جست‌وجوی استاد و پژوهشگر

Keisuke Hiranoمشاهده پروفایل

استاد

• Econometrics
• Statistical Decision Theory
• Causal Inference
• +۳ مورد دیگر

Keisuke Hirano is a Professor of Economics at the Department of Economics, Pennsylvania State University, within the College of Liberal Arts. He holds a Ph.D. in Economics from Harvard University (1998) and a B.A. in Economics (Summa Cum Laude) from Yale University (1992). His research focuses on statistical decision theory, causal inference, and econometric methodologies, with applications to experimental design and forecasting. He has taught courses such as Econometrics I, Forecasting, and Topics in Econometrics at both undergraduate and graduate levels. His research interests emphasize the intersection of statistical theory and applied economics, particularly in addressing challenges in model uncertainty, external validity of experiments, and sequential decision-making. Recent work includes contributions to adaptive experimental design and cross-validation techniques under structural instability. Hirano has been recognized with awards including the Mitchell Prize (2001) for his work on assessing influenza vaccine effects and the Zellner Thesis Award (1999) for his doctoral dissertation. His publications span leading journals like Econometrica , Journal of Econometrics , and Quantitative Economics , reflecting his expertise in econometric theory and applied research. He advises on grants related to statistical decision rules in econometrics and collaborates with institutions globally. His academic contributions also extend to editorial work and mentoring in the field of econometrics.

Nail Kashaevمشاهده پروفایل

دانشیار

• Econometric Theory
• Industrial Organization
• Applied Econometrics
• +۲ مورد دیگر

Dr. Nail Kashaev is an Associate Professor in the Department of Economics at Western University. He holds a PhD from Penn State University (2016) and specializes in econometric theory, industrial organization, and discrete choice modeling. Research focuses on: Structural estimation of discrete choice models Behavioral econometrics with limited consideration Dynamic stochastic decision processes Social interactions in economic decisions Identification strategies for latent variable models His methodological innovations address identification challenges in models with unobserved choice sets and latent covariates. Recent work develops frameworks for analyzing peer effects in consideration sets and preferences, with applications to industrial organization and behavioral economics. Dr. Kashaev maintains active research collaborations across North America and Europe. He teaches graduate courses in Advanced Econometrics and Econometric Theory, and undergraduate courses in Econometrics and Quantitative Methods.

Mohammad Nayeem Teliمشاهده پروفایل

مدرس ارشد

• Computer Vision
• Machine Learning
• Data Science
• +۳ مورد دیگر

Mohammad Nayeem Teli is a Senior Lecturer in the Department of Computer Science at the University of Maryland, College Park. He holds a PhD and M.S. in Computer Science from Colorado State University and a B.E. in Electronics and Communications from National Institute of Technology, Srinagar. His research focuses on Computer Vision, Machine Learning, and Data Science, with notable projects in vaccine design using protein language models, memory-efficient diffusion models, assistive technology for visually impaired individuals, and robotic navigation in dynamic environments. He has taught courses such as Introduction to Machine Learning , Computer Vision , and Data Science , winning the Excellence in Teaching Award (2018-2019). His work includes collaborations with institutions like Harrisburg University and National Institute of Technology, Srinagar, where he previously served as an Assistant Professor. He is an IEEE Senior Member and has received awards like the Best Oral Presentation Award at IEMTRONICS 2021. His research spans vaccine design with protein models, medical imaging optimization, and assistive robotics. Current projects explore invertible diffusion models for 3D medical imaging and LiDAR-based navigation aids for the visually impaired. Publications highlight contributions to adversarial robustness in autonomous vehicles, face recognition challenges, and resource allocation in online gaming systems.

John Chaoمشاهده پروفایل

استاد

• IV Regressions with Many Instruments
• Bayesian Econometrics
• Model Selection Methods in Nonstationary Time Series Analysis
• +۳ مورد دیگر

John Chao is a Professor of Economics at the University of Maryland, affiliated with the Department of Economics within the College of Behavioral and Social Sciences. He holds a PhD in Economics from Yale University (1994) and has been a faculty member since 1995. His research focuses on advanced econometric methodologies, particularly instrumental variable (IV) regressions with many instruments, Bayesian econometrics, and model selection in nonstationary time series analysis. He has also taught at Pennsylvania State University and held visiting positions at Yale University and Shanghai University of Finance and Economics. Research Interests : - IV Regressions with Many Instruments - Bayesian Econometrics - Panel Data Models - Time Series Econometrics - Model Selection in Nonstationary Environments Teaching : Chao has instructed courses such as Econometrics III (ECON721), Workshop in Econometrics (ECON828), and Advanced Topics in Econometrics (ECON423). His lectures emphasize theoretical foundations and practical applications in econometric analysis. Publications : Chao's work spans influential contributions to econometric theory, including advancements in IV estimation under weak instruments, factor models, and forecasting techniques. Key themes include addressing heteroskedasticity, panel data modeling, and the application of Bayesian methods in structural modeling.

