesher is the William Stanley Jevons Professor of Economics and Economic Measurement at University College London (UCL) and holds Honorary Professor status at Beihang University. He previously served as Professor of Econometrics at the University of Bristol (1984–1999) and Lecturer in Econometrics at the University of Birmingham (1971–1983). He earned a Bachelor of Social Science from the University of Birmingham (1970) and a Doctor of Science, Honoris Causa from the University of Birmingham (2017). His research focuses on econometric methods, structural modeling, measurement error analysis, and applied microeconometrics. Key contributions include work on instrumental variable methods, panel data analysis, and applications in transportation economics, population policy, and market structure. Chesher has advised international agencies like the World Bank and UNDP, contributing to real-world policy solutions. He is a Fellow of the British Academy, Econometric Society, and Academy of Social Sciences. Chesher founded UCL’s Centre for Microdata Methods and Practice (2000), a global leader in econometric methodology. His awards include the 2017 Doctor of Science (honoris causa) and leadership roles such as President of the Royal Economic Society (2016–2018). Research highlights include modeling highway maintenance costs in Brazil/India (World Bank), Malaysia’s marriage/fertility policies (UNDP), and private-sector efficiency studies with firms like EDF Energy and regulators like Ofcom. His work bridges theory and practice, emphasizing robust methods for endogeneity, discrete choice models, and incomplete data scenarios.

• Econometrics
• Econometric and statistical methods
• Economics
• +۳ مورد دیگر

Giovanni Forchini is a Professor at the Umeå School of Business, Economics and Statistics (USBE), Umeå University, Sweden. His research focuses on econometrics, panel data analysis, and their applications in health economics and epidemiological modeling. He holds the title of Docent, a Swedish academic qualification reflecting advanced expertise. His work bridges theoretical econometrics with practical policy analysis, particularly in pandemic preparedness and healthcare optimization. Research Themes: Econometric methodologies for panel data and structural equation models Quantifying pandemic impacts on healthcare systems and economies Optimization of resource allocation during public health crises Key Contributions: Developed the DAEDALUS model for integrated economic-epidemiological policy simulations Analyzed SARS-CoV-2 transmission dynamics and vaccine impact in multiple countries Pioneered statistical methods for handling multifactor structures in panel data Awards & Grants: USBSE Pedagogical Prize 2020 Funding from Forte (Swedish Research Council for Health, Working Life and Welfare) and Handelsbanken Teaching & Mentorship: Coordinates Master’s theses in Economics at USBSE Teaches advanced courses like Econometrics 1 & 2 and Analysis of Financial Data

• Econometrics
• Panel Data Analysis
• Health Economics
• +۳ مورد دیگر

Whitney Newey is the Ford Professor of Economics at the Massachusetts Institute of Technology (MIT) and a Research Associate at the National Bureau of Economic Research (NBER). He holds affiliations with multiple prestigious institutions, including fellowships with the American Academy of Arts and Sciences, the Econometric Society, and the International Association of Applied Econometrics. His research focuses on advanced econometric methodologies, particularly in structural and causal parameter estimation, machine learning integration, and high-dimensional regression analysis. Dr. Newey’s academic career includes roles as co-editor of Econometrica and program co-chair for the 2005 World Congress of the Econometric Society. His work bridges theoretical econometrics and applied policy analysis, addressing topics such as taxable income elasticity, demand modeling, and semiparametric estimation techniques. Notable achievements include the 2020 American Economic Association Distinguished Fellow award and the Sloan Foundation Research Fellowship. His publications span leading journals such as Quantitative Economics , Econometrica , and Journal of Political Economy , reflecting contributions to both foundational econometric theory and practical policy evaluation. His advisory roles include mentoring through MIT’s Department of Economics, though specific advisee names are not listed in available materials. Current research emphasizes debiased machine learning applications and inference methods in complex economic models.

• Econometrics
• Machine Learning
• Causal Inference
• +۷ مورد دیگر