جست‌وجوی استاد و پژوهشگر

Wendy D. Fisherمشاهده پروفایل

استاد آموزشی

• Machine Learning
• Geophysical Data Analysis
• Structural Health Monitoring
• +۳ مورد دیگر

Wendy D. Fisher is a Teaching Professor and Director of CS Professional Programs at the Department of Computer Science , Colorado School of Mines. She holds a B.S. in Computer Science from Wright State University (1996), and M.S. and Ph.D. degrees from Colorado School of Mines (2014 and 2017, respectively). Her research focuses on applying Machine Learning to geophysical data for anomaly detection in earth dams and levees, with applications in structural health monitoring and infrastructure safety. She teaches courses such as Advanced Data Science, Machine Learning, and Python Computing. Dr. Fisher has industry experience at Ball Aerospace, where she worked on NASA-sponsored flight software projects. Her research combines interdisciplinary approaches, including geophysics and computer science, to address challenges in environmental and civil engineering. She is also passionate about innovative teaching methods, as evidenced by her exploration of online programming tutors versus traditional study guides in education. Her publications span machine learning applications in structural integrity (e.g., dams/levees), geophysical data analysis, and education technology. While no specific awards are listed, her work reflects a strong commitment to both academic excellence and practical engineering solutions. She advises no listed students but maintains active roles in curriculum development and professional programs at the university. Her work is conducted at the Colorado School of Mines campus in Golden, Colorado.

Ted Juhlمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Nonparametric Models
• +۳ مورد دیگر

Ted Juhl is the O. Maurice Joy Professor and Area Director of Finance at the University of Kansas School of Business. His research focuses on econometric methodologies, particularly in time series analysis, nonparametric models, panel data, and quantile regression. He has contributed to understanding slope heterogeneity in panel data models and testing for structural stability in econometric frameworks. His work bridges theoretical econometrics with practical applications in finance and policy analysis. His research interests emphasize advancing methodologies for analyzing complex economic and financial datasets, including cointegration analysis, predictive regression, and model diagnostics. Notable contributions include addressing issues of heteroskedasticity in fixed effects models and evaluating the impact of R&D funding on scientific productivity in academic chemistry. Recent publications explore topics like rolling regression estimator distributions, policy heterogeneity in corporate finance, and the implications of cross-sectional correlation in panel data. While no specific awards are listed, his extensive publication record reflects sustained scholarly impact in econometric theory and applications. Teaching focuses on econometrics at the graduate level, aligning with his research expertise. His professional address is Capitol Federal Hall, Room 4113, Lawrence, KS. No advising or grant details are explicitly stated in the provided materials.

Carlos Cardonhaمشاهده پروفایل

استادیار

• Discrete optimization
• Mixed-integer linear programming
• Combinatorial optimization
• +۶ مورد دیگر

Carlos Cardonha is an Assistant Professor in the Department of Operations and Information Management at the University of Connecticut School of Business since 2019. He holds a Ph.D. in Mathematics from Technische Universität Berlin (2011) and degrees in Computer Science from the University of São Paulo (B.Sc. 2004, M.Sc. 2006). Previously, he worked as a Research Staff Member at IBM Research – Brazil (2012–2019). His research focuses on discrete optimization, approximation algorithms, and applications of machine learning and mathematical programming to operations research problems. He teaches courses such as OPIM 5641 (Business Decision Modeling) and OPIM 3511 (Business Data Analytics II), supported by DataCamp. Education: Ph.D. in Mathematics, Technische Universität Berlin, Germany (2011) M.Sc. in Computer Science, University of São Paulo, Brazil (2006) Bachelor in Computer Science, University of São Paulo, Brazil (2004) Research Interests: His work spans analytics, optimization, and theoretical computer science, with a focus on mixed-integer linear programming, combinatorial optimization, and algorithm design. He applies these techniques to real-world problems in scheduling, resource allocation, and machine learning model optimization. Grants & Advising: No specific grants or advisees are listed in the provided materials. Labs/Teams: Not explicitly mentioned in the text.