Nicole Lazarمشاهده پروفایل

استاد

• Statistical Inference
• Functional Neuroimaging Data Analysis
• Model Selection
• +۹ مورد دیگر

Nicole Lazar is the Department Head and Professor of Statistics at Pennsylvania State University. She holds a PhD from the University of Chicago (1996), an MS from Stanford University (1993), and a BA in Psychology and Statistics from Tel Aviv University (1988). Her research focuses on foundational statistical inference, neuroimaging data analysis, and topological data science, with applications in cognitive neuroscience, climatology, and psychology. She pioneered statistical methods for fMRI analysis and has developed interdisciplinary approaches at the intersection of statistics, mathematics, and computer science. Her professional service includes roles as Editor of *The American Statistician*, Associate Editor for multiple journals, and co-Editor of a Special Issue on 'Statistical Inference in the 21st Century.' Lazar authored the textbook *The Statistical Analysis of Functional MRI Data* (Springer) and has published extensively in top journals. She is a Fellow of the American Statistical Association (2014), Fellow of the Institute of Mathematical Statistics (2021), and Elected Member of the International Statistical Institute (2006). Previously, she served on faculty at Carnegie Mellon University and the University of Georgia, where she also served as Interim Department Head (2014–2016). Lazar’s teaching includes advanced courses like STAT 470W (Statistical Consulting), STAT 512 (Applied Nonparametric Statistics), and STAT 580/581 (Advanced Topics in Statistics). Her work emphasizes methodological innovations, reproducibility in science, and promoting gender equity in statistics through her presidency of the Caucus for Women in Statistics (2019).

Jung In Kimمشاهده پروفایل

استاد پژوهشی

• Survival Analysis
• Causal Inference
• Biostatistics
• +۳ مورد دیگر

Jung In Kim is an Assistant Research Professor in the Department of Statistics at Pennsylvania State University. Her research focuses on survival analysis, causal inference, and methods for time-to-event data, including recurrent events, unmeasured confounders with instrumental variables, and competing risks. She holds a Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill (2017), an M.S. in Statistics from UCLA (2010), and dual B.S./B.E. degrees in Mathematics and Statistics from Korea University (2008). She teaches courses such as STAT 184 (Introduction to R), STAT 414 (Probability), and STAT 509 (Design and Analysis of Clinical Trials). Her work emphasizes methodological advancements in biostatistics and epidemiology, with applications to cancer risk and cohort studies. Despite no explicitly listed awards, her publications reflect significant contributions to statistical methodologies for complex healthcare data.

Lorin Crawfordمشاهده پروفایل

پژوهشگر

• Machine Learning
• Genomics
• Cancer Biology
• +۵ مورد دیگر

Lorin Crawford is a Principal Researcher at Microsoft Research, focusing on developing interpretable machine learning and AI algorithms to study genetic effects and cancer genomics. He co-leads Project Ex Vivo with the Broad Institute to target cancer cell states. His work bridges computational biology, statistics, and genetics. Education: PhD in Statistical Science from Duke University (2017), advised by Sayan Mukherjee and Kris C. Wood. BSc in Mathematics from Clark Atlanta University. Awards include Sloan and Packard Fellowships, and the COPSS Emerging Leader Award. Research spans genomic analysis, 3D shape modeling, and AI-driven cancer pharmacology. Notable projects include Bayesian kernel models for cancer genomics and topological data analysis for biological shape differentiation. Publications emphasize scalable ML methods for single-cell biology, epistasis detection in GWAS, and interpretable AI frameworks like BAKR. Collaborations involve Broad Institute, MIT, and Harvard researchers.

• Transport of pollutants in the environment
• Fate and transport of nanomaterials
• Groundwater remediation
• +۴ مورد دیگر

Prof. Nadim Copty is a Professor at the Institute of Environmental Sciences, Bogazici University (2011–present), with earlier roles as Associate Professor (2005–2011) and Assistant Professor (2000–2005). His research focuses on pollutant transport, nanomaterial fate in the environment, groundwater remediation, porous media modeling, and air quality control. He holds a Ph.D. (1994) and M.E. (1990) from UC Berkeley's Civil and Environmental Engineering, and a B.E. (1988) in Civil Engineering from the American University of Beirut. Ph.D., UC Berkeley, 1994 M.E., UC Berkeley, 1990 B.E., American University of Beirut, 1988 His research interests include: Contaminant fate and transport in natural systems Groundwater remediation technologies Modeling multiphase flow in porous media Environmental impacts of nanomaterials Air pollution mitigation strategies Recent work highlights include studies on climate change impacts on groundwater levels, surfactant-enhanced DNAPL remediation, and sustainable water management in the Mediterranean. His projects often integrate experimental, numerical, and field-based approaches. Key projects funded by PRIMA, TÜBITAK, and IASS address groundwater sustainability, pesticide transport in Konya Plain, and soil salinity management. His research has contributed to policy-relevant tools like web-based decision support systems for water resource planning.

