جست‌وجوی استاد و پژوهشگر

Dale J. Poirierمشاهده پروفایل

استاد

• Bayesian Econometrics
• Statistical Modeling
• Econometric Theory
• +۳ مورد دیگر

Dale J. Poirier is a Professor of Economics at the University of California-Irvine, where he is affiliated with the Department of Economics in the School of Social Sciences. His office is located in Social Science Plaza, Building A, Room 4175. He teaches courses including Econometrics I (123A), Statistics and Econometrics I (220A), Statistics and Econometrics II (220B), and the Econometrics Colloquium (229). Dale Poirier's research focuses on Bayesian econometrics and statistical modeling. His work spans theoretical developments in Bayesian inference, econometric methodology, and applications across various domains including health economics, political science, and time series analysis. He has made significant contributions to understanding the growth and application of Bayesian methods in economics and statistics since 1970, as documented in his influential 2006 paper 'The Growth of Bayesian Methods in Statistics and Economics Since 1970.' Analysis of Poirier's recent publications reveals a strong emphasis on Bayesian methods in econometrics, with applications ranging from partial identification in econometric models to analyzing violence dynamics in political conflicts. His work demonstrates both theoretical rigor and practical relevance, addressing challenges in model specification, identification, and statistical inference across diverse fields including health outcomes, political violence, and financial modeling. Dale Poirier has contributed significantly to econometrics education through his teaching and publications. His textbook 'Intermediate Statistics and Econometrics: A Comparative Approach' has been widely used, and he provides detailed course materials including syllabi and corrections for his courses. He has supervised numerous graduate students in economics and statistics, contributing to the development of the next generation of econometricians.

Jilin Huمشاهده پروفایل

مدرس

• Representation Learning
• Time Series Forecasting
• Contrastive Learning
• +۳ مورد دیگر

Jilin Hu serves as a Lecturer in the Department of Computer Science at Aalborg University's Technical Faculty of IT and Design. Dr. Hu is actively affiliated with the Data Engineering, Science and Systems research group, the AI for the People initiative, and the Daisy – Center for Data-intensive Systemer. Research focuses on representation learning and time series forecasting, with significant contributions in contrastive learning frameworks for trajectory data and crystal property estimation. Recent work demonstrates expertise in graph neural networks for spatiotemporal modeling, few-shot object detection, and foundation models for time series analysis. Key methodologies include zero-shot learning, self-supervised techniques, and diffusion probabilistic models applied to materials science and computer vision challenges. Dr. Hu leads the Villum Foundation project 'Inverse Design of Materials Using Diffusion Probabilistic Models' (2024-2026) as principal investigator and supervises one PhD student. Current research directions include developing diffusion models for glass-ceramic material design and benchmarking foundation models for time series forecasting applications.

• Cognitive Modelling
• Artificial Intelligence
• Decision-Making
• +۳ مورد دیگر

Dr. Leendert van Maanen is an Associate Professor at the Department of Experimental Psychology, Utrecht University, where he studies cognitive mechanisms underlying information processing and decision-making. His research focuses on developing and validating computational cognitive models, with applications in human-machine interaction and cognitive neuroscience data analysis. Research Themes: Cognitive Modeling, Decision-Making, Human-Centered Artificial Intelligence, Applied Data Science Labs: Leads the COBRA (Cognitive and Behavioral Research in Artificial Intelligence) lab His work spans three levels of cognitive integration in AI: model development, human-machine deployment, and neural data analysis. Recent projects include cognitive strategy detection, digital twin user modeling, and neural event detection using multivariate time series. For collaborations and publications, see leendertvanmaanen.com or Utrecht University Research Portal.

