جست‌وجوی استاد و پژوهشگر

• Quantitative Finance
• Machine Learning in Finance
• Sustainable Finance
• +۳ مورد دیگر

Guillaume COQUERET is a Professor of Finance and Data Science at emlyon business school since 2018 and Director of the AIM Institute, which coordinates research and teaching in artificial intelligence applied to management. His academic qualifications include an HDR (2022) from Université Lumière Lyon 2 and a PhD in Business Administration from ESSEC Business School (2012). His research focuses on quantitative finance, machine learning in capital markets, sustainable finance, and heterogeneous agent models. He previously served as a Quantitative Researcher at EDHEC-Risk Institute (2013–2015). Education : 2022: HDR, Université Lumière Lyon 2 2012: PhD in Business Administration, ESSEC Business School 2008: Master in Probability and Finance, Université Pierre et Marie Curie (Paris 6) 2007: Master in Management, ESSEC Business School Research Interests : Machine learning applications in finance, factor investing, climate risk modeling, ESG integration, and algorithmic portfolio strategies. Publications : Over 30 peer-reviewed articles in journals such as The Journal of Portfolio Management , European Journal of Operational Research , and Quantitative Finance . Notable works include studies on climate betas, biodiversity premiums, and supervised learning in equity investing. Books : Machine Learning for Factor Investing: Python Version (2023) Perspectives in Sustainable Equity Investing (2022) Awards : None explicitly mentioned, but recognized for contributions to quantitative finance and AI in management. Labs/Teams : Leads the AIM Institute, fostering AI-driven research in management and finance.

Roy Brouwerمشاهده پروفایل

استاد

• Environmental Economics
• Water Economics
• Sustainable Development
• +۳ مورد دیگر

Roy Brouwer is a Professor in the Department of Economics at the University of Waterloo and holds the University Research Chair in Water Resource Economics (2018). As Executive Director of Waterloo's Water Institute since 2016, he leads the Water Economics Research Group while teaching Climate Change Economics and Water Resource Economics courses. His research bridges environmental economics with practical water policy solutions across Canada, Europe, and South Asia. MSc in Agricultural Economics (Wageningen Agricultural University) PhD in Environmental Economics (University of East Anglia) Specializing in water economics , Brouwer's work spans valuation methods, hydro-economic modeling, and policy instruments for sustainable resource management. Recent research trends focus on: Multi-regional water allocation optimization Behavioral economics in water governance Green infrastructure-water security nexus Climate change adaptation strategies Risk attitudes in flood protection decisions Policy learning through BMP adoption His publications (2019-2025) demonstrate methodological innovation in discrete choice experiments, input-output modeling, and spatial econometrics applied to water systems. Notable achievements include: 2024 IWA Best Scientific Book Prize 2021 Faculty of Arts Research Excellence Award Founding Editor of Water Resources and Economics journal Advisory roles for Chinese Research Academy of Environmental Sciences and Great Lakes Science Advisory Board

Saumen Mandalمشاهده پروفایل

استاد

• Optimal experimental design
• Biostatistics
• Data science
• +۷ مورد دیگر

Dr. Saumen Mandal is a Professor in the Department of Statistics at the University of Manitoba, Faculty of Science. He holds a PhD from the University of Glasgow, UK, and MSc/BSc (Gold Medal) from the University of Calcutta, India. His research focuses on optimal experimental design, biostatistics, data science, shrinkage estimation, and constrained optimization. He has received numerous teaching awards including the Dr. and Mrs. H.H. Saunderson Award for Excellence in Teaching, Students Choice Best Professor Award, and multiple Merit Awards. He is also a P.Stat. designee from the Statistical Society of Canada. Education: PhD (Statistics), University of Glasgow, UK MSc (Statistics), University of Calcutta, India (First Class First, Gold Medal) BSc Honours (Statistics), University of Calcutta, India Research Interests: Optimal design theory and applications Biostatistical methods for clinical trials and healthcare data Data science and machine learning techniques Shrinkage estimation and model selection Linear models and goodness-of-fit testing Publications span topics like optimal regression designs, response-adaptive clinical trial methods, and statistical models for healthcare data. His work emphasizes practical applications in medicine and data-driven decision making. Awards include: Teaching Excellence Awards (2005-2007) Merit Awards for Teaching and Research (2010-2019) Faculty of Science Innovation in Teaching Award (2020) He advises graduate students in statistics and contributes to research teams in biostatistics and data science. His office is temporarily located at 256 Parker Building during construction.

