جست‌وجوی استاد و پژوهشگر

Sally Paganinمشاهده پروفایل

استادیار

• Bayesian Statistics
• Statistical Computing
• Statistical Genomics
• +۳ مورد دیگر

Sally Paganin is an Assistant Professor of Statistics at The Ohio State University, affiliated with the Department of Statistics within the College of Arts and Sciences. She joined the faculty in 2023 and holds a PhD from the University of Padova (2019). Her research focuses on Bayesian statistics, computational methods, and latent variable modeling, with recent emphasis on genomic data analysis for cancer detection and software development for hierarchical models. Her expertise spans Bayesian nonparametrics, statistical computing, and domain knowledge integration in modeling frameworks. She actively contributes to the NIMBLE project, an R-based platform for hierarchical modeling, and has developed open-source tools like the compareMCMCs package for MCMC efficiency analysis. Dr. Paganin serves as an Associate Editor for the software section of The New England Journal of Statistics in Data Science and previously served as Treasurer of j-ISBA (2021–2022). Her work bridges theoretical advancements with practical applications in healthcare and computational statistics. Key research themes include Bayesian model assessment, latent variable models, and statistical methods for complex data structures. Her publications reflect contributions to MCMC algorithms, semiparametric IRT models, and prior-driven clustering techniques.

Stefan Hoderleinمشاهده پروفایل

استاد

• Econometrics
• Big Data
• Panel Data Analysis
• +۳ مورد دیگر

Stefan Hoderlein is a Professor in the Department of Economics at Emory University. His expertise lies in econometrics, with a focus on nonparametric methods, panel data analysis, and structural models. He holds a PhD from Bonn University and the London School of Economics (2002), and a Diplom Volkswirt from Bonn University (1997). His research interests include advanced econometric techniques such as instrumental variable estimation, demand analysis, and random coefficient models. He has contributed to methodologies addressing unobserved heterogeneity, endogeneity, and identification challenges in economic data. His work often explores applications in consumer behavior, market structure, and policy evaluation. Recent research trends in his publications emphasize nonparametric identification strategies, panel data methodologies, and the integration of big data into econometric frameworks. His technical contributions include Stata modules for statistical testing and frameworks for analyzing aggregate demand and welfare effects. While no specific awards are listed, his extensive publication record reflects sustained scholarly impact in econometric theory and applied economics. Advising details and grant information are not explicitly provided in the sources, though his work often involves collaborative research teams. His office is located in the R. Rollins Building (R428), and he maintains an active academic website.

Thomas Lemieuxمشاهده پروفایل

استاد

• Labor Economics
• Econometrics
• Income Inequality
• +۴ مورد دیگر

Thomas Lemieux is a Professor at the Vancouver School of Economics within the Faculty of Arts at the University of British Columbia , where he has been affiliated since 1999. Previously, he taught at MIT and the Université de Montréal. Born in Quebec City, he earned his Ph.D. from Princeton University. Research Interests: His work focuses on labor economics and econometric methods , particularly analyzing earnings inequality , unionization effects , regression discontinuity designs , and educational returns . He employs advanced decomposition techniques to study wage dynamics across gender, immigration status, and sectoral divides. Scientific Awards: Fellow, Royal Society of Canada Fellow, Society of Labor Economists Research Fellow, Institute for the Study of Labor (IZA) Research Associate, National Bureau of Economic Research (NBER) Publications: He has published extensively in top journals like the Quarterly Journal of Economics , Econometrica , and Journal of Labor Economics , with recent work examining: Union wage premiums using matched employer-employee data Spillover effects of minimum wage policies Changes in task prices and occupational wages Top income dynamics in Canada Immigrant wage gaps across education sources Regression discontinuity identification challenges Canadian labor market responses to the Great Recession

• Structural Health Monitoring
• Civil Engineering
• IoT/Edge Computing
• +۵ مورد دیگر