• Probability
• Statistics
• Extreme Events
• +۴ مورد دیگر

Anna (Ania) Panorska is a Professor at the University of Nevada, Reno within the Graduate Program of Hydrologic Sciences. She holds a Ph.D. in Mathematics (Statistics and Applied Probability program) from UC Santa Barbara (1992) and an M.S. in Statistics from UT El Paso (1988), with prior graduate coursework in Applied Mathematics at the University of Warsaw. Her career spans academia, research institutions (Desert Research Institute), industry (Blue Cross Blue Shield of Tennessee), and statistical consulting. Her educational background includes: M.S. in Statistics, University of Texas at El Paso, 1988 Ph.D. in Mathematics (Statistics and Applied Probability program), University of California, Santa Barbara, 1992 Professor Panorska's research centers on theoretical and applied probability and statistics, with specialized expertise in limit theorems and extreme event modeling within probability theory, and multivariate model development in statistics. Her work is characterized by extensive interdisciplinary collaboration across ecology, engineering, climate science, hydrology, and medicine. Key contributions include novel distributional frameworks for stochastic episodes, heavy-tailed data analysis, and extreme value applications in environmental systems. Analysis of her recent publications reveals a sustained focus on developing new probability distributions (e.g., slash, truncated Zipf, generalized Pareto variants) and their application to multivariate dependencies and extreme events. Her methodological innovations consistently bridge theoretical statistics with practical challenges in hydrology (precipitation extremes), climate science (intense weather events), and ecology (species interaction networks), demonstrating a cohesive trajectory in environmental statistics.

• Nonparametric Statistics
• High-dimensional Statistics
• Inverse Problems
• +۳ مورد دیگر

Cristina Butucea is a Full Professor of Statistics at ENSAE, Institut Polytechnique de Paris (IP Paris), and a Permanent Member of CREST (Center for Research in Economics and Statistics). She specializes in nonparametric and high-dimensional mathematical statistics, with research interests spanning inverse problems, quantum statistics, privacy of data, and machine learning. Her academic career includes faculty positions at several prestigious French institutions including Université Paris-Est Marne-la-Vallée and Université des Sciences et Technologies de Lille. Her educational background includes a post-doc at Humboldt University, Berlin (1998-1999), followed by an Assistant Professor position at Université Paris Nanterre (1999-2007). She was promoted to Professor at Université des Sciences et Technologies de Lille 1 (2007-2010), then at Université Paris-Est Marne-la-Vallée (2010-2016), and currently holds her position at ENSAE, IP Paris (2016-present). Professor Butucea's research focuses on theoretical statistics with applications in modern data science challenges. Her work on differential privacy has established fundamental limits and optimal procedures for statistical estimation under privacy constraints. She has made significant contributions to quantum statistics, particularly in quantum state estimation. Her research on high-dimensional statistics addresses variable selection, sparse structures, and nonparametric estimation in complex settings. She has also contributed to the theory of inverse problems and the analysis of locally stationary processes. Her recent publications demonstrate a strong focus on the intersection of statistics with privacy concerns, quantum information, and high-dimensional data analysis. She has published in top statistical journals including Annals of Statistics, Bernoulli, and Electronic Journal of Statistics. Her work often addresses fundamental questions about optimal rates of convergence, phase transitions in estimation problems, and the theoretical limits of statistical procedures under various constraints. Nominated IMS Fellow in 2019 CO-organizer of the Seminar of Statistics CREST-CMAP Associate Editor of ALEA (Latin American Journal of Probability and Mathematical Statistics) Organizer of several conferences in mathematical statistics and machine learning (Fréjus 2018, Luminy 2019, 2020, Oberwolfach 2021) Professor Butucea has received multiple research grants including ANR HIDITSA (2017-2021), ANR SPADRO (2013-2017), and ANR DIONISOS (2012-2016). She was the Principal Investigator of an ANR project on "Statistics for quantum physics" (2007-2008). She has also been awarded research stays at CIRM Luminy and MFO Oberwolfach. She is actively involved in the academic community as a member of the IMS (Institute of Mathematical Statistics) and the Bernoulli Society. She is also a member of the Institut des Actuaires as an Actuary ISUP.

