جست‌وجوی استاد و پژوهشگر

Roop Aparajita Subhra Purushottamمشاهده پروفایل

دانشیار

• Machine Learning
• Extreme Classification
• Optimization Techniques
• +۵ مورد دیگر

Roop Aparajita Subhra Purushottam is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. His research focuses on machine learning foundations and applications, particularly in extreme classification, optimization techniques, robust learning, and educational technology. He has developed scalable algorithms for web-scale applications and innovative teaching tools for programming education. His research interests span: Design and analysis of machine learning algorithms Statistical learning theory and online optimization Non-convex optimization for large-scale problems Robust learning against adversarial corruptions Applications in information retrieval, education, and environmental monitoring Recent publications demonstrate a strong focus on extreme classification techniques, efficient deep learning architectures, and educational technologies. His work consistently appears in top-tier conferences including KDD, ICML, NeurIPS, and CVPR, with innovations in scaling machine learning systems to handle millions of labels and users. Significant Awards: Gopal Das Bhandari Distinguished Teacher Award (2024) PK Kelkar Faculty Fellowship (2024-2027) Microsoft Bing Ads Greatness Award (2021) Computer Society of India Faculty Award (2018) Multiple best paper awards and nominations at major conferences He leads several research grants and consults for industry partners including Microsoft Research and Tower Research. His team develops open-source tools like Prutor for programming education and DEFRAG for efficient feature agglomeration in extreme classification. He has advised numerous PhD and Master's students who have received prestigious awards for their research contributions.

Pengfei Wangمشاهده پروفایل

استادیار

• Geotechnical Engineering
• Engineering Seismology
• Applied Statistics
• +۴ مورد دیگر

Pengfei Wang is an Assistant Professor in the Department of Civil & Environmental Engineering at Old Dominion University (ODU). He holds a Ph.D. in Geotechnical Engineering and an M.S. in Statistics from UCLA, alongside a B.S. in Transportation Engineering from Tongji University. Prior to ODU, he conducted postdoctoral research at UCLA. His expertise focuses on Geotechnical Engineering , Engineering Seismology , and Applied Statistics , with emphasis on regional geo-hazard modeling, multi-hazards risk assessment, and statistical learning applications. Key research interests include seismic site response analysis, liquefaction susceptibility, and probabilistic risk frameworks for infrastructure resilience. Dr. Wang’s work integrates geospatial analysis and statistical methodologies to address challenges in earthquake engineering. He has developed frameworks for regional landslide and liquefaction risk assessments, particularly in vulnerable regions like California’s Sacramento-San Joaquin Delta. His contributions include advancing HVSR (Horizontal-to-Vertical Spectral Ratio) methodologies and ergodic site response modeling. He maintains active collaborations with institutions globally and contributes to open-source databases for seismic data, promoting transparency and reproducibility in geotechnical research. His educational background in transportation engineering enriches interdisciplinary approaches to civil infrastructure resilience.

Hui Luanمشاهده پروفایل

استادیار

• Spatial Epidemiology
• Bayesian Spatiotemporal Statistics
• Geographical Information Science (GIS)
• +۴ مورد دیگر

Hui Luan is an Assistant Professor in the Department of Public Health at the Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center. He joined UT Southwestern in 2024 after serving as an Associate Professor (tenured) at the University of Oregon (2018–2024). His research focuses on spatial epidemiology, Bayesian spatiotemporal statistics, and GIS applications to address health disparities and environmental determinants of disease. Education: Ph.D. in Planning, University of Waterloo, 2017 M.S. in Cartography and GIS, Wuhan University, 2011 B.S. in GIS, Wuhan University, 2009 Research Interests: Dr. Luan investigates spatial and temporal variations in health outcomes, particularly HIV incidence, and the role of social, physical, and built environments. He develops Bayesian models and spatial analytics to inform geographically tailored interventions. Current projects include optimizing PrEP accessibility, analyzing food environment equity, and leveraging spatial data science for public health policy. Key Achievements: Lead investigator of an NIH-funded R01 project on reducing HIV incidence via spatial data science Editorial board member of Spatial and Spatiotemporal Epidemiology Recipient of the Vu Fellowship (2021–22) Teaching: Teaches courses on GIS in Public Health, Spatial Analysis, and Big Data applications in geography. Recent courses include GIS and Public Health and Spatial and Spatiotemporal Analysis at the University of Oregon. Labs/Teams: Leads the Spatial Data Science Lab focused on advancing Bayesian methods for health data analysis and promoting GIS applications in public health practice.

