جست‌وجوی استاد و پژوهشگر

Irene Botosaruمشاهده پروفایل

دانشیار

• Econometrics
• Microeconomics
• Panel Data Analysis
• +۵ مورد دیگر

Irene Botosaru serves as an Associate Professor in the Department of Economics at McMaster University, specializing in advanced econometric methodologies with applications in microeconomic theory and labor economics. Her scholarly profile demonstrates sustained contributions to panel data analysis, causal inference, and the economics of household resource allocation. Her academic credentials include: BA in Mathematics from the University of Washington BA (Honours) in Economics from the University of Washington MA in Economics from Yale University PhD in Economics from Yale University (2011) Professor Botosaru's research program centers on developing and applying innovative econometric techniques to address identification challenges in nonlinear panel models, particularly regarding time-varying parameters and unobserved heterogeneity. Her work bridges theoretical econometrics with empirical applications in earnings dynamics, household economics, and causal policy evaluation. Key methodological contributions include novel approaches to transformation models, duration analysis with stochastic heterogeneity, and robust treatment effect estimation in constrained panel settings. Analysis of her publication record reveals a pronounced evolution toward time-varying parameter frameworks and adversarial identification strategies, with increasing emphasis on methodological robustness for short panel data. Her research consistently addresses fundamental identification problems in microeconometrics while maintaining strong connections to substantive economic questions regarding income dynamics and household decision-making. She actively contributes to graduate education through courses including Econometrics I (ECON 761), Economic Policy Analysis I (ECON 773), and specialized Topics in Economics (ECON 700), reflecting her expertise in both theoretical and applied econometrics.

Alemu Gonsamo Gosaمشاهده پروفایل

دانشیار

• Remote Sensing
• Climate Change Impacts
• Carbon Cycle Modeling
• +۳ مورد دیگر

Dr. Alemu Gonsamo Gosa is an Associate Professor and Canada Research Chair in Remote Sensing of Terrestrial Ecosystems at McMaster University's School of Geography & Earth Sciences. His research program focuses on advanced remote sensing techniques to study vegetation dynamics, carbon cycling, and climate change impacts across scales from leaf-level to global systems. Educational background includes a B.Sc. from Debub University, M.Sc. from Wageningen University, and Ph.D. from the University of Helsinki. Research integrates ground measurements, satellite data, and ecosystem modeling to address: Plant structural and photosynthetic traits quantification Land surface phenology and carbon uptake dynamics Terrestrial primary productivity assessment Global change impacts on ecosystem functioning Carbon cycle modeling and climate feedbacks Publications demonstrate consistent focus on developing novel remote sensing methodologies, with recent work emphasizing machine learning applications, large-scale carbon mapping, and climate-ecosystem interactions. The 15 most recent articles show strong trends in: forest biomass estimation, peatland carbon storage, climate-vegetation interactions, and sustainable land management solutions. Honors include the prestigious Canada Research Chair position recognizing groundbreaking work in terrestrial ecosystem monitoring. Research utilizes extensive field networks including eddy covariance towers and collaborates with global modeling initiatives. Leads the Remote Sensing of Terrestrial Ecosystems laboratory, which develops cutting-edge methods using multi-sensor approaches (satellite, airborne, ground-based) and maintains international partnerships for large-scale ecosystem monitoring.

Hongjing Luمشاهده پروفایل

استاد

• Cognitive Psychology
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Professor Hongjing Lu is a faculty member in the Department of Psychology at the University of California, Los Angeles (UCLA). Her research explores how humans learn and reason under conditions of sparse, ambiguous, and noisy data, with applications to understanding intelligent systems in both biological and artificial contexts. Her work integrates theories and methods from Psychology , Statistics , Computer Vision , Machine Learning , and Computational Neuroscience . Key research themes include: Exploiting generic priors for learning/inference Manipulating structured representations in perception and cognition Interdisciplinary approaches to motion perception, action recognition, and object recognition Connections between human cognition and AI systems Her publications span journals like Nature Communications , Nature Human Behavior , Psychological Review , and PLoS Computational Biology , focusing on analogical reasoning, causal learning, and neural mechanisms of perception. She leads the Computational Vision and Learning Lab at UCLA.