• Molecular Dynamics
• Computational Fluid Dynamics
• Metastable Systems
• +۳ مورد دیگر

Tony LELIEVRE is a Professor at Ecole des Ponts ParisTech, part of the Institut Polytechnique de Paris. He is a member of the CERMICS research center and the INRIA Matherials project. His work focuses on mathematical modeling, numerical analysis, and computational physics, particularly in molecular dynamics, metastable systems, and fluid mechanics. He holds a Ph.D. from Ecole des Ponts ParisTech (2004) and a Habilitation (2009). Research interests include micro-macro modeling of viscoelastic fluids, free surface flows, and stochastic processes in metastable systems. Notable awards include the ERC Consolidator Grant (2013-2019) and the Grand prix Alcan (2010). He teaches part-time at Ecole Polytechnique and has organized numerous international workshops on computational methods and statistical physics. Key contributions include advancements in adaptive biasing force methods, accelerated molecular dynamics, and quasi-stationary distributions. He serves on editorial boards of journals like SIAM/ASA JUQ and Foundations of Computational Mathematics.

• Partial Differential Equations
• Numerical Analysis
• Finite Element Methods
• +۷ مورد دیگر

Théophile Chaumont-Frelet is a junior researcher at Inria , working with project-team Rapsodi . Previously, he held post-doctoral positions at the Basque Center for Applied Mathematics (BCAM, Bilbao) and CERMICS (École des Ponts ParisTech). His research focuses on partial differential equations , numerical analysis , high-performance computing , and applications in wave propagation , electromagnetism , and geophysics . Education : Engineer in Mathematical Engineering from INSA Rouen (2012), Master in Fundamental and Applied Mathematics (Grade A+, 2012). His work addresses high-frequency Helmholtz and Maxwell equations in heterogeneous media, with emphasis on finite element methods , discontinuous Galerkin techniques , and multiscale modeling . Recent publications analyze frequency-explicit error bounds , polynomial-degree robustness , and stable local reconstructions . He has received the C3I label from GENCI (2017) for computational expertise. Notable collaborations include projects with Total, INRIA, and the Spanish Ministry of Economy (MTM2016-76329-R). Current PhD students and postdocs include Sumit Mahajan and Florentin Proust, while former advisees are Zakaria Kassali, Josselin Defrance, and Patrick Vega. His research grants involve M2NUM (Normandy/FEDER), DIP (INRIA MAGIQUE3D/Total), and the Spanish Ministry project MTM2016-76329-R.

• Remote Sensing
• Hydrology
• Water Resources Management
• +۳ مورد دیگر

Prof. Dr. Mehmet Cüneyd Demirel is a Professor in the Department of Civil Engineering at Istanbul Technical University, specializing in Remote Sensing, Hydrology, and Water Resources Management. He has held roles including Associate Professor (2019–2024) and Doctorate Lecturer, with post-doctoral research at institutions like the Geological Survey of Denmark and Greenland and Portland State University. Education includes a PhD from the University of Twente (2008–2014), an MSc in Hydroelectronics from Istanbul Technical University (2001–2004), and a BEng in Civil Engineering from Selçuk University (1997–2001). He has held research positions globally and contributed to projects such as the EU-funded SPACE project and NASA’s Rapid Response Earth Science grant. His research focuses on distributed hydrological modeling, climate change impacts, and remote sensing applications. Key projects include developing drought indices using AI, improving groundwater anomaly estimations, and optimizing pressure management in water distribution systems. He has received awards like the ITU Project Award (2024) and is an Associate Editor for Hydrological Sciences Journal and Stochastic Environmental Research and Risk Assessment . Notable contributions include over 40 publications, 15 supervised theses, and leadership in international collaborations. His work bridges hydrological modeling, remote sensing, and sustainable water management, addressing global challenges in water resources and climate resilience.