Ehri Ryuمشاهده پروفایل

دانشیار

• Statistical Methodology
• Structural Equation Modeling
• Multilevel Analysis
• +۴ مورد دیگر

Ehri Ryu is an Associate Professor in the Department of Psychology at Boston College. She specializes in developing and evaluating statistical methods for multilevel multivariate data analysis, with a focus on structural equation modeling (SEM) frameworks. Her research addresses methodological challenges such as model fit assessment, factorial invariance, and indirect effect testing, applied to domains like consumer behavior, cognitive neuroscience, and traumatic brain injury outcomes. Education: Ph.D., Arizona State University Research Interests: Ryu's work bridges statistical methodology and applied research. She explores advanced SEM techniques for hierarchical data, collaborating on topics ranging from social media marketing to neurorehabilitation. Her methodological contributions enhance cross-disciplinary applications in psychology, neuroscience, and consumer sciences. Publications Trends: Her publications emphasize multilevel modeling innovations, consumer behavior dynamics during crises, and psychometric rigor in latent variable analysis. Recent work highlights societal impacts like pandemic-era charitable behavior and influencer marketing efficacy. Awards/Grants: No specific awards listed in provided text. Active research grants likely tied to methodology development but not explicitly stated. Labs/Teams: Collaborates across disciplines including cognitive neuroscience and consumer psychology through applied statistical method development. Supervises students in advanced quantitative methods training.

• Bayesian Computation
• Probabilistic Numerics
• Variational Inference
• +۳ مورد دیگر

Junyang Wang is a Research Associate in the Department of Mathematics at Imperial College London's Faculty of Natural Sciences. His research focuses on Bayesian methodology with applications in sustainability and public health, including Bayesian computation, probabilistic numerics, and variational inference. He collaborates with Dr. Sarah Filippi on scalable Bayesian mixture models for clustering risk factor data, and with NCD-RisC on public health applications. Previously, he worked on Bayesian statistical methods for material flow analysis in civil engineering. He holds a PhD in Statistics from Newcastle University (focused on Bayesian probabilistic numerical methods for differential equations) and a Mathematics degree from the University of Cambridge. His work bridges computational statistics with real-world challenges, emphasizing interdisciplinary collaboration across environmental science, epidemiology, and engineering systems. Current projects aim to advance scalable Bayesian techniques for high-dimensional data and complex systems analysis.

Nayel Bettacheمشاهده پروفایل

استادیار مهمان

• Mathematical Statistics
• Machine Learning
• Time Series Analysis
• +۳ مورد دیگر

Nayel Bettache is a Visiting Assistant Professor in the Department of Statistics and Data Science at Cornell University since August 2024. He holds a PhD in Mathematical Statistics from Institut Polytechnique de Paris (advised by Cristina Butucea). His research focuses on foundational aspects of mathematical statistics and machine learning, emphasizing provable theoretical guarantees for practical methods. Key areas include time series analysis, random matrices, and high-dimensional problems. He has held visiting roles at Harvard and Cornell, and was a researcher at CREST-ENSAE. Education : PhD in Mathematical Statistics, Institut Polytechnique de Paris (2021–2024) M.Sc. in Statistics and Learning, ENSAE IP Paris (2017–2020) M.Sc. in Data Science, Ecole Polytechnique (2019–2020) Classes Préparatoires MPSI-MP, Institution Aux Lazaristes (2014–2017) Research Interests : His work bridges theoretical statistics and applied machine learning, with emphasis on matrix regression, high-dimensional estimation, and time series analysis. He designs methods with rigorous statistical guarantees while maintaining computational efficiency. Teaching : Currently teaches Statistical Methods II, R Programming, and Machine Learning at Cornell. Previously served as a teaching assistant at ENSAE with top pedagogical ratings. Awards : Junior Scientific Visibility Grant (2023, Fondation Mathématique Jacques Hadamard) PhD Grant (2021–2024, Genes Foundation) Other Activities : Founded NayelPrepa, an educational company with 250k+ video views and 100+ trained students, rated 100% 5-star on Trustpilot. Conducted industrial research at BNP Paribas and Advestis.