• Magnetic Resonance Imaging (MRI)
• Functional MRI (fMRI)
• Biomedical Imaging Physics
• +۶ مورد دیگر

Dr. Gary Glover is a Professor of Radiology (Radiological Sciences Lab) at Stanford University , with courtesy appointments in Psychology and Electrical Engineering. His work focuses on the physics and mathematics of MRI, particularly rapid scanning methods using spiral k-space trajectories for functional brain imaging and multimodal neuroimaging (fMRI/EEG/fPET/fNIRS) combined with neuromodulation techniques like TMS and transcranial ultrasound. Academic Appointments: Radiology, Psychology, Electrical Engineering Professional Affiliations: Bio-X, Stanford Cancer Institute, Wu Tsai Neurosciences Institute Research Interests include: Development of blood oxygen level-dependent (BOLD) and viscoelastic contrast in MRI Functional MR Elastography for brain activation mapping Optimization of MR-ARFI for transcranial ultrasound guidance Automated spinal cord segmentation (EPISeg) using machine learning Scientific Awards : National Academy of Engineering (2013) Gold Medal, ISMRM (2000) Steinmetz Award, General Electric (1985) Lauterbur Lecture, ISMRM (2018) Recent Publications analyze: Fast fMRI sampling and spurious signal correction Dissociated patterns in default mode network anti-correlations Neural correlates of collaborative behavior in triadic fMRI Salience network contributions to depression pathophysiology

Gaël Kermarrecمشاهده پروفایل

پژوهشگر

• Boundary Layer Meteorology
• Atmospheric Turbulence
• GNSS Applications
• +۳ مورد دیگر

Dr. Gaël Kermarrec is a researcher at the Boundary Layer Meteorology Group , part of the Institute of Meteorology and Climatology within the Faculty of Mathematics and Physics at Leibniz University Hannover . His work focuses on atmospheric turbulence, GNSS applications, and remote sensing for environmental monitoring. Boundary layer meteorology Turbulence theory GNSS signal processing Terrestrial laser scanning Climate change impacts Geodetic time series analysis His research integrates advanced mathematical models like LR B-splines and Matérn covariance with large eddy simulations to study: Atmospheric turbulence effects on optical/GNSS signals Hydrospheric mass loading Deformation analysis of terrain/port infrastructure Climatic sea-level changes Machine learning for remote sensing The 15 most recent articles (2025-2023) demonstrate his focus on: GNSS-based turbulence detection AI-enhanced climate mapping Advanced surface approximation techniques Multi-sensor data fusion Stochastic modeling of geodetic observations Environmental impacts on optical measurements He has developed tools like the Klimascanner QGIS plugin for urban climate resilience and contributes to: Understanding atmospheric scale lengths Improving TLS/GNSS deformation monitoring Analyzing hydrospheric changes Wavefront modeling Ionospheric corrections

Jianjun Shiمشاهده پروفایل

استاد

• Manufacturing Systems
• Quality Control
• Cyber-Physical Systems
• +۳ مورد دیگر

Jianjun (Jan) Shi is the Carolyn J. Stewart Chair and Professor at the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) and holds a joint appointment with the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. He previously served as the G. Lawton and Louise G. Johnson Chair Professor of Engineering at the University of Michigan. His research focuses on system informatics and control for manufacturing and service systems, with notable contributions to quality improvement, cyber-physical systems, and data-driven methodologies. B.S. & M.S. in Electrical Engineering, Beijing Institute of Technology (1984–1987) Ph.D. in Mechanical Engineering, University of Michigan (1992) Dr. Shi’s research interests include process modeling, control systems, and quality engineering. He pioneered methodologies for in-process quality improvement and developed advanced frameworks for high-dimensional data analysis in manufacturing. His work integrates statistical methods, machine learning, and system informatics to enhance operational efficiency and product quality. He has published over 150 peer-reviewed papers and secured $19 million+ in research grants from NSF, DOE, and industry partners. His lab, the System Informatics and Control Group, collaborates with automotive, aerospace, and pharmaceutical sectors. Shi leads initiatives such as the Quality Science Center at the Chinese Academy of Sciences and serves on editorial boards of journals like IIE Transactions and ASME Transactions . Recipient of the IIE Albert G. Holzman Distinguished Educator Award (2011) Fellow of INFORMS, ASME, and IIE Academician of the International Academy for Quality Shi advises 26 Ph.D. graduates, many of whom hold faculty positions or leadership roles in industry. His research group’s innovations have been implemented in global manufacturing systems, yielding significant economic impacts. Current work includes 4D printing, cyber-physical system resilience, and federated learning for industrial data.