• Statistical Inference
• Measurement Error Models
• Boundary Crossing Problems
• +۳ مورد دیگر

Liqun Wang is a Professor of Statistics at the University of Manitoba, within the Faculty of Science. His research focuses on statistical inference in complex models, measurement error correction, boundary crossing problems in stochastic processes, and Monte Carlo simulation methods. He holds a prominent role in advancing methodologies for nonlinear time series analysis and Bayesian inference. His work integrates theoretical rigor with practical applications, addressing challenges in econometrics, environmental science, and public health. Notable contributions include advancements in instrumental variable estimation, second-order least squares methods, and high-dimensional covariance estimation. He actively mentors graduate students in these areas and has published extensively in top-tier statistical journals. Recent research highlights include Bayesian bias correction techniques, sparse covariance matrix estimation, and modeling SARS-CoV-2 dynamics via wastewater data. His methodologies often bridge computational efficiency with statistical accuracy, making them applicable to diverse fields such as finance, biostatistics, and environmental monitoring. Despite prolific output (over 70 publications since 1990), Dr. Wang has yet to be explicitly noted for formal scientific awards. His academic profile emphasizes methodological innovation, with a strong focus on real-world data challenges and interdisciplinary collaboration.

Hyune-Ju Kimمشاهده پروفایل

استاد

• Statistics
• Biostatistics
• Regression Analysis
• +۳ مورد دیگر

Hyune-Ju Kim is a Professor of Mathematics at Syracuse University's College of Arts & Sciences, affiliated with the Applied Statistics program. Her research focuses on change-point problems, resampling tests, regression model selection, sequential analysis, and statistical applications in genetics. She holds a Ph.D. in Statistics from Stanford University (1988) and a B.S. in Mathematics from Seoul National University (1983). Recent work emphasizes advancements in Joinpoint regression software, including model selection methodologies for health data analysis. Her contributions address complex survey data challenges and longitudinal medical trend studies. She has served on multiple departmental committees, including executive roles in faculty search committees and graduate programs. Teaching responsibilities include advanced statistics courses such as MAT 652 Probability & Statistics II, MAT 654 Linear Models, and MAT 750 Statistical Consulting. Her service roles include statistics liaison for Syracuse University Project Advance and leadership in curriculum development.

Zihang Luمشاهده پروفایل

استادیار

• Biostatistics
• Epidemiological Research
• Bayesian Analysis
• +۴ مورد دیگر

Dr. Zihang Lu is an Assistant Professor in the Department of Public Health Sciences at Queen’s University, affiliated with the School of Medicine and Faculty of Health Sciences. He holds a PhD and MSc in Biostatistics from the University of Toronto (2020 and 2013) and a BSc in Biostatistics from Southern Medical University (2012). His research focuses on developing novel statistical and machine learning methods for complex data analysis, with applications in clinical and epidemiological studies, particularly in asthma, obesity, sleep, pain, and cancer research. He collaborates with clinicians to address statistical challenges in longitudinal and functional data analysis, Bayesian modeling, and integrative clustering. Education : PhD in Biostatistics, University of Toronto (2020) MSc in Biostatistics, University of Toronto (2013) BSc in Biostatistics, Southern Medical University (2012) Research Interests : Bayesian data analysis and variable selection Longitudinal and functional data clustering High-dimensional data integration Statistical methods for disease subtyping Design and analysis of observational studies Advising & Grants : Current advisees: Caroline Lee (MSc 2022), Veronique Rowley (2021), Zhiwen Tan (PhD 2022), Mojtaba Ahmadiankalati (MSc 2021) Recruiting CANSSI Distinguished Post-Doctoral Fellow for Bayesian methods in longitudinal health data analysis Accepting MSc/PhD students for 2023 Labs & Teams : His team focuses on statistical methodology development and clinical collaborations, with active projects in pediatric lung function (e.g., the CHILD Cohort Study) and chronic disease management.

• Bayesian statistics
• High-dimensional inference
• Statistical machine learning
• +۳ مورد دیگر

Mai Dao is an Assistant Professor in the Department of Mathematics, Statistics, and Physics at Wichita State University's Fairmount College of Liberal Arts and Sciences. She earned her Ph.D. from Texas Tech University under the mentorship of Professors Min Wang and Souparno Ghosh. Research Focus: Bayesian statistics, high-dimensional inference, and statistical machine learning Contact: mai.dao@wichita.edu | Jabara Hall 319 | Office hours: Tue & Thu 3:30-4:30 p.m. Research Interests include: Bayesian quantile regression High-dimensional data analysis Statistical machine learning algorithms Variable selection techniques Computational statistics Econometric modeling Recent Article Trends : Mai Dao's publications (2021-2025) emphasize Bayesian quantile regression methods, focusing on variable selection, high-dimensional inference, and computational approaches. Key themes include handling non-ignorable missing data, macroeconomic stress testing, and bridge-randomized regression techniques. Academic Expertise spans: Bayesian statistical modeling High-dimensional inference Machine learning applications Quantile regression methodologies Statistical computing