Rimantas Rudzkisمشاهده پروفایل

استاد

• Probability Theory
• Mathematical Statistics
• Econometric Modeling
• +۳ مورد دیگر

Rimantas Rudzkis is an Affiliated Professor at Vilnius University's Institute of Mathematics and Informatics, specifically within the Interdisciplinary Statistical Research Group. He serves as the Head of the Applied Statistics Department and has been working at the Institute since 1978. His academic credentials include a Doctor of Mathematics from Vilnius University (1978), a Habilitation Doctor from the Institute of Mathematics and Informatics (1993), and Professor title from Vytautas Magnus University (1996). Dr. Rudzkis has extensive educational background, having graduated from Kaunas Polytechnic Institute in 1973 with a specialization in computing technology and engineering mathematics. His professional journey began as an assistant at KPI (1973-1974), followed by postgraduate studies at the Institute of Mathematics and Informatics (1974-1977). His research primarily focuses on probability theory , mathematical statistics , and econometric modeling . He has developed methods for data clustering, nonparametric density estimation, and created mathematical models of Lithuanian macroeconomic indicators using VAR methodology. His work spans theoretical developments in statistical decision algorithms and practical applications in economic forecasting. Analysis of his recent publications reveals a consistent focus on statistical methodology development, particularly in goodness-of-fit testing, multivariate analysis, and applications to economic and financial data. His research shows a clear progression from theoretical probability work toward increasingly applied econometric modeling, with significant emphasis on Baltic region economic analysis in recent years. Dr. Rudzkis maintains significant professional engagement through multiple editorial roles, including membership on the editorial boards of 'Lithuanian Mathematical Collection' (since 1999), 'Lithuanian Statistical Works' (since 2000), and 'Money Studies' (since 2000). He has also served as Editor for proceedings of the '8th International Conference on Probability Theory and Mathematical Statistics'. His academic service includes leadership as Head of the Applied Statistics Department Seminar, membership on the program committee for international conferences, and serving as an Expert member of the Lithuanian Academy of Sciences since 1994. He has also been actively involved with professional societies, serving on the boards of both the Lithuanian Mathematical Society and Lithuanian Statistical Union since 1998. Dr. Rudzkis teaches probability theory, mathematical statistics, and specialized courses including multivariate statistics, time series analysis, correlation-regression analysis, and econometrics at multiple universities including VDU, KTU, and VGTU.

Alessandro Reclaمشاهده پروفایل

پژوهشگر ارشد

• Data Analytics
• Marketing Research
• Quantitative Methods
• +۳ مورد دیگر

Alessandro Recla is a Research Fellow in the Decision Sciences Department at Bocconi University , affiliated with the School of Management through his role as SDA Fellow in Decision Sciences and Business Analytics. He teaches Statistics and Business Analytics courses across undergraduate, graduate, and Master's programs at Bocconi University (since 2006), Università Cattolica del Sacro Cuore (since 2021), Università dell’Insubria (since 2014), and Franklin University Switzerland (since 2022). Education : Economics, Statistics, and Social Sciences (Bocconi University, 2004) Research Focus : His work specializes in data analytics , marketing research , and quantitative methods for corporate decision-making, with applications in banking (churn prediction), tourism demand analysis, and dashboard design. Publications emphasize descriptive analytics , multivariate models , and time series analysis . Teaching Excellence : Recipient of Bocconi University's Excellence in Teaching Awards for both undergraduate (2018) and graduate (2020) programs. Courses include Data Visualization, Dynamic Forecasting, and Applied Statistics. Consulting & Tools : Applies statistical modeling to real-world business challenges, particularly in marketing decision processes and tourism analytics.

