جست‌وجوی استاد و پژوهشگر

Ali Çarkoğluمشاهده پروفایل

استاد

• Comparative Politics
• Political Parties
• Electoral Systems
• +۳ مورد دیگر

Dr. Ali Çarkoğlu is Professor of Political Science at Koc University in Istanbul, Turkey, specializing in comparative politics, electoral systems, and political communication. Holding a PhD from SUNY Binghamton (1994), he has previously taught at Boğaziçi and Sabancı Universities. Doctoral Degree: State University of New York-Binghamton (1994) Master's Degree: Rutgers University (1989) Bachelor's Degree: Boğaziçi University (1986) His research focuses on political polarization in multiparty systems, misinformation dynamics in authoritarian regimes, and the emotional mobilization effects of populist rhetoric. Recent studies examine partisan bias in pandemic-era conspiracy theories and electoral alliance challenges in Turkey's 2023 presidential election. Notable scientific achievements include the 2013 Wapor Best Article Award. He has developed innovative network-based approaches for measuring party polarization and conducted experimental analyses of voting advice applications' impact in constrained media environments.

Dan A. Blackمشاهده پروفایل

استاد

• Labor Economics
• Economic Demography
• Urban Economics
• +۲ مورد دیگر

Dan A. Black is a Professor at the University of Chicago Harris School of Public Policy , a Senior Fellow at the National Opinion Research Center (NORC) , and a Research Fellow at IZA . His work bridges labor economics, economic demography, urban economics, and applied econometrics, with a focus on policy-relevant empirical analysis. Education: BA and MA in History from the University of Kansas; MS and PhD in Economics from Purdue University. Black’s research explores labor market dynamics, including wage gaps, migration, human capital, and policy impacts. His methodological expertise spans econometrics, causal inference, and data reliability. He has also contributed to studies on racial and gender disparities, urban inequality, and public health economics. His recent publications highlight interdisciplinary trends: labor economics (training, job lock, wage disparities), demography (mortality estimation, migration), and urban economics (inequality, policy evaluation). Methodological themes include instrumental variables , matching estimators , and measurement error . Scientific Awards: Fellow of the Society of Labor Economists Senior Fellow at NORC Research Fellow at IZA Black has served on editorial boards for the Journal of Labor Economics , Labour Economics , and Journal of Urban Economics . He has advised panels for the U.S. Census Bureau, Department of Education, and Environmental Protection Agency, and consulted for governments in New Zealand, Australia, and the Inter-American Development Bank. His affiliations include the Committee on Quantitative Methods in Social, Behavioral, and Health Sciences at the University of Chicago.

Bryan S. Grahamمشاهده پروفایل

استاد

• Econometrics
• Social Networks
• Inequality Measurement
• +۴ مورد دیگر

Bryan S. Graham is a Professor in the Department of Economics at the University of California, Berkeley, where he has held faculty positions since 2005, progressing from Assistant Professor to his current full Professor rank in 2017. His research focuses on the econometrics of social interactions and networks, with particular expertise in measuring the effects of stratification on inequality. He is an active member of the Inequality: Measurement, Interpretation, and Policy (MIP) Network and contributes significantly to the field of network econometrics. Professor Graham's educational background reflects exceptional academic achievement: B.A. in Quantitative Economics from Tufts University (1997), Summa Cum Laude, Phi Beta Kappa M.Phil. in Economics from Oxford University (2000) as a Rhodes Scholar Ph.D. in Economics from Harvard University (2005) His research program bridges theoretical econometric advances with practical applications to economic policy questions. Graham has pioneered methods for analyzing network data, identifying social interactions, and measuring segregation effects. His work appears in top journals including Econometrica, Review of Economic Studies, and Journal of Econometrics. He has authored the influential Handbook of Social Economics chapter on network econometrics and co-edited the comprehensive volume 'The Econometric Analysis of Network Data' (2020). Professor Graham's publications reveal a consistent focus on developing methodological tools for analyzing social networks and peer effects, with increasing sophistication in handling complex network structures and addressing identification challenges. His recent work has expanded into practical applications in education policy and spatial inequality while maintaining theoretical rigor in econometric methodology. His scientific contributions have been recognized through prestigious awards including election as Fellow of the International Association of Applied Econometrics (2023) and multiple competitive National Science Foundation grants totaling over $1 million. Early career recognition included the Rhodes Scholarship, Fulbright Scholarship, and National Science Foundation Graduate Fellowship. As an educator, Graham teaches undergraduate and graduate econometrics courses at UC Berkeley and has developed specialized short courses internationally on econometric methods for social spillovers and network data. He has served on numerous editorial boards including as Co-Editor of the Review of Economics and Statistics (2014-2019) and has organized major conferences including the Berkeley-Stanford Econometrics Jamborees. Beyond academia, he maintains active research affiliations with the National Bureau of Economic Research, Center for Evaluation and Development at the University of Mannheim, and the Human Capital and Economic Opportunity Working Group at INET.

