جست‌وجوی استاد و پژوهشگر

• Computer Networks
• Telecommunications
• Machine Learning
• +۴ مورد دیگر

Živko Bojović is affiliated with Singidunum University, holding a doctoral degree in Telecommunications and Signal Processing from the University of Novi Sad. His career includes roles as an engineer and manager in telecommunications companies like Telekom Srbija and PTT Serbia. He has extensive experience in academic publishing, with monographs on network technologies and textbooks on software-defined networking (SDN) and IoT applications. His research focuses on cybersecurity, machine learning applications in healthcare, smart government systems, and network virtualization. Education: PhD in Telecommunications (2011), Postgraduate studies in Telecommunications (2000-2001), Bachelor’s in Electronics with Telecommunications (1986-1992). He also completed high school in Peć with a mathematical focus. Research Interests: He explores advanced networking technologies including SDN, 5G/6G networks, IoT security, and educational technology innovations. His work bridges theoretical advancements with practical implementations in healthcare diagnostics and public sector digitization. Recent studies include applying machine learning to predict thrombosis and thyroid cancer metastasis, reflecting his interdisciplinary approach. His articles collectively highlight trends in smart systems (e.g., government services, energy grids) and network security, with frequent contributions to journals like Computer Applications in Engineering Education and Journal of Network and Systems Management . The Wiley Top Cited Article award (2020-2021) recognizes his impactful work on rapid transition to distance learning during crises. Prof. Bojović holds IEEE membership and sits on editorial boards for journals like Inventi Impact: Software Engineering . He actively participates in EU Horizon projects focusing on 6G privacy and smart city analytics.

Armine Bagyanمشاهده پروفایل

عضو هیئت علمی

• Limit theorems
• Probability inequalities for random positive definite matrices
• Dimension reduction
• +۴ مورد دیگر

Armine Bagyan is an Associate Teaching Professor of Statistics at Pennsylvania State University's Department of Statistics. She holds a Ph.D. in Statistics from Penn State (2015) and an M.S. in Mathematics from Moscow State University. Her research focuses on limit theorems, probability inequalities for random positive definite matrices, and dimension reduction. She serves as the Department Coordinator for the Graduate Minor in Statistics and is involved in undergraduate advising and university committees. Education: Ph.D. in Statistics, Penn State University, 2015 M.S. in Mathematics, Moscow State University Her work bridges theoretical probability and high-dimensional statistical analysis, with applications in matrix distributions and stochastic processes. Recent publications explore asymptotic expansions in Bingham distributions and modeling disease spread via continuous-time Markov chains. Dr. Bagyan advises Statistics majors and Schreyer Honors College students, and teaches courses including Probability Theory, Mathematical Statistics, and Applied Regression Analysis.

• Geotechnical Engineering
• Finite Element Analysis
• Soil Mechanics
• +۶ مورد دیگر

Murat HAMDERI is a Professor at the Department of Civil Engineering, Faculty of Engineering, Turkish-German University since 2024. He previously held roles as Assistant Professor (2015–2020) and Associate Professor (2020–2024) at the same institution. His academic journey includes a Ph.D. from Drexel University (2010), focusing on geotechnical engineering, following M.Sc. and B.Sc. degrees from Boğaziçi University and Istanbul Technical University, respectively. Key affiliations include: Supervisory Engineer at Istanbul Regional Directorate of State Hydraulic Works (2000) Senior Consultant at a Dutch geotechnical firm (2012–2015) Consultant for the Akkuyu Nuclear Power Plant (2012) Research/Teaching Assistant at Drexel University (2004–2010) Research interests span geotechnical engineering, finite element analysis, soil mechanics, and foundation engineering. His work emphasizes retaining wall design, soil-structure interaction, and numerical modeling of geotechnical systems. He has conducted studies on colloidal silica applications for soil improvement and seismic response of reinforced soil structures. Recent publications focus on displacement methods for cantilever walls, p-y curves in sand, and lateral earth pressure coefficients, published in journals like Applied Sciences and International Journal of Geomechanics . No scientific awards or grants are explicitly mentioned in the provided text.