Elham Hosseinzadehمشاهده پروفایل

مدرس

• Energy Storage
• Thermal Management
• Electrified Transport Systems
• +۳ مورد دیگر

Elham Hosseinzadeh is a Lecturer in Energy Storage and Conversion at the University of York, affiliated with the Department of Electronic Engineering in the School of Physics, Engineering and Technology. Her research focuses on energy storage systems (batteries and fuel cells), thermal management, and electrified transport. She holds a Ph.D. from the Technical University of Denmark (2013) in mechanical engineering, specializing in PEM fuel cell modeling. Prior roles include Research Fellow at WMG, University of Warwick, and Lead Engineer at Dyson, where she addressed real-world battery manufacturing challenges. Her expertise spans fuel cell system optimization (e.g., for forklift trucks), battery thermal management for electric vehicles, and computational modeling of energy systems. She teaches courses like Renewable Power Generation and Fuel Cell and Battery Technologies. Research grants include EPSRC and HVM Catapult-JLR projects. Her work emphasizes interdisciplinary approaches, combining experimental analysis, numerical simulations, and multi-domain modeling to enhance energy storage efficiency and safety. She leads research on battery lifecycle management, novel cooling materials, and multi-objective optimization in automotive applications.

Jeroen Dalderopمشاهده پروفایل

استادیار

• Econometrics
• Financial Economics
• Time Series Analysis
• +۳ مورد دیگر

Jeroen Dalderop is an Assistant Professor of Economics at the University of Notre Dame, located in Jenkins and Nanovic Halls. His research focuses on econometric methods for modeling and forecasting economic and financial time series, with an emphasis on non- and semiparametric techniques applied to financial asset prices and their connections to the real economy. Education: Ph.D. in Economics, University of Cambridge (2018) M.Phil. in Economics, University of Cambridge (2013) M.Sc. in Quantitative Finance and B.Sc. in Econometrics & Operations Research, Tilburg University (2012) Research Interests: His work develops advanced statistical methodologies to analyze financial asset prices—including derivatives—and their relationship with macroeconomic variables. Key themes include probability weighting functions in option pricing, conditional density models for asset returns, and latent variable frameworks in asset pricing. Publications: Recent work emphasizes nonparametric approaches to pricing kernels, market-implied densities, and latent variable models. His research bridges econometric theory with practical applications in finance, reflecting a focus on rigorous methodological innovation. Advising & Grants: No specific advisees or grants are listed in the provided materials. His CV is available for further details. Labs/Teams: No dedicated lab or team affiliations are mentioned.

Aida Cuni Sanchezمشاهده پروفایل

پژوهشگر ارشد

• Tropical Forest Ecology
• Carbon Stocks
• Ethnobotany
• +۴ مورد دیگر

Aida Cuni Sanchez is a Research Fellow affiliated with the University of York (Department of Environment and Geography) and Colorado State University (Ecosystem Science and Sustainability). Her work bridges natural and social sciences, focusing on tropical forest ecology, carbon dynamics, and community-based conservation in Africa. She has held postdoctoral and research positions across institutions including the University of Copenhagen, University College London, and the University of Oxford, contributing to projects in over ten African countries. Her research emphasizes ecosystem services, socio-ecological resilience, and participatory approaches to forest management. Key projects include assessing carbon stocks in Central African forests and evaluating threats to montane ecosystems in Kenya and Cameroon. She advocates for science communication to local communities and policymakers, alongside capacity-building efforts through teaching and mentoring African PhD students. Notable contributions include studies on bushmeat hunting impacts, climate change adaptation strategies for pastoralists, and collaborative frameworks with networks like Mountain Sentinels and AfroMont. Her work integrates Bayesian modeling and stakeholder engagement to inform policy and conservation practices. Education: PhD in Environmental Sciences (University of Southampton), Biology (Barcelona University) Key Skills: Field ecology, biomass estimation, participatory research, multidisciplinary collaboration Current Projects: African montane forests' socio-ecological functioning and management strategies Publications emphasize carbon dynamics, ethnobotanical resource use, and community perceptions of ecosystem services, with a focus on policy-relevant outputs and open-access dissemination.

David H Kreitmeirمشاهده پروفایل

پژوهشگر ارشد

• Applied Economics
• Political Economy
• Environmental and Resource Economics
• +۸ مورد دیگر

David H. Kreitmeir is a Research Fellow at Monash University, currently affiliated with the Department of Economics (since July 2024). Previously, he was part of the SoDa Labs within the Monash Business School from September 2022 to July 2024. His research spans political economy, environmental economics, corporate governance, and digital economics , with methodological expertise in natural language processing, machine learning, geocomputation, and financial econometrics . His recent publications focus on: Political Economy : Accountability mechanisms for multinationals through civil society and media collaboration, state repression of socioenvironmental protests. Development Economics : Analysis of long-term socioeconomic impacts using mortality prediction instruments. Digital Economics : Productivity effects of generative AI in corporate contexts. He actively contributes to methodological advancements via the synthReturn R package, designed to estimate treatment effects on firm stock returns using synthetic control methods.