جست‌وجوی استاد و پژوهشگر

Aureo de Paulaمشاهده پروفایل

استاد

• Econometrics
• Applied Economics
• Network Formation
• +۴ مورد دیگر

Aureo de Paula is a Professor of Economics at University College London (UCL), Department of Economics. He holds positions at several prestigious research institutions including the Centre for Microdata Methods and Practice (UK), the Institute for Fiscal Studies (UK), and CEPR. He is an elected Fellow of the Econometric Society and the International Association for Applied Econometrics, and a Turing Fellow (2021/23). His educational background includes: B.A. and M.Sc. in Economics from Pontificia Universidade Catolica—RJ (Brazil) in 1996 and 2000 M.A. and Ph.D. in Economics at Princeton University in 2002 and 2006 Aureo de Paula is an applied econometrician whose research focuses on the intersection of applied economic theory, econometrics and empirical microeconomics. His work spans methodological questions like the identification and estimation of multi-agent models, and empirical applications, mostly problems in developing nations and industrial organisation. His recent publications demonstrate expertise in network formation, environmental economics, health economics, and econometric methodology. His research has been featured in prominent media outlets including The Times, The Daily Mail, Economics Observatory, WSJ, and Chicago Booth Review. Professor de Paula has held significant editorial positions, including: Co-editor at the Journal of Econometrics (since 2023) Director for the Review of Economic Studies (2020-2023) Associate editor for The Review of Economic Studies, Journal of Business and Economic Statistics, Econometrics Journal, Econometric Reviews, and the Journal of Econometrics His scientific awards and recognitions include: Elected Fellow of the Econometric Society Elected Fellow of the International Association for Applied Econometrics Irving B. Kravis Award for Excellence in Undergraduate Teaching at the University of Pennsylvania Faculty Education Award at UCL (joint with Dunli Li) ERC Starting Grant (2013) Turing Fellow (2021/23) Professor de Paula serves in important leadership roles, including as an elected member of the Econometric Society Council and as chair of its Latin America Regional Standing Committee from 2022 until 2025. His research has been supported by various grants, including the ERC Starting Grant, and he has collaborated with institutions like the Office for National Statistics and The Alan Turing Institute.

• Microeconometrics
• Financial Econometrics
• Semi- and Non-parametric Statistics
• +۳ مورد دیگر

Antonio Cosma is a faculty member at the Department of Business Sciences, University of Bergamo. He holds a Doctorate in Economics and a Master’s in Financial Economics from Université catholique de Louvain. His research focuses on microeconometrics, financial econometrics, and semi/non-parametric statistical methods. Doctorate: Economics, Université catholique de Louvain Master’s: Financial Economics, Université catholique de Louvain His work analyzes conditional moment restrictions, tail dependence in global markets, and wavelet-based estimation techniques. Publications appear in journals like Journal of Financial and Quantitative Analysis and Bernoulli , with a focus on computational finance and statistical modeling for economic data. Recent articles investigate stochastic volatility in American options, stratification effects in econometric inference, and diversification risks in hedge fund markets. He teaches Elementi di Matematica and Strumenti per la Misurazione del Rischio at the University of Bergamo.

Yu Luoمشاهده پروفایل

مدرس

• Hidden Markov models
• Bayesian theory and applications
• Causal inference
• +۲ مورد دیگر

Yu Luo is a Lecturer in Statistics at the Department of Mathematics, King’s College London, affiliated with the Faculty of Natural, Mathematical & Engineering Sciences. He holds a PhD in Biostatistics from McGill University (Canada) and previously worked at Imperial College London and Lancaster University. His research focuses on Bayesian methodologies, hidden Markov models, causal inference, and applications in healthcare and transportation. Key projects include developing longitudinal Bayesian frameworks for causal analysis and evaluating policy impacts through statistical models. Education: PhD in Biostatistics, McGill University, Canada Research Interests: Hidden Markov models and Bayesian computation Causal inference methodologies Medical and transportation engineering applications Grants & Projects: EPSRC-funded project: Bayesian causal estimation via model misspecification (2024-2025) Recent Publications: Focus on Bayesian statistical methods applied to health systems, policy evaluation, and environmental interventions. Notable topics include causal dose-response modeling, stop-and-search policy analysis, and global climate change interventions.