Carlo Orsiمشاهده پروفایل

استادیار

• Non-Central Probability Distributions
• Gaussian Process Regression
• Change Point Analysis
• +۶ مورد دیگر

Carlo Orsi is an Assistant Professor in Statistics at IMT School for Advanced Studies Lucca since November 2023. He holds a Ph.D. in Statistics (2014) from the University of Milan-Bicocca, preceded by a Master's (2011) and Bachelor's (2005) in Statistics from the same institution. Education: Bachelor's in Statistics, University of Milan-Bicocca (2005) Master's in Biostatistics & Experimental Statistics, University of Milan-Bicocca (2011) Ph.D. in Statistics, University of Milan-Bicocca (2014) His research focuses on non-central probability distributions over the unit simplex, multi-output Gaussian process regression, change point analysis, and applied statistics in tourism, medicine, and engineering. He has held adjunct professor roles at multiple universities, including teaching Mathematical Analysis and Probability, and served as a research collaborator at institutions like the National Research Council. His recent publications explore theoretical advancements in non-central distributions, with implications for statistical computation and simulation. He has held diverse roles in academia and industry, including statistical work at pharmaceutical and market research firms, and organizational roles like membership in the Local Organizing Committee for an international statistical conference (2008).

Silvia Novoمشاهده پروفایل

استادیار

• Functional Data Analysis
• Semiparametric Regression
• Regularization Methods
• +۲ مورد دیگر

Silvia Novo is an Assistant Professor at the Department of Statistics of Universidad Carlos III de Madrid. She holds a PhD in Statistics and Operations Research from Universidade da Coruña (2021) and previously obtained a Mathematics degree and MSc in Statistical Techniques from Universidade de Santiago de Compostela. As an Associate Editor of Computational Statistics, her research focuses on methodological advancements in functional data analysis, semiparametric regression, regularization techniques, quantile regression, and computational statistics. Education: Mathematics Degree, Universidade de Santiago de Compostela MSc in Statistical Techniques, Universidade de Santiago de Compostela PhD in Statistics and Operations Research, Universidade da Coruña (2021) Her publications demonstrate expertise in statistical learning with functional data, including novel approaches for combining partial-linear/single-index models with k-NN estimation, sparse regression for mixed functional/high-dimensional predictors, and automated location-adaptive estimation techniques. Recent work applies these methods to real-world problems like Tecator data analysis, emphasizing computational efficiency and interpretability. Editorial Role: Associate Editor at Computational Statistics.

Jennifer Bobbمشاهده پروفایل

دانشیار

• Clinical Trials
• Environmental Biostatistics
• Spatial Statistics
• +۵ مورد دیگر

Jennifer Bobb is an Affiliate Associate Professor in the Department of Biostatistics at the University of Washington and an investigator at Kaiser Permanente Washington Health Research Institute (KPWHRI). Her research focuses on statistical methods for addressing challenges in using administrative health data, pragmatic clinical trials for addiction treatment, and environmental health studies. She holds expertise in spatial confounding analysis and multi-pollutant mixture modeling, contributing to public health initiatives like the Moving to Health study examining built environment impacts on obesity and diabetes. Dr. Bobb's education includes a PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health (2012) and a BA in Mathematics from Washington University in St. Louis. Her work bridges methodological innovation with applied research, including developing statistical tools for EHR-based trials and environmental exposure studies. Her research interests span clinical trials methodology, environmental epidemiology, and high-dimensional data analysis. Notable contributions include leadership in the PROUD trial evaluating nurse-led OUD treatment models and methodological advancements in spatial epidemiology. She received the 2024 COPSS Emerging Leader Award for her statistical leadership. Key Projects: PROUD Trial, Moving to Health Study, Pragmatic Clinical Trials Awarded: COPSS Emerging Leader Award (2024) Editorial Roles: Associate Editor, Biostatistics Journal Professional Service: Chair, ASA Biometrics Section (2024) Her work emphasizes translating statistical insights into actionable public health strategies, particularly in addiction medicine and environmental health equity.