Zhaohai Liمشاهده پروفایل

استاد

• Genetic Epidemiology
• Statistical Methods for Meta-analysis
• Clinical Biostatistics
• +۳ مورد دیگر

Dr. Zhaohai Li Professor of Statistics at George Washington University, specializing in statistical methodologies for genetic epidemiology and clinical biostatistics. His research focuses on meta-analysis techniques, empirical Bayes methods, and population-based study designs. He has contributed extensively to improving statistical approaches in clinical trials and addressing challenges in genetic association studies. Education: Ph.D. in Statistics, Columbia University, 1989 Research Interests: His work addresses critical issues in modern biostatistics including: Population stratification in genetic studies Hardy-Weinberg equilibrium testing Optimal experimental design for case-control studies Handling missing data in genetic linkage analysis Development of robust statistical tests for complex survey data Publications Overview: Dr. Li's recent work emphasizes methodological advancements in: Bayesian approaches to population genetics Meta-analytic frameworks for combining study results Statistical solutions for multi-stage clinical trials Algorithmic improvements for genome-wide association analyses Professional Contributions: His articles consistently address practical challenges in biomedical research, bridging theoretical statistics with real-world genetic and clinical applications.

Karen Bandeen-Rocheمشاهده پروفایل

استاد

• Biostatistics
• Gerontology
• Aging
• +۱۴ مورد دیگر

Karen Bandeen-Roche is a Professor and the Hurley-Dorrier Professor and Chair of the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, with joint affiliations in the School of Medicine and the School of Nursing. She is a leading expert in biostatistical methodology, particularly in latent variable models, longitudinal analysis, and multivariate survival methods applied to aging and gerontology. Her research focuses on developing statistical models for unobservable processes such as frailty, resilience, and functional status in older adults. She has made significant contributions to the measurement of aging-related constructs and has extensive collaborative work in ophthalmology and neurology. Her methodological work includes mixture models, measurement error correction, and latent class modeling. The recent publications highlight a strong trend in gerontological biostatistics, with a focus on frailty, dementia risk, resilience, and multisystem physiological responses in aging. Her work integrates complex data from observational cohorts and clinical studies, often employing innovative latent variable frameworks to address measurement challenges in health outcomes. Scientific Awards and Honors: Marvin Zelen Leadership Award in Statistical Science (2016) Fellow of the American Statistical Association (2001) Brookdale National Fellow (1997) Golden Apple Award for Excellence in Teaching (2010) Garland Clay Award (1999) Chair, NIH BMRD Study Section (2006–2008) President, Eastern North American Region, International Biometric Society (2011–2013) Executive Board, International Biometric Society (2015–2022) Board of Directors, National Institute of Statistical Sciences (2020–2023) Karen Bandeen-Roche has been deeply involved in advising and training the next generation of researchers. She co-directs a training program in Biostatistics and Epidemiology of Aging and has received multiple teaching and mentoring awards. She has served on numerous academic committees, including appointments and promotions, faculty senate, and ethics committees at Johns Hopkins. Her grants and collaborative research span aging, dementia, ophthalmology, and cardiovascular health, often supported by NIH and other federal agencies. She leads the Center on Aging and Health and is actively involved in interdisciplinary research initiatives that bridge biostatistics, medicine, and public health. Her lab and research team focus on developing and applying advanced statistical methods to understand the biological and social determinants of healthy aging.