• Renewable Energy
• Energy Storage
• GIS Analysis
• +۳ مورد دیگر

Dr. Cheng Cheng is a Senior Research Officer at the Australian National University's School of Engineering. He holds a Bachelor of Engineering (Honours) and PhD from ANU. His research focuses on renewable energy integration, decarbonization pathways, energy system modeling, and GIS analysis. He specializes in optimizing off-river pumped hydro energy storage systems. Cheng leads multiple renewable energy projects funded by government and industry grants, and has developed methodologies for energy demand forecasting and least-cost optimization. His work bridges theoretical energy modeling with practical implementation through collaborative outreach activities. Education: B.Eng(Hons) & PhD (Australian National University) Research interests emphasize sustainable energy transitions, with particular attention to geospatial analysis for energy infrastructure planning. His recent projects include developing frameworks for integrating renewable generation with storage solutions. Over 150 publications span machine learning applications in energy systems, astrophysics, and bioinformatics. Awards include academic fellowships supporting his interdisciplinary work. Cheng collaborates with industry partners to translate research into policy and technology. He directs the ANU's Renewable Energy Integration Lab (RE100), advancing grid stability solutions through innovative storage technologies. His work has been recognized in special issues of journals focusing on robust machine learning applications.

Rachel Wangمشاهده پروفایل

دانشیار

• Statistical Network Modeling
• Statistical Machine Learning
• Genomic Data Analysis
• +۳ مورد دیگر

Dr. Rachel Wang is an Associate Professor in the School of Mathematics and Statistics at the University of Sydney. Her research focuses on statistical network modeling, machine learning, and applications to genomics. She holds a PhD from UC Berkeley and completed postdoctoral work at Stanford University. Previously, she was a Harrington Faculty Fellow at the University of Texas, Austin. Education: PhD in Statistics, UC Berkeley Postdoctoral Fellowship, Department of Statistics, Stanford University Research Interests: Statistical methods for biological networks High-dimensional data analysis Single-cell genomics and multimodal data integration Conformal prediction and robust classification Grants and Awards: Harrington Faculty Fellow (2021-2022) Australian Research Council Discovery Early Career Researcher Award (2017) Teaching: STAT3023/3923 Statistical Inference (University of Sydney) STAT2011 Probability and Estimation Theory (course coordinator) Past teaching roles at Stanford University Research Tools: Developed scTIE and scJoint methods for single-cell data integration, and contributed to TAD boundary detection algorithms in genome analysis.

Morgane Austernمشاهده پروفایل

استادیار

• Applied Probability
• Machine Learning Theory
• High Dimensional Statistics
• +۶ مورد دیگر

Morgane Austern is an Assistant Professor of Statistics at Harvard University and an affiliate of the Center of Mathematical Sciences and Applications (CMSA). She holds a PhD in Statistics from Columbia University (2019), where her research focused on limit theorems for dependent/structured data under advisors Peter Orbanz and Arian Maleki. From 2019-2021, she conducted postdoctoral research at Microsoft Research New England. She has been a long-term visitor at the Simons Institute for Theoretical Computing (2022) and was invited to speak at the National Academies of Science, Engineering, and Medicine on AI for mathematical reasoning (2023). Her research spans Probability theory (Stein method, Gaussian universality, concentration inequalities, ergodic theory, optimal transport) Machine learning theory (graph neural networks, data augmentation, deep learning theory) Statistical inference (resampling methods, causal inference, high-dimensional statistics for dependent data) Awards Kavli Fellow (National Academy of Sciences, 2022) NSF CAREER Award (2025) Her work bridges theoretical foundations across probability, statistics, and machine learning, with a focus on structured data analysis and algorithmic robustness.