Carlos Sotoمشاهده پروفایل

استادیار

• Differential Privacy
• Bioinformatics
• Statistical Analysis
• +۳ مورد دیگر

Carlos Soto is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on the intersection of differential privacy, bioinformatics, and statistical analysis, with an emphasis on genomic data, geometric modeling, and privacy-preserving techniques. He is recognized for award-winning teaching and has contributed to interdisciplinary projects involving chromosome structure analysis and Bayesian statistical methods. His research interests include developing mathematical frameworks for privacy in data analysis, analyzing three-dimensional chromosome conformations, and applying geometric and algebraic methods to biological problems. His work often bridges theoretical statistics with computational biology, addressing challenges in genomic data interpretation and structural bioinformatics. Key trends in his publications include advancements in differential privacy for healthcare and genomic data, Bayesian approaches to topological analysis of genomes, and statistical methods for comparing chromosomal shape populations. He has also explored applications of shape analysis in power systems fault detection and incremental learning. Award-winning teaching in mathematics and statistics Advising & Grants: No advising or grant details explicitly provided in the text. Labs/Teams: No specific lab or collaborative team affiliations mentioned.

Yiguo Sunمشاهده پروفایل

استاد

• Econometrics
• Theoretical Econometrics
• Applied Econometrics
• +۸ مورد دیگر

Yiguo Sun is a Professor of Economics and University Research Leadership Chair at the University of Guelph, Department of Economics and Finance. She specializes in econometrics, focusing on semi-/nonparametric methods for panel data, non-stationary time series, and spatial regression models. Her research addresses issues such as investment dynamics, social interactions, and threshold effects. She holds a B.Sc. and M.Sc. from Hebei Normal University, an M.A. from the University of Guelph, and a Ph.D. from the University of Toronto. Awards include the CBE Senior Research Fellow (2018-2021) and University Research Leadership Chair (2022-2025). Education: B.Sc. in Mathematics, Hebei Normal University (1993) M.Sc. in Mathematics, Hebei Normal University (1996) M.A. in Economics, University of Guelph (1997) Ph.D. in Economics, University of Toronto (2002) Research Interests: Dr. Sun’s work centers on advancing econometric methodologies, particularly in nonparametric and semiparametric frameworks. Key areas include: Threshold regression models and their applications in inflation dynamics and social interactions Spatial econometrics and panel data analysis Nonlinear estimation techniques addressing endogeneity and measurement error Economic growth and natural resource nexus Recent Contributions: Her 2024 paper on investment-uncertainty relationships introduced a novel estimator addressing endogeneity and measurement bias. The 2023 Social Threshold Regression advanced peer effect analysis through a spatial Durbin framework. Recent articles explore spatial spillovers in trade policies and Canadian inflation dynamics using threshold models. Awards: University Research Leadership Chair (2022-2025) CBE Senior Research Fellow in Spatial Econometrics (2018-2021) SSHRC Insight Grant (2022-2025) Grants & Advising: She leads SSHRC-funded projects on social networks and measurement errors in finance. Supervised students include Delong Li (investment dynamics), Chaoyi Chen (threshold estimation), and Hui Xiao (model averaging). Research teams focus on econometric theory and applied policy analysis. Labs/Teams: Active in the University of Guelph’s CBE research community, collaborating with Thanasis Stengos, Emir Malikov, and international scholars on spatial econometrics and nonlinear methods.

Joshua L. Warrenمشاهده پروفایل

دانشیار

• Spatial Statistics
• Spatiotemporal Modeling
• Bayesian Statistics
• +۷ مورد دیگر

Joshua L. Warren is an Associate Professor in the Department of Biostatistics at the Yale School of Public Health. His academic appointments include primary affiliation with Biostatistics and secondary affiliations with Climate Change and Health, Public Health Modeling, Yale Superfund Research Center, and Yale-BI Biomedical Data Science Fellowship. Dr. Warren received his Ph.D. in Statistics from North Carolina State University in 2011 and completed his postdoctoral training at the University of North Carolina at Chapel Hill. He has been a faculty member at Yale University since July 2014. Dr. Warren's research focuses on statistical methods in public health with an emphasis on environmental health problems. His work primarily involves: Introducing spatial and spatiotemporal models in Bayesian frameworks Studying associations between environmental exposures (particularly air pollution) and health outcomes Developing methods for analyzing data related to preterm birth, low birth weight, and congenital anomalies Applying statistical models in collaborative settings including epidemiology, geography, nutrition, and glaucoma research Theoretical work in spatial/spatiotemporal modeling and Bayesian nonparametrics His recent publications demonstrate expertise across diverse areas including environmental health, infectious disease modeling (particularly tuberculosis), reproductive health, and glaucoma research. His work frequently employs sophisticated spatial statistical methods to analyze complex public health data and identify critical windows of exposure to environmental hazards. Scientific Awards Coauthor, Kenneth Rothman Epidemiology Prize Paper (2018) First Author, Best Paper in Biometrics (2012) Dr. Warren serves as an Associate Editor for Statistics in Biosciences (2015-present) and previously for the Journal of the American Statistical Association - Theory and Methods (2018-2020). He has also served as a reviewer for organizations including the Health Effects Institute, Swiss National Science Foundation, and NIH. His research collaborations include frequent work with Nicole Deziel, Daniel Weinberger, Ted Cohen, Xiaomei Ma, Virginia Pitzer, and Yu Lan.