Nicholas Clarkمشاهده پروفایل

مدرس

• Ecological Forecasting
• Disease Ecology
• Statistical Modeling
• +۲ مورد دیگر

Nicholas Clark is an ARC DECRA Fellow and Lecturer at the School of Veterinary Science, University of Queensland, with affiliations to the Centre for Marine Science. His research focuses on computational tools for predicting ecological responses to environmental change, including host-parasite interactions, wildlife populations, and veterinary diseases. Education includes: B.Sc. Marine Ecology, University of North Carolina Wilmington Ph.D. Ecological Modelling, Griffith University Research interests center on ecological forecasting, automatic time-series analysis, macroecology of infectious diseases, and epidemiology at the human-wildlife interface. He maintains several R packages including MRFcov for Markov Random Fields and mvgam for multivariate generalized additive modeling. His recent publications show strong focus on disease ecology, statistical methods, and One Health approaches. Research impacts include media coverage of ecological forecasting advances and applications in conservation and biosecurity. Honors include an ARC DECRA Fellowship. He actively mentors students in ecological forecasting and computational biology methods.

Myra Yazbeckمشاهده پروفایل

استاد

• Health Economics
• Development Economics
• Labour Economics
• +۴ مورد دیگر

Myra Yazbeck is a Full Professor in the Department of Economics at the University of Ottawa and holds an Honorary Associate Professor role at the School of Economics, University of Queensland. She is a Research Associate at the Center for the Economics of Human Development (HCEO), University of Chicago, and a Research Fellow at the Economic Research Forum for Arab Countries, Iran and Turkey (ERF). Her research focuses on Health Economics, Labour Economics, Development Economics, and Social Interactions, with emphasis on poverty measurement, health inequality, and policy analysis. Her work spans topics including socioeconomic health disparities, peer effects on adolescent health, and the impact of economic policies on public institutions. She has contributed to journals like Journal of Health Economics and Social Science and Medicine , and serves as an Associate Editor for the Journal of Population Economics . Key areas of research include applying econometric methods to assess health achievement, analyzing labor market dynamics, and evaluating public programs aimed at reducing poverty and inequality. Her recent work addresses challenges in data-scarce environments, such as Lebanon's economic crisis, and studies spillover effects of retirement on health vulnerability. Yazbeck’s interdisciplinary approach integrates quantitative techniques with policy analysis, focusing on vulnerable populations and socioeconomic determinants of health outcomes. She collaborates with international research networks to advance evidence-based solutions for global development challenges.

• Econometrics
• Hypothesis Testing
• Multivariate Models
• +۳ مورد دیگر

Professor Grant Hillier is a Professor of Econometrics within the Department of Economics at the University of Southampton. He is responsible for Senior Recruitment and serves as a Fellow of CeMMAP (Centre for Microdata Methods and Practice), a leading research center in Microeconometrics. Born in Adelaide, Australia, he earned his first degree there before completing his doctoral research at the University of Pennsylvania under a Fulbright Scholarship. His research focuses on inference in structural models, hypothesis testing theory, multivariate models, and financial econometrics, with recent work exploring properties of Wishart matrices. He has held the role of Associate Editor of Econometric Theory (1987–2012) and received the Econometric Theory Plura Scripsit Award in 2008 for significant contributions to econometric theory. His academic career includes extensive contributions to econometric methodology, spatial autoregressive models, and nonparametric testing. He collaborates with researchers such as Raymond Kan and Federico Martellosio on topics like matrix properties and spatial econometrics. His work bridges theoretical advancements and practical applications in econometric analysis. Education: B.A. (Adelaide, Australia), Ph.D. (University of Pennsylvania, USA). Key Roles: CeMMAP Fellow, Editor of Econometric Theory . Awards: Econometric Theory Plura Scripsit Award (2008). Research Areas: Wishart matrices, hypothesis testing, spatial econometrics, structural models. His publications span high-impact journals like Journal of Econometrics and Econometric Theory , addressing foundational econometric challenges and methodological innovations.