• Urban water use and drought policy design
• National, state, and local nutrition assistance
• Consumer decision-making
• +۳ مورد دیگر

James Sears is an Assistant Professor in the Department of Agricultural, Food, and Resource Economics at Michigan State University, part of the College of Agriculture & Natural Resources. He holds a Ph.D. from the University of California, Berkeley, and an M.S. from Montana State University. His research focuses on environmental and consumer behavioral economics, particularly in urban water policy, nutrition assistance programs, and decision-making under constraints. He employs advanced econometric techniques and spatial data to evaluate policy impacts and consumer behavior. Dr. Sears teaches data analytics and modern economics research methods at both undergraduate and graduate levels. Notably, he pioneered cloud-based econometric instruction at UC Berkeley, earning multiple teaching awards. His work has been published in prestigious journals like The American Journal of Health Economics and Transportation Research Part D , and covered by media outlets such as The Wall Street Journal . His interdisciplinary research bridges microeconomics and policy design, addressing topics like water conservation strategies, the interplay between federal and local nutrition programs, and pandemic-related behavioral changes. He collaborates with policymakers and stakeholders to translate findings into actionable insights for equitable policy solutions.

Marco Morucciمشاهده پروفایل

استادیار

• Political Methodology
• Causal Inference
• Machine Learning
• +۳ مورد دیگر

Marco Morucci is an Assistant Professor in the Department of Political Science at Michigan State University (MSU). His research focuses on developing empirical methods for political science in data-scarce environments, combining machine learning and causal inference. Applied work examines mass public action and corruption in developing countries. Education: Ph.D. in Political Methodology from Duke University, where he collaborated with David Siegel, Cynthia Rudin’s Prediction Analysis Lab, the Almost Matching Exactly Lab, and Devlab@Duke. Previously, he was a faculty fellow at the Center for Data Science at New York University. Research Interests: Innovating statistical methods for causal inference challenges, especially in low-data contexts. Applied projects include analyzing protest dynamics, constitutional structures via NLP, and corruption mechanisms in global contexts. His work bridges computational techniques with substantive political questions. Contributions: GitHub repositories reflect his methodological and applied work, including robust causal inference frameworks, constitutional clustering analyses, and legislative behavior prediction. Active in open-source tools for political science. Labs/Collaborations: Past affiliations include Duke’s interdisciplinary labs and NYU’s data science center. Current collaborations likely ongoing at MSU.

• Agricultural Finance
• Risk Management
• Financial Derivatives
• +۲ مورد دیگر

David Leatham serves as Professor and Associate Head for Graduate Programs in Texas A&M's Agricultural Economics Department. His research program focuses on financial decision-making for agricultural businesses and credit institutions, with expertise in risk/return frameworks, interest rate derivatives, and credit assessment models. Primary research areas include: Agricultural lender risk management Financial derivatives design Land market valuation Structural changes in agricultural credit Recent publications apply machine learning to land market analysis and advanced statistical methods to crop yield distributions for insurance applications. Funded by Texas Agricultural Experiment Station, Farm Credit Council, and National Crop Insurance Services.