Carlo Gaetanمشاهده پروفایل

استاد

• Environmental Statistics
• Spatial Statistics
• Extreme Value Theory
• +۳ مورد دیگر

Carlo Gaetan is a Full Professor at the Department of Environmental Sciences, Informatics and Statistics at Ca' Foscari University of Venice. His research focuses on statistical modeling for environmental applications, including extremes in climate data, spatial-temporal analysis, and environmental risk assessment. He contributes to editorial roles, such as Associate Editor of the Journal of the Royal Statistical Society, Series C. His work spans environmental statistics, spatial modeling, and applications in climate change and health. Gaetan is actively involved in projects like the Venice 2021 climate scenario initiative and collaborates on studies assessing air pollution impacts and disease modeling. His teaching includes office hours for students, emphasizing accessibility and academic guidance.

• Cognitive Neuroscience
• Neuroimaging
• Computational Neuroscience
• +۳ مورد دیگر

Professor Hinze Hogendoorn is a cognitive neuroscientist and head of the Timing Lab at Queensland University of Technology (QUT). He holds a PhD from Utrecht University and has held academic positions at Utrecht University (7 years as Assistant Professor), University of Melbourne, and Harvard Vision Sciences Laboratory. His research focuses on understanding how the brain processes time through computational methods, neuroimaging (e.g., EEG decoding), and behavioral approaches. Key interests include neural mechanisms of motion extrapolation, perceptual timing, and the real-time conscious experience. Affiliations: Faculty of Health, School of Psychology & Counselling at QUT. Education: PhD in Neuroscience from Utrecht University, Netherlands. Research emphasizes decoding neural representations of time and motion, with contributions to understanding illusions like the flash-lag effect. He has received an ARC Future Fellowship (2020) and engages in science communication through TV programs, TEDx, and executive education. Outreach includes popular science writing (e.g., The Conversation) and organizing the Brisbane Cognition Conference 2024. His work bridges neuroscience and applied psychology, addressing how brain delays influence perception and cognition.

• Applied Statistics
• Data Visualization
• Psychological Dynamic Models
• +۳ مورد دیگر

Prof. Dr. C.J. (Casper) Albers is a Professor of Applied Statistics and Data Visualisation and Dean of the Faculty of Behavioural and Social Sciences at the University of Groningen. He holds a PhD in mathematical statistics (2003) and has held academic positions including Research Fellow at The Open University (2005-2009) and Director of the Heymans Institute for Psychological Research (2020-2024). His research focuses on statistical models for psychological longitudinal data, data visualization, and improving scientific practice. He has published over 100 articles, supervised 13 PhD students, and secured nearly €2 million in research funding. He is an Elected Fellow of the International Statistical Institute and serves as President of the Netherlands Society for Statistics and OR. Key activities include Open Science advocacy, steering roles in initiatives like the Rudolf Agricola School, and media engagement as a statistical expert in over 200 media appearances. His work spans applied statistics, psychometrics, and environmental psychology, emphasizing dynamic behavioral modeling and statistical communication.

• Data Science
• Blockchain Technology
• Transportation Systems
• +۷ مورد دیگر

John Easton is a Professor in the School of Social Sciences at Heriot-Watt University, United Kingdom. His research bridges technology, data science, and transportation systems, with a strong focus on blockchain applications, data quality, and railway infrastructure. He actively contributes to advancements in smart cities and industrial information integration. Research Interests: His work centers on data-driven decision-making in industrial and transport contexts. Key areas include blockchain-based systems for public transit ticketing, data quality in industry, railway track monitoring, and digital transformation in transportation. His research supports UN Sustainable Development Goals through innovations in sustainable infrastructure and smart mobility. The recent publications indicate a strong trend in applying blockchain and data analytics to transportation and industrial systems. Topics such as decision-making frameworks for blockchain adoption, integrated data platforms for railway maintenance, and secure data sharing in smart cities reflect a cohesive research agenda focused on technological innovation for societal benefit. Scientific Awards: No specific awards are mentioned in the provided text. Advising and Grants: While no direct information on students or grants is available, his senior academic rank and extensive publication record suggest active supervision and research leadership. Collaborations with researchers like J.D. Preece and Q. Fu indicate team-based research projects likely funded by external grants. Labs and Teams: Although no specific lab or research group is named, his collaborative work and focus areas suggest involvement in interdisciplinary research teams working on smart transportation, blockchain applications, and industrial data systems at Heriot-Watt University.