• Survival Analysis
• Longitudinal Data Analysis
• High-Dimensional Data Analysis
• +۸ مورد دیگر

Bin Nan serves as Chancellor's Professor in the Department of Statistics at the University of California, Irvine, where he develops statistical and machine learning methodologies to advance biomedical research and improve human health outcomes through rigorous data analysis. His educational credentials demonstrate a strong quantitative foundation: Ph.D. in Biostatistics, University of Washington (2001) M.S. in Biostatistics, University of Washington (1999) M.S. in Statistics, Virginia Commonwealth University (1997) M.S. in Aerospace Engineering, Beijing University of Aeronautics & Astronautics (1987) B.S. in Aerospace Engineering, Beijing University of Aeronautics & Astronautics (1984) Nan's research program focuses on developing cutting-edge statistical methods for survival analysis, longitudinal data, high-dimensional inference, and machine learning, with direct applications to epidemiology, bioinformatics, and brain imaging. His work addresses critical challenges in biomedical data such as temporal dependence in neuroimaging sequences, estimation of large correlation matrices, and analysis of disease onset with terminal events, all aimed at identifying biomarkers for earlier disease diagnosis. Analysis of his recent publications (2015-2023) reveals a consistent trajectory toward methodological innovation in handling complex biomedical data structures, particularly through de-biased lasso techniques for survival models, neural network applications to censored data, and specialized approaches for longitudinal data with terminal events. These advances predominantly support Alzheimer's disease research and transplant outcome studies. No specific scientific awards were documented in the source material. His research program maintains continuous funding through National Science Foundation and National Institutes of Health grants, including a recent $1.8 million award for Alzheimer's disease methodology development. Nan actively collaborates with the UCI Alzheimer's Disease Research Center and UCI Center for the Neurobiology of Learning and Memory, though student advising details were not provided. His teaching portfolio includes advanced graduate courses in probability theory, survival analysis, and high-dimensional inference. Nan operates within interdisciplinary biomedical research teams focused on translating statistical innovation into clinical applications, particularly through brain imaging analysis and biomarker identification for neurodegenerative diseases.

• Computational Statistics
• Causal Inference
• Machine Learning
• +۳ مورد دیگر

Niels Richard Hansen is a Professor at the Department of Mathematical Sciences , University of Copenhagen, leading research at the intersection of Artificial Intelligence and Statistics . He co-founded the Copenhagen Causality Lab and focuses on automating causal explanation discovery from data using Bayesian networks, stochastic processes, predictive models, and machine learning. His work emphasizes creating interpretable and robust AI systems capable of generalizing across domains. His research has produced over 56 publications spanning causal inference , graphical modeling , stochastic processes , and machine learning . Recent work includes: Predictive and causal learning (2018 keynote) High-dimensional regression solutions (2016 lecture) Interdisciplinary applications in actuarial science , environmental statistics , and neuroscience He actively contributes to scientific communication through media appearances and public explanations of statistical concepts, including analyses of: Gaussian correlation inequality proofs Daylight saving time and blood clots Mathematical approaches to lotteries Climate change vs lunar effects