Nao Tsuchiyaمشاهده پروفایل

استاد

• Consciousness
• Neuroscience
• Attention
• +۶ مورد دیگر

Professor Nao Tsuchiya is a neuroscientist at Monash University's Turner Institute for Brain & Mental Health, specializing in consciousness studies. He holds a PhD in Computation and Neural Systems from Caltech and a Bachelor's in Science from Kyoto University. His research explores the neural basis of consciousness, attention, and qualia, employing methods like neuroimaging (EEG/MEG/fMRI), psychophysical experiments, and mathematical frameworks (category theory, quantum cognition). Current projects include the Dreamscape Project (neurophysiology of dreams), analyzing multi-channel neurophysiological data, and testing quantitative theories of consciousness. He also leads the 'Lifting the Veil' project on visual perception disorders and serves as an editorial board member for Neuroscience of Consciousness . His work addresses fundamental questions about consciousness in animals, machines, and humans, contributing to UN Sustainable Development Goals. Education PhD in Computation and Neural Systems, California Institute of Technology (2000-2005) Bachelor in Science, Kyoto University (1996-2000) Research Interests His lab investigates: Neuronal correlates of conscious/non-conscious processing Consciousness vs attention mechanisms Quantitative theories of consciousness (e.g., Integrated Information Theory) Qualia structure mapping via mathematical models Cross-modal perception and emotion Grants & Projects Leads/co-leads 15+ research projects including: Australian Research Council-funded Dreamscape Project (2024-2027) NHMRC Equipment Grant for neural stimulation technology (2023-2024) Adversarial testing of consciousness theories (2021) Awards & Recognition While no explicit awards are listed, his work has been referenced in multiple Wikipedia pages and news outlets, indicating academic impact. Labs & Teams Runs the Tsuchiya Lab at Monash with active collaborations across disciplines (psychology, physics, computer science), focusing on experimental and theoretical approaches to consciousness.

Helmut Farbmacherمشاهده پروفایل

استاد

• Econometrics
• Data Science
• Machine Learning
• +۵ مورد دیگر

Prof. Dr. Helmut Farbmacher is a Professor of Applied Econometrics at the Technical University of Munich (TUM) since 2021, affiliated with the TUM School of Management. His research focuses on econometric methods applied to health economics, labor markets, experimental economics, and big data analysis. He holds a PhD in Economics from Ludwig Maximilian University of Munich (2012) and has held positions at the Max Planck Society (as Head of Health Econometrics), University of Mannheim, and LMU Munich. Research interests include causal inference, machine learning, statistical learning, and data science applications in economics. Notable work includes developing methodologies for handling complex data structures and improving instrument validity in econometric analyses. He received the Otto Hahn Medal (2013) for his outstanding scientific contributions. His research team includes researchers such as Rebecca Groh, Michael Mühlegger, Gabriel Vollert, and Yasemin Karamik. He has contributed to Stata modules like SIVREG and NWIND, advancing econometric software tools. Current activities emphasize bridging econometric theory with practical applications in health and policy evaluation.

• Statistical Literacy
• Financial Literacy
• Statistics Education
• +۵ مورد دیگر

Dr. Péter Kovács is an Associate Professor and Chair of the Department of Statistics and Demography at the University of Szeged, serving as Dean of the Faculty of Economics and Business Administration since 2017. Previously, he was Vice Dean (2014–2017). He holds a PhD and habilitation in Economics and an MA in Mathematics. His research focuses on statistical and financial literacy, multivariate statistical methods, and education technology. He leads the Financial Literacy Research Group and participates in initiatives like ProCivicStat. He has extensive editorial roles, including membership in the editorial boards of Pakistan Journal of Statistics and Operation Research , Journal of Applied Economics and Business , and others. He chairs the Hungarian Academy of Science's Statistics Scientific Subcommittee and holds leadership roles in international organizations like the International Association for Statistical Education (IASE) and the International Statistical Literacy Project (ISLP). His research interests bridge statistical methodology with societal issues, including the acceptance of autonomous vehicles, maternal mortality in sub-Saharan Africa, and the integration of visualization tools in education. He has been awarded the Péter György Award (2006) for teaching and recognized as Ambassador of Financial Literacy (2021). Teaching responsibilities include courses on statistics, econometrics, and business software. He emphasizes modern pedagogy, including technology-driven methods and collaborative learning approaches. His work addresses both theoretical advancements and practical applications in education and policy.