Dimitris Korobilisمشاهده پروفایل

استاد

• Macroeconometrics
• Time-Series Analysis
• Forecasting
• +۶ مورد دیگر

Professor Dimitris Korobilis holds the rank of Professor of Econometrics at the Adam Smith Business School, University of Glasgow. He previously served as Professor of Finance at the University of Essex. His roles include directing the MSc in Data Analytics for Economics and Finance, co-ordinating the annual ASBS Summer School in Empirical Macroeconomics, and leading the macroeconomics research cluster. Education: PhD in Economics from the University of Strathclyde (2010). Research focuses on applied statistical inference in macroeconomic and financial data, emphasizing Bayesian methods, machine learning, and high-dimensional modeling. Notable contributions include developing algorithms for macroeconomic forecasting and policy analysis, with applications at institutions like the IMF and ECB. His work bridges econometric theory and practical policy tools. Teaching includes courses on Econometrics, Statistical Machine Learning, Time Series Forecasting, and Bayesian Data Analysis. Advises PhD students exploring topics like DSGE models and Bayesian microeconometrics. Publications span over 30 peer-reviewed articles in top journals such as the Journal of Econometrics and Journal of Business & Economic Statistics, with a focus on Bayesian VAR models, factor analysis, and macroeconomic risk monitoring.

Takayuki Mizunoمشاهده پروفایل

دانشیار

• Computational Social Science
• Econophysics
• Network Science
• +۱۲ مورد دیگر

Takayuki Mizuno is an Associate Professor at the National Institute of Informatics and SOKENDAI (The Graduate University for Advanced Studies), where he leads interdisciplinary research at the intersection of informatics, econophysics, and computational social science. His work focuses on analyzing complex global systems such as economic networks, financial markets, human mobility, and geopolitical dynamics using big data and network science. Doctor of Science, Chuo University (Physics) JSPS Research Fellow (2004) Lecturer, Institute of Economic Research, Hitotsubashi University (2007–2010) Associate Professor, University of Tsukuba (2011–2013) Senior Research Fellow, Canon Institute for Global Studies (2009–present) His research interests include computational social science, econophysics, network science, wealth inequality, financial bubbles, cryptocurrency regulation, public opinion manipulation, and global supply chain ethics. He develops models to understand market dynamics, detect hidden financial influences, and visualize geopolitical power shifts. The recent articles highlight a consistent trend in applying big data and network analysis to real-world socio-economic problems: visualizing China's indirect control in global shareholder networks, detecting financial risks, analyzing online market competition, studying supply chain ethics, and understanding cultural integration through human mobility. The work bridges physics, economics, computer science, and political science. Scientific Awards: Best Paper Award, Japanese Society for Artificial Intelligence (2019) Best Paper Award, Information Processing Society of Japan (2019, 2013) Student Encouragement Award, IPSJ (2019) Best Paper Award, Transdisciplinary Federation of Science and Technology (2019) JSPS Research Fellow (2004) Dr. Mizuno advises multiple Ph.D. and master’s students and leads the Mizuno Laboratory, which conducts cutting-edge research on global challenges. He has secured significant research grants from JSPS, JST (Sakigake), and other funding bodies. His work is frequently featured in major media outlets including Nikkei, NHK, The Wall Street Journal, and Yomiuri Shimbun, indicating broad societal impact. He is actively involved in professional service, including as Director of the Society for Computational Social Science of Japan and member of the Editorial Board of the Japanese Society for Artificial Intelligence. He also participates in government advisory committees on big data, economic policy, and telecommunications. His lab, the Mizuno Laboratory, focuses on the fusion of political economy and data science, aiming to disentangle complex global networks to address issues like economic bubbles, terrorism, and social polarization.