Luc Bridetمشاهده پروفایل

مدرس

• Microeconomic Theory
• Information Economics
• Behavioural Economics
• +۷ مورد دیگر

Luc Bridet is a Lecturer in the School of Economics & Finance at the University of St Andrews, UK. He is actively involved in research and teaching, with a strong focus on microeconomic theory and its applications in information, behaviour, and institutional design. He is currently accepting postgraduate research students and supervises PhD projects in areas such as organisational incentives, charity evaluation, and higher education policy. Research Interests: Luc's research centers on the economics of information, particularly the trade-off between experimentation and optimal decision-making under uncertainty. He explores behavioural aspects such as image concerns, self-delusion, and self-signalling, and applies indirect inference methods to test theoretical models using real-world data, especially in higher education contexts. His broader expertise includes microeconomic theory, industrial organisation, public economics, contract theory, and experimentation. Publication Trends: His recent publications reflect a consistent focus on strategic behaviour under asymmetric information. Topics include endogenous optimism in financial markets, taxation of persuasive efforts in conflict settings, and the labour market consequences of educational timing. These works combine theoretical modelling with practical implications, often using dynamic frameworks and real-world calibration. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: Luc Bridet is accepting postgraduate research students and offers supervision on topics including team incentives, intrinsic motivation, charity sector evaluation, student assessment design, and grade inflation. While specific grants are not listed, his research output suggests active engagement in funded or institutionally supported projects, particularly through the School of Economics & Finance at St Andrews. Labs and Research Teams: Luc is affiliated with the research community in the School of Economics & Finance at the University of St Andrews. His collaborative work, such as with Paul Schwardmann and Margaret A. Leighton, indicates participation in research networks focused on applied microeconomics and behavioural economics. He contributes to the school's research output through discussion papers and peer-reviewed articles, aligning with broader institutional research themes including sustainable development goals related to quality education and reduced inequalities.

Tasos Christofidesمشاهده پروفایل

استاد

• Probability Inequalities
• Demimartingales
• Stochastic Orders
• +۲ مورد دیگر

Tasos Christofides is a Professor in the Department of Mathematics and Statistics at the University of Cyprus, School of Natural and Applied Sciences. He has been serving in this position since 2004 after previously holding the rank of Associate Professor from 1991 to 2004. Prior to his appointment at the University of Cyprus, he was an Assistant Professor at the State University of New York at Binghamton from 1987 to 1991. His educational background includes a Ph.D. (1987) and MSE (1985) in Mathematical Sciences from The Johns Hopkins University, USA, and a Mathematics degree (1983) from the University of Athens, Greece. Professor Christofides specializes in Probability Inequalities, Demimartingales, Stochastic Orders, Survey Methodology, and Indirect Questioning Techniques. His research primarily focuses on theoretical statistics with applications to survey methodology, particularly in developing techniques for handling sensitive questions while preserving respondent privacy. His work bridges probability theory with practical survey design, making significant contributions to both theoretical foundations and applied methodologies. His publication record demonstrates consistent contributions to statistical theory, with a particular emphasis on U-statistics, demimartingales, and randomized response techniques. The trajectory of his research shows progression from foundational work on U-statistics and probability inequalities in the early career to more applied survey methodology techniques in later years, while maintaining strong theoretical underpinnings. Professor Christofides serves as Associate Editor for several prestigious statistical journals including Communications in Statistics-Theory and Methods, Communications in Statistics-Computation and Simulation, Journal of Statistical Theory and Practice, and Statistics and Probability Letters. His editorial service reflects his standing in the statistical community and his expertise across multiple domains of statistical theory and methodology. While specific grant information is not provided in the available text, his sustained research output and editorial positions indicate active engagement with the research community.