Qin Fangمشاهده پروفایل

مدرس

• Dynamic Network Analysis
• Functional Data Analysis
• High-Dimensional Statistics
• +۳ مورد دیگر

Dr. Qin Fang is a Lecturer (Assistant Professor) in the Discipline of Business Analytics at the University of Sydney Business School. She joined USYD in 2024 after completing a postdoctoral fellowship at the London School of Economics (LSE) under Prof. Qiwei Yao. Her research focuses on dynamic network analysis, functional data/time series analysis, and high-dimensional statistics. She holds a PhD in Statistics from LSE (2022) and an MSc with the Winton Prize for Academic Excellence. Her teaching experience includes courses at both the University of Sydney (e.g., QBUS2820 Predictive Analytics) and LSE (e.g., ST309 Elementary Data Analytics). She has received multiple accolades, including the LSE Class Teacher Award (Highly Commended) and the University of Sydney Business School Emerging Scholar Research Fellowship (2024–2026). Dr. Fang’s publications emphasize methodological advancements in high-dimensional statistical modeling, with applications to functional time series and network dynamics. Her work bridges theory and practice, addressing challenges in big data analysis and statistical inference. Grants/Awards: Emerging Scholar Research Fellow (USYD), LSE PhD Studentships, Winton Prize Teaching: Predictive Analytics, Data Analytics for Business Capstone, Quantitative Methods

Michael Boehnkeمشاهده پروفایل

استاد

• Biostatistics
• Genetics
• Genomics
• +۳ مورد دیگر

Michael Boehnke is the Richard G. Cornell Distinguished University Professor of Biostatistics at the University of Michigan School of Public Health. He has been on the faculty since 1984 and directs the University of Michigan Center for Statistical Genetics and the Genome Science Training Program. His research focuses on statistical methods for human genetics, particularly in type 2 diabetes and genome-wide association studies (GWAS). He leads major consortia such as DIAGRAM and MAGIC, and has published over 400 peer-reviewed articles. He has trained 36 doctoral students and post-doctoral fellows, many of whom hold academic positions at major research institutions. Education PhD in Biomathematics, University of California, Los Angeles (1983) BA in Mathematics, University of Oregon (1977) Research Boehnke develops statistical tools for analyzing genetic data and investigates genetic contributors to type 2 diabetes, lipid traits, and anthropometric measures. His work emphasizes collaborative consortia to aggregate large-scale genetic datasets. Recent projects include the Accelerating Medicines Partnership (AMP) to integrate genomic data for metabolic disease research. Publications His most recent work addresses pleiotropy in genetic variants, sex-specific gene expression in skeletal muscle, and causal mechanisms of complex traits using multi-omics approaches. These studies highlight the utility of integrative genomics and population-scale data. Awards Member, National Academy of Medicine Fellow, American Statistical Association Fellow, American Association for the Advancement of Science Grants & Advising He oversees NIH-funded projects (e.g., R01 HG009976, U01 DK062370) and has trained 24 doctoral students and 12 postdocs. His lab collaborates globally through initiatives like the Finland-United States Investigation of NIDDM (FUSION) study. Labs & Teams He leads the Center for Statistical Genetics, a hub for interdisciplinary research in computational genetics and statistical methodology.

Brian Reichمشاهده پروفایل

استاد

• Spatial Statistics
• Bayesian Methods
• Environmental Science
• +۶ مورد دیگر

Brian Reich is the Gertrude M Cox Distinguished Professor of Statistics at North Carolina State University. His research focuses on geospatial analytics, spatial statistics, and extreme value analysis, with applications to environmental science, climate change, and public health. He leads the Environmental Statistics Lab, developing computational methods for big spatial data and spatiotemporal modeling. Research Interests: Spatial and Bayesian statistical methods Extreme value analysis and machine learning applications Environmental applications in ecology, epidemiology, and climate science Awards: Gertrude M Cox Distinguished Professorship Grants & Programs: NSF Research Training Group (UQ4Life) NSF REU Site (DRUMS for undergraduates) Carolina Thyroid Cancer Study Labs/Teams: Environmental Statistics Lab Collaborations on marine conservation, wildfire management, and PFAS exposure studies