Thomas Le Barbanchonمشاهده پروفایل

استاد

• Labor Economics
• Public Finance
• Econometrics
• +۵ مورد دیگر

Thomas Le Barbanchon is a Full Professor of Economics at Bocconi University since 2025 and holds the Rodolfo Debenedetti Chair in Labor Economics. He received his PhD from Ecole Polytechnique (CREST-ENSAE) in 2012 and has been affiliated with institutions like CEPR, CREST, J-PAL, LEAP, BIDSA, IGIER, IZA, and IFS. His research focuses on labor economics, particularly job search mechanisms, unemployment insurance effects, gender disparities, and labor market policy evaluation. Educational background: PhD in Economics (Ecole Polytechnique), MSc in Economics (Universitat Pompeu Fabra), Diplôme d'ingénieur statisticien-économiste (ENSAE), and Diplôme d'ingénieur (Ecole Polytechnique) Research highlights include: Quantifying the impact of unemployment insurance reforms in France Analyzing gender differences in job search behavior Studying the effectiveness of hiring credits during economic crises Investigating how traditional AI improves job matching Examining migrant-native job search segregation patterns Scientific contributions appear in top journals like American Economic Journal: Applied Economics , Quarterly Journal of Economics , and Review of Economic Studies . Awards include two ERC grants (2017 & 2024) and the Bocconi Impact Award (2022). He mentors PhD students in economics and labor policy, currently serves as Director of IGIER research center, and maintains active editorial roles at Review of Economic Studies and Journal of the European Economic Association .

Pedro Galeanoمشاهده پروفایل

دانشیار

• Outlier Detection
• Time Series Analysis
• Bayesian Inference
• +۳ مورد دیگر

Pedro Galeano is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid (UC3M) since 2009. He holds a PhD in Statistics (2004) under Prof. Daniel Peña, focusing on multiple time series. Previously, he served as Visiting Assistant Professor of Statistics and Econometrics at the University of Chicago’s Graduate School of Business and as a Postdoctoral Fellow at the Department of Statistics and Operations Research at Universidade de Santiago de Compostela. His research focuses on time series analysis, outlier detection, Bayesian inference in financial models, and functional data analysis with applications to missing data. He is an Associate Editor of the Journal of Time Series Analysis and advises the Heliyon journal. Key contributions include developing methodologies for detecting structural breaks, modeling systemic risk via copula approaches, and advancing robust statistical techniques for high-dimensional data. Active in academic leadership, Galeano co-organized the NICDA Workshop 2025 and has published extensively on topics like dynamic factor models, sequential parameter change detection, and functional data applications in energy markets. His work bridges theoretical statistics with practical applications in finance, economics, and environmental science.

• Quantitative Methods
• Equivalence Testing
• Robust Statistics
• +۲ مورد دیگر

Robert A Cribbie is a Professor in the Department of Psychology at the Faculty of Health, York University. His work focuses on quantitative methods for psychological data analysis, particularly equivalence testing, multiplicity control, and robust statistical procedures. Current research emphasizes negligible effect testing, Bayesian statistics, and longitudinal data modeling Active in teaching graduate/undergraduate courses: Statistical Methods, ANOVA, Regression, Multivariate Analysis PI of multiple SSHRC grants (2020-2026) for equivalence testing and statistical modeling research Advisor to 12+ graduate students including Victoria Celio, Naomi Martinez Gutierrez, and Udi Alter Leads Robust Statistics Lab which developed the 'negligible' R package for equivalence analysis Recent publications address: • Equivalence testing in structural equation modeling (2024-2025) • Methodological improvements for Bayesian statistical guides (2025) • Multiplicity control practices in psychological research (2025) • Effect size interpretation standards (2023)

Burak Güneralpمشاهده پروفایل

دانشیار

• Urbanization
• Global Change
• Sustainability
• +۵ مورد دیگر

Burak Güneralp is an Associate Professor in the Department of Geography at Texas A&M University. His research focuses on urbanization, global environmental change, and sustainability, with a particular emphasis on systems analysis and geospatial methodologies. He holds a Ph.D. in Natural Resources and Environmental Sciences from the University of Illinois at Urbana-Champaign (2006), and M.S. and B.S. degrees in Industrial Engineering from Bogazici University, Turkey (2000 and 1997). Dr. Güneralp’s work addresses human-environment interactions, urban land change, and biodiversity conservation. His projects often integrate interdisciplinary approaches, including systems dynamics modeling and remote sensing, to analyze urban expansion impacts. Notable contributions include global forecasts of urban land use and assessments of flood risk under climate change. He has received awards such as the Chinese Academy of Sciences Fellowship (2012) and multiple recognitions from the System Dynamics Society. His research has informed policy discussions through contributions to the IPCC’s Fifth Assessment Report and publications in high-impact journals like *PNAS* and *Global Environmental Change*.