Michael Frankeمشاهده پروفایل

استاد

• Experimental Pragmatics
• Cognitive Modeling
• Probabilistic Modeling
• +۷ مورد دیگر

Michael Franke is a Professor in the Department of Linguistics at the University of Tübingen. His research focuses on experimental pragmatics, cognitive modeling, and evolutionary approaches to language use, reasoning, and decision-making processes. He employs probabilistic modeling techniques to investigate higher-order cognition and the relationship between empirical data, abstract theory, and mathematical modeling. His primary research interests include experimental pragmatics, cognitive and evolutionary modeling, probabilistic reasoning, Bayesian statistics, and computational linguistics. Franke's work bridges theoretical linguistics with experimental cognitive science, using both human behavioral data and computational modeling approaches. He has made significant contributions to understanding scalar implicatures, pragmatic reasoning, and the application of game theory to linguistic phenomena. Recent publications reveal a growing focus on the intersection of human cognition and language models, particularly examining how large language models handle pragmatic phenomena like scalar implicatures, quantifier interpretation, and question answering. His work increasingly integrates neural and symbolic approaches to model pragmatic reasoning processes. Professor Franke has developed several important research tools including magpie (a framework for web-based experiments), faintr (an R package for interpretation of categorical predictors in Bayesian regression), and R_eproducible_LaTeX (for writing reproducible research papers). He has also created educational resources including web-books on probabilistic pragmatics, Bayesian regression, and understanding large language models. In his teaching, Franke offers courses on logic, data analysis, and Bayesian regression. His educational materials demonstrate his commitment to making complex statistical and computational methods accessible to students in linguistics and cognitive science.

• Statistical Ecology
• Spatial Statistics
• Epidemiological Modeling
• +۳ مورد دیگر

Ali Arab is an Associate Professor and Director of Graduate Studies in the Department of Mathematics and Statistics at Georgetown University's College. His academic focus integrates statistical methodologies with ecological and public health challenges. He holds a Ph.D. from the University of Missouri-Columbia and an M.S. from Southern Illinois University. Ph.D. in [Discipline], University of Missouri-Columbia M.S. in [Discipline], Southern Illinois University Research interests span statistical ecology, spatial statistics, and epidemiological modeling. He develops innovative statistical tools to address environmental and public health issues, such as Lyme disease surveillance and migration misinformation analysis. His work also explores climate impacts on avian migration patterns and vector-borne disease dynamics. Recent publications emphasize spatial-temporal modeling, Bayesian approaches, and digital health data applications. His articles highlight trends in using statistical methods to tackle real-world problems like pandemic communication and environmental health risks. While specific grants and awards are listed in his profile, detailed information is not provided here. His advising and grant activities are central to his role in fostering graduate research.

• Fluid Dynamics
• Hydraulic Systems
• Renewable Energy
• +۳ مورد دیگر

Fabrizio Paltrinieri is an Associate Professor at the University of Modena and Reggio Emilia, affiliated with the Department of Engineering Sciences and Methods. His research focuses on fluid dynamics, hydraulic systems, renewable energy, and mechanical engineering, emphasizing applications in automotive, aerospace, and industrial contexts. He teaches courses on energy conversion from renewable sources, industrial hydraulic systems, and fluid automation. His work involves advanced numerical simulations (CFD) and experimental validation of hydraulic components like pumps, valves, and actuators. Key contributions include optimizing high-speed gear pumps for electro-hydraulic transmissions and analyzing combustion systems for energy efficiency. He collaborates on projects involving hydrogen production, ceramic kiln energy optimization, and oxygen-enriched diesel engines. Education: Not explicitly listed in provided texts, but inferred through academic rank and publications. Grants/Awards: None explicitly mentioned. Research interests span hydraulic machinery design, fluid-structure interaction, and sustainable energy systems. His publications (2001–2024) reflect expertise in computational modeling, experimental validation, and interdisciplinary engineering solutions. Lab/Team Affiliation: Not explicitly detailed, but research involves collaborations with industry partners and academic networks in fluid power and mechanical systems.