Renato Pajarolaمشاهده پروفایل

استاد

• Computer Graphics
• Scientific Visualization
• 3D Modeling
• +۵ مورد دیگر

Prof. Dr. Renato Pajarola is the Head of the Visualization and MultiMedia Lab at the Department of Informatics, University of Zurich. His research focuses on computer graphics, scientific visualization, and geometric processing, with applications in 3D scanning, point cloud analysis, and real-time rendering. He leads a team developing advanced visualization techniques for high-dimensional data, parallel rendering frameworks, and interactive systems for complex datasets. Key research areas include: 3D reconstruction of indoor environments Tensor approximation for volume visualization Interactive ray tracing and point cloud processing Parallel rendering frameworks (e.g., Equalizer) Scientific computing and sensitivity analysis His recent publications emphasize: High-dimensional data exploration using tensor methods Efficient rendering techniques for large-scale point clouds Integration of citizen-reported weather data for environmental analysis Prof. Pajarola’s lab collaborates on projects like VIAN (visual annotation tool for film analysis) and Terrender (web-based terrain visualization). His Erdős number is 3, reflecting interdisciplinary research connections in mathematics and computer science.

• Quantitative Finance
• Portfolio Optimization
• Covariance Matrix Estimation
• +۳ مورد دیگر

Gianluca De Nard serves as a Senior Research Associate at the University of Zurich's Department of Economics, Research Fellow at New York University's Volatility and Risk Institute, and Head of Quantitative Research at OLZ AG in Zurich. His career bridges academic research and practical finance applications, with significant contributions across multiple institutions. Dr. De Nard specializes in quantitative finance with expertise in portfolio optimization, covariance matrix estimation, and financial econometrics. His research focuses on developing robust statistical methods for handling large-dimensional financial data, with particular emphasis on shrinkage techniques and factor models. His work addresses fundamental challenges in modern portfolio theory, including nonstandard errors in financial research, climate risk integration, and AI applications in investment management. Analysis of his recent publications reveals a clear evolution toward integrating machine learning with traditional financial econometrics. His 2024-2025 work shows increasing sophistication in applying AI techniques to portfolio construction while maintaining rigorous statistical foundations. The recurring themes across his research include improving covariance matrix estimation, developing factor models for portfolio selection, and addressing methodological challenges in financial research. Dr. De Nard has established collaborations with leading researchers including Nobel laureate Robert F. Engle, Olivier Ledoit, and Michael Wolf. His work appears in top finance journals including the Journal of Finance, Journal of Financial Econometrics, and Journal of Banking and Finance, with his 2024 paper 'Nonstandard Errors' accumulating over 17,000 downloads. At OLZ AG, he applies his academic research to practical investment challenges as Head of Quantitative Research, demonstrating his ability to translate theoretical advances into real-world solutions. His dual academic-industry roles position him at the forefront of quantitative finance research and application.

Barbara Fischerمشاهده پروفایل

مدرس

• Evolutionary Medicine
• Obstetric Selection
• Pelvic Evolution
• +۴ مورد دیگر

Barbara Fischer is an Elise Richter Fellow and Lecturer at the University of Vienna, Department of Evolutionary Biology. Her research investigates the evolutionary biomechanics of human childbirth, combining geometric morphometrics, comparative anatomy, and evolutionary modeling to resolve the 'obstetric dilemma'. Key contributions include the cliff-edge model of obstetric selection explaining rising Caesarean rates, analyses of sexual dimorphism in hominin pelves, and methods for skeletal age estimation using pubic symphysis morphology. Comparative work extends to Darwin's finches' self-medication behaviors and stickleback adaptation plasticity. She develops statistical approaches for morphological integration studies and teaches biostatistics, mathematical biology, and evolutionary medicine. Recognized with an Elise Richter Fellowship and science communication awards.