• Rock Mechanics
• Civil Engineering
• Mining Geotechnics
• +۴ مورد دیگر

Professor Anna Giacomini is a leading academic in Rock Mechanics and Civil Engineering at the University of Newcastle. She holds a PhD from the University of Parma, Italy, and has been at the University of Newcastle since 2005. Her roles include Director of the Priority Research Centre for Geotechnical Science and Engineering and Deputy President of the Academic Senate (Research). She specializes in rockfall hazard analysis, mine geotechnics, and numerical modeling of geomechanical systems. Her research focuses on improving safety in mining and civil environments, with over $7.5M in funding and 140+ publications. Key areas include rockfall trajectory analysis, energy absorption in safety barriers, and drapery systems. She has led 20 major projects through ACARP and pioneered low-cost photogrammetric monitoring systems for rock slopes. Professor Giacomini is also a co-founder of HunterWiSE, promoting women in STEM. She has received prestigious awards such as the 2022 NSW Premier’s Engineering Prize and the 2019 John Booker Medal. Her administrative roles include membership in the ARC College of Experts and leadership in gender equity initiatives. Her technical contributions span experimental and numerical rock mechanics, including advancements in discrete element modeling (DEM) and stochastic approaches for discontinuity shear strength prediction. She collaborates internationally with institutions like the Colorado School of Mines and the University of Bologna.

• Machine Learning
• Computer Vision
• Medical Imaging
• +۷ مورد دیگر

Ali Gooya is a Senior Lecturer (Associate Professor) in Machine Learning at the School of Computing Science, University of Glasgow, UK. His research focuses on probabilistic deep learning applied to medical imaging, particularly in cardiology and oncology, emphasizing semi/unsupervised methods due to sparse expert annotations. He holds a PhD in medical image analysis from the University of Tokyo (2007) and has held academic positions at the University of Leeds and Sheffield before joining Glasgow in 2022. Affiliations: Senior Lecturer in Machine Learning, University of Glasgow (2022–present) Lecturer in Computing, University of Leeds (2018–2022) Lecturer in Computing, University of Sheffield (2016–2018) Postdoctoral Researcher, University of Pennsylvania (2008–2011) Research Interests: Deep learning for medical imaging, probabilistic modeling, cardiac and cancer imaging, computational anatomy, and marker discovery. Key applications include motion analysis, segmentation, and predictive modeling in healthcare. Key Achievements: Won prestigious fellowships including Allen Touring Institute (2022), JSPS Short-Term (2020), Marie-Curie IIF (2014), and JSPS-PDRA (2008). Pioneered Bayesian deep learning frameworks for cardiac motion assessment and generative models in medical imaging. Grants & Supervision: EPSRC Impact Acceleration Award (PI) EPSRC New Investigator Grant (EP/S012796/1) Actively supervising PhD students in areas like Bayesian deep atlases for cardiac motion analysis. Labs & Teams: Leads research in medical AI within the School of Computing Science, collaborating on projects integrating imaging and patient metadata for clinical decision support.

Boris Gutmanمشاهده پروفایل

استادیار

• Medical image analysis
• Neuroimaging
• Imaging biomarkers of neurodegenerative disease
• +۴ مورد دیگر

Boris Gutman is an Assistant Professor of Biomedical Engineering at Illinois Institute of Technology , affiliated with the Armour College of Engineering . He holds a Ph.D. and B.S. in Biomedical Engineering and Applied Mathematics respectively from the University of California, Los Angeles (UCLA) .

Andrea Meilán-Vilaمشاهده پروفایل

استادیار

• Nonparametric Estimation
• Directional Statistics
• Kernel Smoothing
• +۳ مورد دیگر

Andrea Meilán-Vila is an Assistant Professor in the Department of Statistics at Universidad Carlos III de Madrid since 2021, holding a Juan de la Cierva Fellowship since 2023. She earned her PhD in Statistics from Universidade da Coruña (2021) and previously served as a Postdoctoral Fellow at Universidade de Santiago de Compostela's Department of Statistics, Mathematical Analysis and Optimisation. Her research focuses on nonparametric methods for analyzing complex data types, including directional, spatial, and functional data. Key areas include kernel smoothing techniques, goodness-of-fit testing for regression models, and spatial trend estimation. She serves as an Associate Editor for the Journal of Nonparametric Statistics . Recent work emphasizes applications in climate science (temperature curve modeling), fluid dynamics (wake flow control), and biomedical imaging (hippocampus shape analysis). Her methodologies address challenges like sparse data estimation and spatial correlation in regression frameworks. Key Projects: STENED (Stein-based goodness-of-fit tests for non-Euclidean data) Awards: Juan de la Cierva Fellowship (2023) Publications span journals like Journal of Fluid Mechanics , Statistical Papers , and TEST , with a focus on methodological advancements in statistical modeling and computational validation.