Francesco Pantaloneمشاهده پروفایل

مدرس

• Computational Statistics
• Official Statistics
• Sampling Theory
• +۳ مورد دیگر

Francesco Pantalone is a Lecturer in Official Statistics at the University of Southampton, affiliated with the Statistical Sciences Research Institute (S3RI) and the Methodological Innovation group. His research focuses on survey methodology, official statistics, and environmental surveys, with particular attention to sampling design impacts and variance estimation in complex surveys. He teaches courses including STAT1003, STAT2007, STAT6125, and RESM6007, covering quantitative methods and data analytics. His recent work includes advancements in spatial sampling algorithms, M-quantile regression applications, and census coverage estimation. Supervising PhD student Jillian Delaney, his contributions span both methodological development and practical applications in official statistics.

Lynda Khalafمشاهده پروفایل

استاد

• econometrics
• financial econometrics
• energy econometrics
• +۱۰ مورد دیگر

Lynda Khalaf is a Professor in the Department of Economics at Carleton University, Faculty of Public Affairs. She holds a Ph.D. from Montréal and has a B.A. and M.B.A. from Beirut. She is a leading expert in econometrics, specializing in simulation-based and identification-robust inference, with applications in financial, energy, and macroeconomic modeling. B.A., M.B.A. (Beirut) Ph.D. (Montréal) Her research focuses on advanced econometric methods, particularly in the context of weak identification, multivariate models, asset pricing, and income inequality. She has pioneered the use of Monte Carlo and simulation-based techniques to address finite-sample inference problems. Her recent work integrates computational optimization methods like Particle Swarm Optimization into econometric testing frameworks. She has made significant contributions to the analysis of DSGE models, inflation dynamics, and risk assessment in financial markets. The trends in her recent publications show a consistent focus on improving statistical reliability in economic measurement, especially under non-standard conditions such as heavy-tailed distributions, weak instruments, and model misspecification. Her work bridges theoretical econometrics with empirical applications in inequality, energy, and macroeconomics. Tier II Canada Research Chair (2004–2008) Ranked in the top 5% of economists by RePEc SSHRC Insight Grant (2019) NSERC Discovery Grant (2019) Professor Khalaf has supervised numerous graduate students, several of whom have received awards for their thesis work. She has received continuous research funding from major Canadian agencies, including SSHRC, NSERC, and FQRSC. Her collaborative projects involve researchers from McGill University, Université Laval, and international institutions. She has served on editorial boards and scientific committees for major conferences and journals, and was President of the Société Canadienne de Science Économique (2012–2013). She also chaired the SSHRC Adjudication Committee for Economics.

Prem Devanbuمشاهده پروفایل

استاد پژوهشی

• Empirical Software Engineering
• Naturalness of Software
• Software Engineering education
• +۳ مورد دیگر

Prem Devanbu is a Research Professor of Computer Science at the University of California, Davis, where he has been a faculty member since transitioning from his industrial R&D position at Bell Labs in New Jersey. He holds a distinguished position in the Department of Computer Science within the College of Engineering, focusing on cutting-edge research at the intersection of software engineering and artificial intelligence. Dr. Devanbu earned his B.Tech from the Indian Institute of Technology (IIT) Madras and completed his Ph.D at Rutgers University under the supervision of Alex Borgida. His career path from industry to academia has shaped his practical yet research-oriented approach to software engineering problems. Devanbu's research primarily centers on Empirical Software Engineering , the Naturalness of Software , and Software Engineering education . His groundbreaking work on the naturalness hypothesis—that software exhibits statistical properties similar to natural language—has profoundly influenced the field. This research has expanded to explore bimodality in software (its dual nature as both machine-executable code and human-readable text), opening new avenues for analysis and tool development. His recent work heavily focuses on the application of Large Language Models to software engineering tasks, particularly in code summarization, program repair, and type inference. Analysis of Dr. Devanbu's recent publications reveals a clear trend toward leveraging Large Language Models for software engineering tasks. His research demonstrates how statistical properties of code can be exploited to improve software development processes, with particular emphasis on program understanding, documentation generation, and automated repair. The work bridges theoretical insights about code naturalness with practical applications that address real-world software maintenance challenges. Dr. Devanbu has received numerous prestigious awards recognizing his contributions to the field: ACM SIGSOFT Outstanding Research Award (2021) - "for profoundly changing the way researchers think about software by exploring connections between source code and natural language" Alexander von Humboldt Research Award (2022) IEEE Computer Society Harlan Mills Award (2024) ACM Fellow Six "test-of-time" or "10 year most influential paper" awards (MSR 2006, MSR 2009, ESEC/FSE 2008, ESEC/FSE 2009, ESEC/FSE 2011, ICSE 2012) Throughout his career, Dr. Devanbu has been actively involved in mentoring the next generation of software engineering researchers, serving on doctoral committees, and participating in New Faculty Symposia to support early-career academics. His research has been supported by significant grants that have enabled his team to explore innovative approaches at the intersection of empirical methods and software tool development. At UC Davis, he has contributed to building a strong software engineering research group that bridges theoretical insights with practical applications. Dr. Devanbu leads research efforts focused on understanding the statistical properties of software and leveraging these insights to build practical tools. His work on the naturalness and bimodality of code has established a framework that continues to influence how researchers approach program analysis and software development. His current team is at the forefront of exploring how Large Language Models can be effectively applied to software engineering tasks while accounting for the unique characteristics of code as a specialized form of human communication.