• Genomics
• Bioinformatics
• Statistical Genetics
• +۵ مورد دیگر

Dr. John Shaffer is an Assistant Professor at the University of Pittsburgh School of Dental Medicine, specializing in genetic research. His work focuses on applying statistical and bioinformatics methods to understand genetic contributors to complex diseases like orofacial clefts, dental caries, and craniofacial phenotypes. He explores epigenetic mechanisms, gene-environment interactions, and 3D morphological analysis. Education: PhD in Human Genetics, University of Pittsburgh (2008). Research interests include: Heritability and linkage analysis Genetic architecture of craniofacial traits Epigenomic studies in cancer and neurotrauma recovery Public health disparities in oral care Genetic epidemiology of dental diseases Publications highlight advancements in: Cranial vault morphology genetics Orofacial cleft risk loci identification Genetic basis of dental caries Environmental influences on oral health Collaborations include the GLIDE2 oral health genomics consortium and multi-ethnic GWAS initiatives.

Nicholas Bishopمشاهده پروفایل

دانشیار

• Aging Populations
• Multimorbidity
• Cognitive Decline
• +۴ مورد دیگر

Nicholas Bishop, PhD is an Associate Professor in the Department of Human Development and Family Science at the University of Arizona's Norton School of Human Ecology. He holds a courtesy faculty appointment in the School of Sociology and has extensive research experience in aging populations, multimorbidity, and health disparities. His work focuses on complex health conditions in older adults, with particular attention to Mexican-origin populations and dietary interventions. Education: PhD, Arizona State University, Sociology, 2011 MA, University of Colorado at Denver, Sociology, 2007 BA, University of Colorado at Denver, Sociology, 2004 Research Interests: Dr. Bishop examines the progression of age-related health conditions, including cognitive decline, multimorbidity, and mobility limitations. He investigates modifiable risk factors such as dietary intake and food insecurity, using advanced statistical methods like latent class analysis and longitudinal modeling. Grants: Recent grants include: $106,231 subcontract from NIA for MEMORABLE study (multimorbidity and dementia) $34,665 Egg Nutrition Center grant on cognitive health and egg consumption $64,751 California Walnut Commission grant on walnut consumption and cognition Awards: 2019 College Achievement Award for Excellence in Scholarly Activities (Texas State University), 2010 Graduate Research Fellowship (Arizona State University). Labs/Teams: Leads research initiatives in the Norton School's Resilience and Health of Marginalized Populations group, collaborating with interdisciplinary teams on projects related to aging and chronic disease.

Joao Luiz Bastosمشاهده پروفایل

دانشیار

• racism and health
• racial health inequities
• intersectional health inequities
• +۲ مورد دیگر

Dr. Joao Luiz Bastos is an Associate Professor in the Department of Health Sciences at Simon Fraser University's Faculty of Health Sciences, joining in March 2023. His work centers on structural determinants of health inequities, with expertise in racism's health impacts and intersectional frameworks. He developed Brazil's first discrimination measurement scale and conducts multinational research in Australia, Brazil, and the United States. Education includes: DDS from Universidade Federal de Santa Catarina MSc in Epidemiology from Universidade Federal de Pelotas PhD in Epidemiology from Universidade Federal de Pelotas Research examines how interlocking systems of oppression create health disparities through: Racial discrimination mechanisms Structural intersectionality frameworks Psychometric validation of discrimination scales Cross-national health inequity patterns Publications focus on oral health inequities, validation of discrimination scales, and intersectional analysis of health outcomes. Recent works demonstrate methodological innovation in measuring systemic discrimination across populations. No scientific awards reported at this time. Teaching interests include health inequities, epidemiologic methods, and multivariate statistics, though no current teaching assignments are listed.