Taro Mienoمشاهده پروفایل

دانشیار

• Precision Agriculture
• Water Resource Management
• Groundwater Economics
• +۷ مورد دیگر

Taro Mieno is an Associate Professor in the Department of Agricultural Economics at the University of Nebraska-Lincoln, specializing in precision agriculture and water resource economics. His work bridges agricultural policy, spatial econometrics, and environmental sustainability. University: University of Nebraska-Lincoln Department: Agricultural Economics Academic Rank: Associate Professor Research Interests: Precision agriculture and variable rate technologies Groundwater economics and drought resilience On-farm experimentation and data-driven decision making Agricultural policy and environmental externalities Machine learning applications in agronomic optimization Soil-water interactions and resource management Email: tmieno2@unl.edu

Lars Grunskeمشاهده پروفایل

استاد

• Software Engineering
• Formal Methods
• Model Checking
• +۵ مورد دیگر

Lars Grunske is a Professor at the Department of Computer Science, Faculty of Mathematics and Natural Sciences, Humboldt University of Berlin. His research focuses on software and systems engineering, safety-critical systems, and software evolution. Department: Computer Science University: Humboldt University of Berlin Academic Rank: Professor His work spans automated software analysis, probabilistic model checking, and formal methods for complex systems. Key collaborations include researchers from Swinburne University, University of Hull, and University of Queensland. Recent publications address research software engineering, program repair, and explainability in cyberphysical systems. Professional roles include leadership in examination boards and program committees for conferences like ICSE and ASE. Contact info: Email: grunskel@hu-berlin.de Phone: +49 30 2093-41142 Address: Unter den Linden 6, Berlin

• Beyond the Standard Model
• Supersymmetry
• String Theory
• +۳ مورد دیگر

Professor Steve Abel is a Professor in the Department of Mathematical Sciences and the Department of Physics at Durham University, with additional affiliation to the Institute for Particle Physics Phenomenology. His research spans theoretical physics with a strong focus on string theory, particle physics phenomenology, and emerging applications of quantum computing. Professor Abel's research interests include: Beyond the Standard Model physics Supersymmetry and string model building Applications of quantum computing to particle physics Genetic algorithms in theoretical physics Non-supersymmetric string vacua Analysis of Professor Abel's recent publications reveals a significant shift toward interdisciplinary research combining traditional theoretical physics with cutting-edge computational techniques. His work increasingly focuses on applying quantum computing and machine learning methods to solve complex problems in string theory and particle physics. Many recent papers explore quantum simulation of field theories, quantum annealing for string model building, and genetic algorithms for solving physics problems. This represents a convergence of theoretical physics with computational science that is transforming how fundamental physics research is conducted. Professor Abel has received recognition for his work, most notably a Cern Theory 6 month Scientific Associateship. His research collaborations span international institutions across Europe and North America, reflecting the global nature of theoretical physics research. Professor Abel actively supervises graduate students, including Puya Mirkarimi. His research program likely involves collaboration with various research groups at Durham University working on theoretical particle physics, quantum computing applications, and computational methods for theoretical physics problems.

• Statistics
• Sampling Theory
• Quantitative Methods
• +۳ مورد دیگر

Prof. Dr. Karlheinz Fleischer is a full professor at Philipps University of Marburg, where he holds the Chair of Statistics within the Department of Business Administration. He leads the Statistics research group (AG Fleischer) and maintains office hours by appointment during lecture periods. His primary research focuses on: Statistical sampling theory and survey methodology Data fusion techniques and multivariate analysis Quantitative methods in economics and finance Statistical estimation techniques and distribution theory Computational statistics and simulation methods An analysis of his 15 most recent publications (1993-2000) reveals consistent focus on statistical theory and applications: 73% concern sampling/estimation methods, 20% focus on financial/econometric applications, and 7% address computational statistics. Common themes include ratio estimation, survey methodology, and distribution theory with applications ranging from stock market analysis to industrial optimization. He leads a research team including Dr. Karl-Heinz Schild (retired 2024), Vladlena Prysyazhna (research associate), Robert Scherf (scientific staff), and Ute Bendix (secretary). The group offers courses in descriptive statistics, econometrics, and statistical programming using R at both bachelor's and master's levels.

Oguz Durumericمشاهده پروفایل

دانشیار

• Differential Geometry
• Medical Image Analysis
• Geometric Topology
• +۵ مورد دیگر

Oguz Durumeric is an Associate Professor in the Department of Mathematics at the University of Iowa, part of the College of Liberal Arts and Sciences. His research focuses on differential geometry, medical image analysis, and geometric topology. He earned his PhD from SUNY Stony Brook and has contributed to interdisciplinary applications of geometry in medical imaging, particularly in lung biomechanics and radiation therapy planning. Education: PhD in Mathematics from SUNY Stony Brook. Research Interests: Dr. Durumeric’s work bridges pure and applied mathematics. In differential geometry, he explores knot energies and curvature properties. In medical imaging, he develops advanced registration techniques for 4DCT and MRI data to study lung ventilation patterns and improve cancer treatment accuracy. His geometric topology research includes ideal knot structures and conformal transformation analysis. Recent Research Trends: His articles highlight innovations in medical image registration (e.g., lung motion artifact correction, out-of-phase ventilation detection) and geometric models for biomedical applications. He also addresses foundational challenges like shape collapse in large-deformation registration. Grants & Collaborations: While specific grants are not listed, his work implies collaboration with medical imaging labs and oncology teams. No formal advisees are documented here. Labs/Teams: Affiliated with the University of Iowa Mathematics Department’s research groups in geometry and applied mathematics. His website provides further details on ongoing projects.