Aurélie Labbeمشاهده پروفایل

استاد

• Data Science
• Statistical Learning
• Machine Learning
• +۷ مورد دیگر

Aurélie Labbe is a Full Professor in the Department of Decision Sciences at HEC Montréal, holding the prestigious FRQ-IVADO Chair in Data Science. Appointed as Co-Scientific Director – Academic Partnerships at IVADO in October 2023, she plays a key leadership role in establishing connections between IVADO and partner universities. Her academic journey includes a PhD in Statistics from the University of Waterloo, a Master's degree in Statistics from the University of Montreal, and dual Bachelor's degrees in Applied Mathematics and Social Sciences from Paris-Dauphine University and Pure Mathematics from Versailles-St Quentin University. Her research spans multiple interdisciplinary domains with a focus on developing advanced statistical and machine learning methodologies for big data analysis. Labbe's work bridges theoretical statistics with practical applications across diverse fields including genomics, neuroscience, transportation systems, and health informatics. She has made significant contributions to kernel methods, matrix factorization techniques, random forest applications, and spatiotemporal data analysis, with publications appearing in top journals across multiple disciplines. Analyzing her recent publications reveals a clear trend toward methodological innovation applied to complex real-world problems. Her work demonstrates expertise in handling high-dimensional data from diverse sources including neuroimaging, transportation networks, and genomic studies. The interdisciplinary nature of her research connects statistical theory with applications in healthcare, transportation safety, and biological sciences, reflecting her ability to develop methods that address domain-specific challenges while advancing statistical methodology. Holder of the FRQ-IVADO Chair in Data Science Member of the Center for Mathematical Research Training Professor Labbe actively mentors the next generation of data scientists, supervising numerous doctoral and master's students. Her supervision portfolio includes 1 doctoral thesis (2023), 4 master's theses (2022-2024), and 32 supervised projects spanning 2019-2025. Her students' work covers diverse applications including transportation safety, healthcare analytics, financial modeling, and environmental analysis. Through her leadership of the FRQ-IVADO Chair in Data Science, she coordinates research activities that integrate mathematical, statistical, and computer science expertise with domain knowledge from various data-generating fields. As Co-Scientific Director at IVADO, Professor Labbe leads efforts to establish connections with faculties and departments across five partner universities, integrating them into IVADO's research and knowledge transfer activities. Her leadership role positions her at the forefront of advancing data science research and applications in Quebec's academic ecosystem.

Tao Zouمشاهده پروفایل

دانشیار

• Covariance regression modeling
• Network data modeling
• Financial statistics
• +۳ مورد دیگر

Tao Zou is an Associate Professor at the Research School of Finance, Actuarial Studies and Statistics, Australian National University. His research spans covariance regression modeling, network data analysis, and applications in financial and environmental statistics. He earned a Ph.D. in Statistics in 2016. Ph.D. in Statistics, 2016 Dr. Zou’s work pioneers covariance regression, where covariances are modeled as functions of covariiates. Key contributions include robust estimation techniques, spatio-temporal modeling, missing data imputation via semi-supervised learning, and distributed data aggregation. His methods address challenges in high-dimensional and non-Euclidean data analysis. Recent publications (2025–2023) explore quasi-score matching for spatial autoregressive models, regularization in network regression, functional principal component analysis for complex data, and environmental applications like PM2.5 pollution studies. These works emphasize robustness, scalability, and interdisciplinary relevance in economics, finance, and environmental science. Dr. Zou collaborates on projects like the 2023 Data Analysis App to Empower Assessment of Immunogenicity of Biologics (Co-Investigator). While his student supervision list isn’t explicitly provided, his methodological advancements influence big data and spatial statistics. He contributes to open-access software and continues expanding covariance regression for non-normal and functional data.

Nikos Frantzikinakisمشاهده پروفایل

استاد

• Ergodic Theory
• Combinatorics
• Number Theory
• +۳ مورد دیگر

Nikos Frantzikinakis serves as a Professor in the Department of Applied Mathematics within the Theoretical Mathematics School at the University of Crete. His office is located in room C-307 with contact number 39-3853 and email frantzikinakis@uoc.gr. His academic credentials include: Ph.D., Stanford University, 2002 Professor Frantzikinakis specializes in ergodic theory, focusing on convergence and multiple regression theorems alongside analytical methods in combinatorics and number theory. His research bridges dynamical systems with additive combinatorics, particularly investigating recurrence phenomena in multiplicative functions and polynomial sequences. He has pioneered approaches connecting Furstenberg systems to number-theoretic structures, significantly advancing understanding of multiple recurrence in non-commuting transformations and partition regularity problems. Analysis of his recent publications reveals consistent exploration of ergodic-theoretic structures applied to combinatorial number theory. Key trends include recurrence along generalized Pythagorean triples, joint ergodicity for commuting transformations, and partition regularity of quadratic forms. His work demonstrates sophisticated integration of harmonic analysis, nilsequence theory, and multiplicative function correlations, with notable emphasis on the Chowla conjecture and Erdős discrepancy problems through ergodic frameworks. He maintains active research within the Applied Mathematics Laboratory at the University of Crete, contributing to theoretical mathematics through both individual scholarship and collaborative academic infrastructure.