Harlan Campbellمشاهده پروفایل

استاد مدعو

• Bayesian Statistics
• Meta-Analysis
• Causal Inference
• +۳ مورد دیگر

Harlan Campbell is an Adjunct Professor in the Department of Statistics at the University of British Columbia (UBC), affiliated with the Faculty of Science. His research focuses on Bayesian statistics, causal inference, meta-analysis, and statistical methodology, with applications in medical and public health contexts. Campbell collaborates internationally, such as leading the Zika virus individual participant data consortium to study pregnancy outcomes. He has advised at least one student: Nathaniel Wu Dyrkton. His work integrates evidence synthesis, clinical trial analysis, and methodological innovation to address gaps in data reporting and analysis across disciplines. Education details are not explicitly provided, but his career reflects advanced training in statistical theory and applications. Research interests include developing robust statistical techniques for observational studies, addressing measurement error in meta-analyses, and improving methodologies for indirect treatment comparisons. He has contributed to frameworks for estimating infection fatality rates during the COVID-19 pandemic and advancing Bayesian approaches to evidence synthesis. Publications span topics like structural uncertainty in evidence synthesis, doubly robust estimators for clinical trials, and causal inference in longitudinal studies. His work emphasizes rigorous statistical methods to enhance reliability in medical and epidemiological research. Grants and collaborations are implied through his involvement in global consortia and methodological reviews but are not explicitly detailed in the provided text. Campbell maintains an active Google Scholar profile with extensive citation activity.

Elizabeth Halloranمشاهده پروفایل

استاد

• Epidemiology
• Vaccine Development
• Infectious Disease Modeling
• +۴ مورد دیگر

Dr. Elizabeth 'Betz' Halloran is a Professor in the Department of Biostatistics and Epidemiology at the University of Washington and an Adjunct Professor in Applied Mathematics. She leads the Biostatistics, Bioinformatics, and Epidemiology (BBE) Program at Fred Hutchinson Cancer Research Center's Vaccine and Infectious Disease Division. Her roles include directing the Summer Institute in Statistics and Modeling in Infectious Diseases (SISMID) and the Center for Inference and Dynamics of Infectious Diseases (CIDID), which advises federal responses to outbreaks. Affiliations: School of Public Health, University of Washington; Fred Hutchinson Cancer Research Center Education: MD (Freie Universität Berlin), MPH and DSc (Harvard University), BS (University of Oregon) Her research focuses on vaccine trial design, causal inference in infectious diseases, and modeling strategies to mitigate outbreaks of threats like Ebola, Zika, and influenza. She has pioneered methods for evaluating direct and indirect vaccine effects, contributing to global public health strategies. Key Achievements: Elected to the National Academy of Medicine (2019) Recipient of the Nathan Mantel Lifetime Achievement Award (2019) Developed statistical frameworks for analyzing vaccine efficacy in populations Her work spans collaborations with institutions like NIH, WHO, and universities worldwide. She advises on pandemic preparedness, including modeling SARS-CoV-2 transmission and vaccine strategies during the COVID-19 pandemic.