Dr. Zhen Peng is a Research Fellow at Curtin University's School of Civil and Mechanical Engineering, part of the Faculty of Science and Engineering. He holds an ARC Early Career Industry Fellowship (2025–2028), focusing on developing cost-effective bridge monitoring systems using computer vision and edge computing in collaboration with Main Roads WA. His work bridges structural engineering, IoT/edge computing, and machine learning to enhance infrastructure safety. Dr. Peng earned his PhD from Curtin University (Chancellor's Commendation, 2022). His research emphasizes structural dynamics, nonlinear damage detection, and mobile crowdsensing frameworks for infrastructure monitoring. He has published extensively in top journals like Engineering Structures and Structural Control and Health Monitoring , receiving notable awards such as the 2023 Best Paper Award and a Gold Medal in the China Postdoctoral Innovation Competition. His current projects include deploying IoT-driven systems for real-time bridge condition assessment and training students via available 2025 PhD scholarships. Dr. Peng teaches courses in civil engineering and structural analysis, contributing to both academia and industry through innovation in smart infrastructure technologies.

Wei Dingمشاهده پروفایل

استاد

• Knowledge Discovery
• Data Mining
• Machine Learning
• +۷ مورد دیگر

Wei Ding is a Professor in the Department of Computer Science at the University of Massachusetts Boston (UMass Boston). She earned her Ph.D. in Computer Science from the University of Houston in 2008. From 2019 to 2023, she served as a Program Director at the National Science Foundation's Division of Information and Intelligent Systems (IIS), overseeing programs in Information Integration, Smart Health, Deep Learning Foundations, and Scalable Systems. Her research integrates knowledge discovery, data mining, and machine learning with applications spanning health sciences, astronomy, geosciences, and environmental sciences. She employs advanced techniques like spatio-temporal modeling, deep neural networks, and semantic analysis to address complex real-world problems such as disease subtyping, physical activity prediction, and environmental forecasting. Her work emphasizes interdisciplinary collaboration and societal impact. Analysis of her recent publications reveals a focus on AI-driven healthcare solutions (e.g., neuroimaging biomarkers, disorder diagnosis), fundamental ML advancements (e.g., generalization, GAN stability), and cross-domain applications (e.g., climate forecasting, animal behavior analysis). Recurring themes include low-data learning, interpretability, and scalable algorithms. Awards & Honors: IEEE Fellow (2023) NSF Director's Award (2022) WISAY Distinguished Woman in Science Award, Yale University (2019) AI for Earth Award (2018) Best Paper Awards (ICTAI 2011, ICCI 2010) Advising & Grants: She mentors PhD and Master’s students in the Knowledge Discovery Lab (KDLab), with alumni at institutions like Facebook, Google, and McKinsey. Her research is funded by NSF, NIH, NASA, and DOE, including: NIH R01: Predicting youth physical activity (2016) NSF EAGER: Machine learning for cancer subtyping (2017) NIH R01: Accelerometer/gyroscope data for activity estimation (2022) Leadership: She directs the KDLab and co-founded the Women in Sciences Club (WINS). She serves as Associate Editor for ACM TKDD, TIST, and KAIS journals.

Boyu Zhangمشاهده پروفایل

استادیار

• Medical Image Analysis
• Machine Learning
• Deep Learning
• +۴ مورد دیگر

Boyu Zhang is an Assistant Professor in the Department of Computer Science at the University of Idaho, part of the College of Engineering. He holds a Ph.D. in Computer Science & Technology from Harbin Institute of Technology (2016), an M.S. from the same institution (2009), and a B.S. from Jilin University (2005). His research focuses on medical image analysis, deep learning, and AI applications in healthcare. Key areas include breast cancer detection via ultrasound imaging, explainable AI, graph neural networks for multi-omics data integration, and materials science predictions using machine learning. His work emphasizes interpretability in AI systems, such as the Bi-RADS-Net series for breast cancer diagnosis and the development of sharpness-aware optimizers for medical imaging tasks. He also explores multi-task learning frameworks and novel neural network architectures like SepNet for directional data analysis. His contributions span medical imaging benchmarks (e.g., BUSIS dataset) and computational methods for materials property prediction.