Roderick M. Rejesusمشاهده پروفایل

استاد

• Agricultural Economics
• Risk Management in Agriculture
• Crop Insurance Policies
• +۴ مورد دیگر

Roderick M. Rejesus is a Professor and Extension Specialist in the Department of Agricultural and Resource Economics at North Carolina State University. He holds a Ph.D. from the University of Illinois at Urbana-Champaign. His work focuses on applied production economics, agricultural risk management (including crop insurance and safety-net policies), and economic impacts of agricultural technologies. He teaches ECG 741: Agricultural Production and Supply in Fall semesters. Education: Ph.D. in Agricultural Economics, University of Illinois at Urbana-Champaign (2001) Research Interests: Dr. Rejesus examines policy-driven agricultural practices such as soil health management and cover crops, their resilience to climate extremes, and their economic implications. His work integrates empirical analysis of crop insurance mechanisms, yield risk, and environmental sustainability. He evaluates conservation programs (e.g., no-till practices) and their effects on land values and erosion. His studies often address global challenges like climate change and food security, with projects in the U.S., Philippines, and other regions. Recent Research Trends: His articles emphasize climate-smart agriculture, crop insurance dynamics under extreme weather, and policy incentives for sustainable practices. Key themes include quantifying yield sensitivity to environmental factors (e.g., warming temperatures), analyzing soil health benefits, and modeling production risk in agricultural systems. Advising & Grants: While specific grants or advisees are not listed, his role as an Extension Specialist suggests involvement in applied research partnerships with farmers and policymakers. His work often involves collaborative projects with international institutions. Labs/Teams: Affiliated with North Carolina State’s Extension programs and interdisciplinary teams focused on agricultural economics and policy.

Joseph L Pagliariمشاهده پروفایل

استاد بالینی

• Real Estate Investment
• Asset Pricing
• Strategic Leverage
• +۹ مورد دیگر

Joseph L. Pagliari is a Clinical Professor of Real Estate at the University of Chicago Booth School of Business. With over 40 years of industry experience, he focuses his research and teaching on issues broadly surrounding institutional real estate investment, analyzing important questions from rigorous theoretical and empirical perspectives. His educational background includes: Bachelor's degree in Finance from University of Illinois-Urbana (1979) MBA from DePaul University-Chicago (1982) PhD in Finance from University of Illinois-Urbana (2002) Professor Pagliari's research centers on asset pricing, strategic use of leverage, portfolio allocation, joint ventures, hedonic pricing, and option-pricing theory. His work specifically addresses the risk-adjusted performance of core and non-core funds, principal/agent issues in incentive fees, comparisons between REITs and private real estate, real estate's pricing and return-generating process, real estate's role in mixed-asset portfolios, and analysis of high-yield financing. His research demonstrates how real estate characteristics impact investment decisions, pricing mechanisms, and portfolio construction across different market conditions and time horizons. His recent publications show a strong focus on understanding leverage dynamics in real estate debt markets, analyzing real estate returns by investment strategy, and examining the role of real estate in mixed-asset portfolios. Pagliari's work on high-yield lending reveals how asset-level volatility significantly impacts mezzanine debt returns, while his research on investment horizons demonstrates how time horizon affects optimal real estate allocations in portfolios. His analysis of credit spreads across different leverage ratios provides critical insights into real estate debt pricing over nearly three decades. Professor Pagliari has received significant recognition for his contributions to real estate research: 2015 winner of PREA's James A Graaskamp Award (recognizing significant research contributions to the common body of knowledge) He actively contributes to the academic and professional real estate community through board memberships and presentations. Pagliari serves on the board of the Real Estate Research Institute (RERI) and previously served on the Real Estate Information Standards (REIS) board. He has presented his research at numerous industry events including ARES, AREUEA, NCREIF, NAREIM, PREA, and ULI, as well as at the Federal Reserve Bank of Atlanta and before a subcommittee of the House of Representatives. His views have also been published in popular press outlets including Barron's and The Wall Street Journal. Professor Pagliari is deeply involved in the real estate academic community, serving as editor of the Handbook of Real Estate Portfolio Management and contributing to numerous academic and professional associations including the American Real Estate Society (ARES), American Real Estate and Urban Economics Association (AREUEA), Homer Hoyt Institute (where he is a Hoyt Fellow), National Association of Real Estate Trusts (NAREIT), National Council of Real Estate Investment Fiduciaries (NCREIF), Pension Real Estate Association (PREA), and Urban Land Institute (ULI).