Heng Xiaoمشاهده پروفایل

استاد

• Data-Driven Turbulence Modeling
• Uncertainty Quantification
• Computational Fluid Dynamics
• +۵ مورد دیگر

Heng Xiao is a Professor of Data-Driven Fluid Dynamics at the University of Stuttgart, affiliated with the Institute of Aerospace Thermodynamics (ITLR) and the Cluster of Excellence EXC 2075 'Data-Integrated Simulation Science' in the Stuttgart Center for Simulation Science (SC SimTech). He previously served as Associate Professor (2020-2022) and Assistant Professor (2013-2020) at Virginia Tech, USA, and was a Postdoctoral Researcher/Lecturer at ETH Zürich (2009-2012). Ph.D., Civil Engineering, Princeton University, 2009 M.S., Scientific Computing, Royal Institute of Technology (KTH), 2005 B.S., Civil Engineering, Zhejiang University, 2003 His research focuses on integrating data science (machine learning, uncertainty quantification, data assimilation) with traditional physical models to advance predictive capabilities in multi-scale fluid systems. Key areas include Data-Driven Turbulence Modeling , Laminar-Turbulent Transition , Subsurface Flows , and Particle-Laden Flows . His work addresses turbulence modeling through neural operators, Bayesian inference, and physics-informed machine learning, with applications in aerospace, ocean engineering, and geosciences. Recent publications highlight trends in Neural Operators for Nonlocal Models , Ensemble Kalman Methods for Turbulence Inference , and Machine Learning for Permeability Prediction . Collaborative projects, such as the DFG-funded development of coupled turbulence and heat-flux models for film cooling, underscore his focus on real-world impact. Fellowship, Center of Turbulence Research Summer Program, Stanford University (2016) Finalist, Undergraduate Research Advisor Award, Virginia Tech (2014) Advisor to doctoral students including Jian-Xun Wang, Rui Sun, Jin-Long Wu, and Carlos Michelén-Ströfer, he leads the 'Data-Driven Fluid Dynamics' group at Stuttgart. The team collaborates with academia and industry, emphasizing high-performance computing and open-source tools like SediFoam for sediment transport simulations.

Fartein Ask Torvikمشاهده پروفایل

دانشیار

• Alcohol Use and Abuse
• Anxiety
• Depression
• +۳ مورد دیگر

Fartein Ask Torvik is an Associate Professor II since 2016, based at an institution not explicitly named in the text. He is also a Researcher/Postdoctoral Fellow at The Norwegian Institute of Public Health since 2012, having completed his PhD there between 2009 and 2012. His academic work spans alcohol use and abuse, anxiety, depression, behavioral genetics, quantitative methods, and personality research. Education : PhD Candidate (2009–2012) at The Norwegian Institute of Public Health. Research Interests : Focuses on behavioral genetics, mental health comorbidity, educational attainment, and the societal impacts of mental disorders. His work combines quantitative methodologies with large-scale population studies, often leveraging genetic and registry data. Key themes include gene-environment interactions, assortative mating, and the interplay between mental health and academic performance. Research Trends in Articles : His recent publications emphasize genetic and environmental influences on mental health, educational disparities, and familial transmission patterns. Studies often use twin cohorts, Mendelian randomization, and national registry data to explore causal relationships in health and education. Awards : No specific scientific awards or honors are listed in the text. Advising & Grants : No formal advisees or grant details are provided. His collaborative projects include the Health, Cognition, Family, and Employment among Men (HOMME) and Learning from Mistakes: A Longitudinal Neuroimaging-Cohort-Registry Study . Labs/Teams : Engaged in interdisciplinary teams focusing on population health, psychiatric genetics, and developmental epidemiology, likely through collaborations with The Norwegian Institute of Public Health and other academic partners.