Luis C. Herreraمشاهده پروفایل

استادیار

• Power Electronics Control
• DC Microgrids
• Power System Optimization
• +۵ مورد دیگر

Luis C. Herrera is an Assistant Professor in the Department of Electrical Engineering at the School of Engineering and Applied Sciences, University at Buffalo. Previously, he served as Assistant Professor at Rochester Institute of Technology (2016-2018) and as Research Engineer at University of Dayton Research Institute (2015-2016). PhD in Electrical Engineering (2015) from The Ohio State University BS in Engineering (2010) from University of Tennessee at Martin His research focuses on power electronics control , DC microgrids , fault detection via parameter estimation , and optimal energy utilization . Key projects include improving DC microgrid resiliency , time-scale separation control , and voltage balancer design for aerospace applications . Recent publications emphasize data-driven fault detection , unknown input observers , and quickest change detection in DC systems. He has received: NSF CAREER Award (2023) for DC microgrid control Two AFRL Summer Faculty Fellowships (2019, 2020) Current advisees include PhD students Richard Tarbell , Sarbajit Basu , and Patrick Johnson . Past advisees include Kaushik Gajula (now at Sonnen Inc.) and Lalit Marepalli (L&T Technologies).

Isaac Millerمشاهده پروفایل

استاد

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

J. Isaac Miller is a Professor and Department Chair in the Department of Economics at the University of Missouri. His research focuses on econometrics, time series analysis, energy economics, and climate change impact assessment. He has developed structural econometric models for climate and energy demand, with applications to policy evaluation and forecasting. Key research areas: Climate econometrics, mixed-frequency time series, energy demand modeling, and economic impacts of climate change. Recent publications highlight statistical frameworks for climate sensitivity analysis, energy consumption forecasting, and mitigation strategy optimization. Teaching includes graduate courses in econometric theory and advanced time series methods.

Sinan Yıldırımمشاهده پروفایل

پژوهشگر

• Bayesian Statistics
• Monte Carlo Methods
• Data Privacy
• +۳ مورد دیگر

Sinan Yıldırım is a Researcher in the Faculty of Engineering and Natural Sciences at Sabancı University, Turkey. His primary research focuses on Bayesian Statistics, Monte Carlo methods, and data privacy, with interdisciplinary applications in machine learning and signal processing. He holds a BSc and MSc in Electrical and Electronics Engineering from Boğaziçi University, followed by a PhD in Mathematical Statistics from the University of Cambridge. Postdoctoral research (2013-2015) at the University of Bristol’s School of Mathematics involved the EPSRC-funded project 'Intractable Likelihood: New Challenges from Modern Applications (i-like).' His work bridges theoretical statistics with practical problems in privacy, control systems, and energy optimization. Research interests emphasize Bayesian methodologies for privacy-preserving data analysis, dynamic modeling of complex systems, and stochastic optimization algorithms. Recent publications explore differential privacy in machine learning, Monte Carlo techniques for high-dimensional inference, and applications of Bayesian methods in robotics and energy systems. Advising and grants include contributions to multi-party resource sharing frameworks and privacy-aware algorithms. His work integrates computational methods with real-world challenges in engineering and policy modeling.