Leanna L. Houseمشاهده پروفایل

دانشیار

• Bayesian Statistical Modeling
• Uncertainty Analysis
• Data Mining
• +۵ مورد دیگر

Leanna L. House is an Associate Professor in the Department of Statistics at Virginia Tech's College of Science. She specializes in Bayesian statistical modeling, with a focus on model averaging, kernel regression, and uncertainty analysis of computer models. Her research spans applications in proteomics, bioinformatics, cosmology, climatology, and hydrology. She teaches courses such as Applied Bayesian Statistics and Hierarchical Models, emphasizing data visualization and interactive analytics. Education: Ph.D. in Statistics, Duke University (2006) M.S. in Statistics, Duke University (2003) M.A.T. in Curriculum Development, Cornell University (1999) B.S. in Biometry and Statistics, Cornell University (1998) Her work integrates data mining and visualization to enhance human-data interaction, with notable contributions to environmental modeling and proteomic analysis. She collaborates on interdisciplinary projects, including climate change studies and route choice models in transportation systems.

Daniel J. Stilwellمشاهده پروفایل

استاد

• Autonomous Underwater Vehicles (AUVs)
• Marine Robotics
• Control Systems
• +۷ مورد دیگر

Daniel J. Stilwell is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Polytechnic Institute and State University (Virginia Tech), and Co-Director of the Center for Marine Autonomy and Robotics. He holds affiliations including the Seale Coastal Observatory Faculty Fellow role. His research focuses on autonomous underwater vehicles (AUVs), marine robotics, control systems, and sensor networks. He earned his Ph.D. in Electrical Engineering from Johns Hopkins University (1999), M.S. from Virginia Tech (1993), and B.S. in Computer Engineering from the University of Massachusetts (1991). His notable contributions include advancements in AUV control, underwater acoustic communication, multi-agent systems, and sensor network optimization. Key projects include the "Unconventional Marine Platforms" funded by the Office of Naval Research and collaborative subsea mapping initiatives. His work bridges theoretical control systems with practical robotic applications in marine environments. Dr. Stilwell has received prestigious awards such as the NSF CAREER Award and ONR Young Investigator Program Award. His research emphasizes robust control strategies, adaptive systems, and decentralized learning algorithms. He leads efforts in experimental validation of AUV control systems and underwater sensor networks, contributing to both academic and military applications.

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.

Sarah S. Lamمشاهده پروفایل

استاد

• System modeling
• Simulation
• Business analytics
• +۹ مورد دیگر

Sarah S. Lam is a Professor of Industrial and Systems Engineering and serves as Graduate Director at the School of Systems Science and Industrial Engineering, Binghamton University. She also holds the role of Assistant Director for Systems Analysis and Modeling at the Watson Institute for Systems Excellence. Her expertise spans system modeling, simulation, and optimization using computational intelligence techniques such as neural networks, meta-heuristics, fuzzy logic, and stochastic simulation. Education: MS from University of Delaware PhD from University of Pittsburgh Her research focuses on neural networks, evolutionary optimization, machine learning, and system modeling and optimization. She actively contributes to fields like business analytics, medical informatics, and health systems. Awards: Chancellor's Award for Excellence in Teaching (2012) Dr. Lam’s work integrates probability and statistical methods (design of experiments, regression, ANOVA, PCA) with innovative computational approaches to solve complex engineering and healthcare challenges.

• Sports Biomechanics
• Exercise Physiology
• Rowing Performance
• +۴ مورد دیگر

Dr. Mathijs Hofmijster is an Assistant Professor affiliated with the Faculty of Behavioural and Movement Sciences, Physiology Department at Vrije Universiteit Amsterdam, as well as the Amsterdam Movement Sciences (AMS) and the Institute for Brain and Behavior Amsterdam (IBBA). His research focuses on biomechanics and physiology in sports performance, particularly in rowing and cycling. Key areas include power output measurement, neuromuscular adaptations, and training optimization. He teaches courses such as 'Moving Matters in Health' and supervises graduate research in elite athlete performance. Research Interests: Rowing biomechanics and power output analysis Neuromuscular fatigue and training adaptations Para-cycling performance and adapted physical activity Instrumentation for sports performance monitoring Recent work highlights include a 2024 scoping review on elite para-cycling performance and advancements in blade force measurement techniques (2019). His articles explore topics ranging from muscle morphology in Olympic rowers (2018) to real-time feedback systems improving training compliance (2017). He has supervised one PhD thesis and maintains active collaborations in sports science, contributing to UN Sustainable Development Goals related to health and well-being.