Chong Liuمشاهده پروفایل

استادیار

• Machine Learning
• Bayesian Optimization
• Bandit Algorithms
• +۳ مورد دیگر

Dr. Chong Liu is an Assistant Professor of Computer Science at the State University of New York at Albany (SUNY Albany) in the College of Nanotechnology, Science, and Engineering. He received his PhD in Computer Science from UC Santa Barbara in 2023 and completed a postdoctoral fellowship at the University of Chicago's Data Science Institute (2023-2024). His research focuses on Machine Learning and AI for Science, particularly Bayesian optimization, bandit algorithms, generative models, and AI applications in drug discovery. He has received the SUNY IITG/OER Impact Grant and serves as Associate Editor for IEEE-TNNLS, Area Chair for ICML/AISTATS, and editorial board reviewer for JMLR. PhD: UC Santa Barbara (2023), advised by Yu-Xiang Wang Postdoc: University of Chicago Data Science Institute (2023) Research Interests : Broad: Machine Learning, Optimization, AI for Science Specific: Bayesian optimization, Bandit algorithms, Active learning, Experimental design, Generative models, AI for drug discovery Applications: Binding affinity prediction, Drug screening, Policy optimization Recent Article Trends : His 2024-2025 publications focus on extending Bayesian optimization theory under practical constraints, quantum-accelerated bandit methods, and multi-objective optimization for drug discovery. Earlier works include private learning frameworks and human-in-the-loop systems. Scientific Awards : 2025: SUNY IITG/OER Impact Grant Professional Activities : Organized NeurIPS workshops on AI for Drug Discovery (2023, 2025), co-organizing INFORMS sessions, and serving on program committees for ICML, NeurIPS, ICLR, and AAAI. He has given invited talks at institutions including University of Chicago, UC Santa Barbara, and Genentech. Teaching : Teaching courses like Numerical Methods (CSI 401) and Machine Learning (CSI 436/536) with syllabi spanning 2024-2025 semesters.

Udo Seifertمشاهده پروفایل

استاد

• Stochastic Thermodynamics
• Non-equilibrium Statistical Mechanics
• Biophysics
• +۳ مورد دیگر

Udo Seifert is a Professor in the II. Institute for Theoretical Physics at the University of Stuttgart, part of Faculty 08. His research focuses on stochastic thermodynamics, non-equilibrium statistical mechanics, and entropy production in complex systems. He has contributed significantly to understanding Markov networks, thermodynamic inference, and the interplay between fluctuations and irreversibility. His work bridges theoretical frameworks with experimental techniques, such as single-molecule experiments and motor-bead assays. Key areas of interest include entropy estimation in partially accessible systems, localization of entropy production, and the development of model-free entropy estimators. His recent studies explore the thermodynamic uncertainty relation, active matter systems, and the dynamics of biochemical oscillators. He has published extensively on topics like nonequilibrium fluctuations in chemical reaction networks, driven systems, and the application of stochastic processes to biophysical systems. Seifert's research also extends to membrane mechanics, with studies on fluid vesicle shapes and membrane-mediated interactions. His work emphasizes the integration of theoretical models with experimental data, aiming to uncover fundamental principles governing non-equilibrium phenomena. Despite the absence of listed awards or students in the provided text, his prolific publication record underscores his influential role in advancing stochastic thermodynamics and related fields.

Yang Wangمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Domain Adaptation
• +۳ مورد دیگر

Dr. Yang Wang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on computer vision and machine learning, with a particular emphasis on domain adaptation, meta-learning, and privacy-preserving techniques. He actively advises prospective graduate students through a dedicated webpage outlining application procedures. Research interests include few-shot learning, test-time adaptation, and cross-modal applications such as handwritten text recognition and gaze estimation. His work explores how models can adapt dynamically to new domains using limited labeled data, with applications in crowd counting, medical data analysis, and cybersecurity. He also investigates privacy-preserving methods for deep learning models to protect user attributes and sensitive information. Recent publications highlight advancements in meta-auxiliary learning frameworks and efficient user adaptation techniques. His contributions span journals and conferences, showcasing innovations in both foundational machine learning methodologies and real-world applications.

Douglas Nychkaمشاهده پروفایل

استاد

• Spatial Statistics
• Nonparametric Regression
• Computational Statistics
• +۵ مورد دیگر

Douglas Nychka is a Professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines since 2018. He holds an emeritus position at the National Center for Atmospheric Research (NCAR), where he previously directed the Institute for Mathematics Applied to Geosciences (IMAGe) from 2004 to 2017. Nychka earned his PhD in Statistics from the University of Wisconsin-Madison and a BA in Mathematics (with Physics emphasis) from Duke University. His research focuses on spatial statistics, nonparametric regression, and computational methods for large datasets, particularly applied to environmental and geophysical problems. He has developed influential R packages like fields and LatticeKrig , which are widely used for spatial data analysis. Nychka received prestigious awards including the Jerry Sacks Award for Multidisciplinary Research (2004) and recognition as a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. His work bridges statistical theory, computational innovation, and real-world applications in climate science and renewable energy. His academic career includes 14 years as a faculty member at North Carolina State University and roles at the National Institute of Statistical Sciences. Nychka's research emphasizes spatial statistics for climate data, statistical downscaling, and uncertainty quantification, with contributions to solar radiation modeling and extreme event analysis. He actively engages in interdisciplinary projects, collaborating with experts in climatology, environmental science, and data science. Professional service includes roles on committees for the National Research Council and leadership in statistical societies. His teaching focuses on modernizing curricula to integrate data science with applied statistics. Nychka’s work is characterized by a commitment to open-source software and reproducible research, exemplified by his R package contributions.

Lucy Bastinمشاهده پروفایل

استاد

• Biodiversity Informatics
• Remote Sensing
• GIS (Geographic Information Systems)
• +۷ مورد دیگر

Lucy Bastin is a Professor in the School of Computer Science and Digital Technologies at Aston University, part of the College of Engineering and Physical Sciences. She holds academic roles since 2003, including leadership in the Digital Observatory for Protected Areas (DOPA) project at the European Commission. Her research focuses on biodiversity informatics, remote sensing, and citizen science, with applications in conservation planning and sustainable development. She advises PhD students on topics like GIS, remote sensing, and citizen observatories. Education: BSc Zoology (University of Nottingham), MSc GIS (University of Leicester), PhD in Spatial Population Ecology (University of Birmingham). She also holds a Postgraduate Certificate in Teaching and Learning in Higher Education. Research Interests: Essential Biodiversity Variables, metadata standards for citizen science, uncertainty in conservation models, disease mapping (e.g., MRSA), and environmental policy support. She developed the DOPA toolkit for protected area monitoring and co-authored the Bari Manifesto for biodiversity variables. Key Projects: DOPA Explorer 2.0, FLIERS EU project, EuroGEOSS initiatives Recent Awards: Midlands Women in Tech Finalist (2021), Best Paper Award (2018) Teaching: Software Engineering, Professional Ethics in Computing, GIS modules Labs/Teams: Part of the Computer Science Research Group and Aston Centre for Artificial Intelligence Research and Application. Collaborates with global partners on initiatives like BIOPAMA and the Green Deal Data Space.

• Sustainable Development
• Spatial Inequalities
• Climate Change
• +۶ مورد دیگر

Dr. Hamid Rabiei is an Assistant Professor in Geography at the School of History and Geography, Dublin City University. He holds a PhD in Spatial Planning and Urban Development from Politecnico di Milano, Italy. His research focuses on sustainable development, spatial inequalities, climate change, and remote sensing/GIS applications. He has secured prestigious grants like the Marie Skłodowska-Curie Fellowship and published extensively in top-tier journals such as Habitat International and Applied Geography . His work bridges engineering, social science, and data science to address global challenges like environmental degradation and urbanization patterns. Key research interests include smart cities, spatial composite indicators, gentrification, and social vulnerability to natural hazards. He has contributed to developing novel models like the Adaptive Inverse Distance Weighting (AIDW) for population estimation and the Ordered Geographically Weighted Averaging (OGWA) for composite indicators. Recent work includes spatial analysis of internal migration impacts in Iran, Afghan immigrant segregation in Tehran, and ensemble modeling of extreme temperatures. He serves on editorial boards of academic journals in environmental studies and urban planning. His research emphasizes policy relevance, collaborating with decision-makers to translate findings into actionable strategies. Publications span 2023-2025, reflecting interdisciplinary approaches to urban sustainability, disaster risk, and big data applications. Grants and fellowships highlight his international recognition, particularly in integrating geospatial techniques with socioeconomic analysis.