Hyun Kwon Suhمشاهده پروفایل

استادیار

• Smart agriculture
• Machine learning in agriculture
• Artificial intelligence in agriculture
• +۱ مورد دیگر

Hyun Kwon Suh serves as an Assistant Professor in the Department of Integrative Biological Sciences and Industry at Sejong University, Seoul since 2022. His academic trajectory includes prior appointments as Assistant Professor at Dong-A University (2020-2021) and Research Professor at Chungnam National University (2018-2019), alongside ongoing leadership as Founder and CEO of DIGILOG Inc. since 2020. His educational foundation comprises: B.A. from Iowa State University, US (2007) M.A. from Seoul National University, Korea (2012) Ph.D. from Wageningen University, the Netherlands (2017) Dr. Suh's research pioneers the integration of artificial intelligence with agricultural systems, specializing in smart farming solutions. His work spans machine learning applications for crop monitoring, agricultural robotics for automated harvesting, and AI-driven environmental control in greenhouses. This interdisciplinary approach bridges computational science with practical agricultural challenges to enhance productivity and sustainability. Analysis of his publication record reveals a concentrated focus on deploying deep learning and computer vision for precision agriculture, particularly in real-time crop monitoring, yield prediction, and nondestructive quality assessment. His methodologies increasingly leverage time-series analysis and edge computing to address field-level agricultural constraints. His recognition includes: Ministerial Citation from the Ministry of Agriculture, Food and Rural Affairs (December 2021) While specific grant details and student mentorship records aren't documented in available sources, his dual academic-industrial role through DIGILOG Inc. demonstrates active translation of research into commercial agricultural technology solutions, particularly in crop imaging and robotic systems.

• Machine Learning
• Information Theory
• Neural Networks
• +۳ مورد دیگر

Shujian Yu is an Associate Professor II (part-time) in Machine Learning at the Department of Physics and Technology, UiT The Arctic University of Norway, located in Tromso. He specializes in developing machine learning methodologies with applications spanning neuroscience, computer vision, and industrial processes. Research Focus His research integrates information theory with machine learning, focusing on: Novel divergence measures (Cauchy-Schwarz, Rényi entropy) for time-series analysis Interpretable neural networks for brain network-based psychiatric diagnosis Causal discovery methods for industrial fault detection Information bottleneck approaches for feature selection Publication Trends Recent works (2022-2025) demonstrate strong emphasis on information-theoretic learning, graph neural networks, and applications in neuroscience and computer vision. Publications appear in premier venues including IEEE Transactions, Neural Networks, and ICLR, with consistent focus on model interpretability and efficiency. Research Group Member of UiT's Machine Learning Group, collaborating with researchers in neuroscience, signal processing, and data science.

• Business Analytics
• Time Series Forecasting
• Statistical Learning
• +۴ مورد دیگر

João Nuno Costa Gonçalves is an Assistant Professor at the Faculty of Philosophy and Social Sciences (FFCS) of the Portuguese Catholic University in Braga, where he serves as Coordinator of the Degree in Applied Data Science and the scientific area of Computing and Data Science. He is also an integrated researcher at CEGE - Center for Studies in Management and Economics at Católica Porto Business School, demonstrating his strong connection between academic teaching and research in business analytics. His educational background includes: PhD in Industrial and Systems Engineering, University of Minho Masters in Systems Engineering, University of Minho Degree in Mathematics, University of Minho Dr. Gonçalves' research focuses on the intersection of data science and business applications, with particular emphasis on Business Analytics, Time Series Forecasting, and Statistical Learning. His work bridges theoretical advancements with practical implementation in supply chain management, inventory optimization, and decision support systems. He has developed numerous frameworks that help organizations transition toward data-driven decision-making processes, especially in complex industrial contexts like automotive supply chains. His publication portfolio reveals consistent contributions to operations research and business analytics, with recent work emphasizing explainable AI for business contexts, proactive academic success management, and bridging the gap between theoretical analytics and practical implementation. His research demonstrates a clear trajectory from foundational statistical methods toward increasingly sophisticated AI applications in business decision-making. As an educator, he teaches across multiple programs including the Degree in Applied Data Science, Degree in Data Science and Technology (EaD), and Master in Digital Communication. His courses span from foundational mathematics to advanced data science applications, including Mathematical Analysis I & II, Statistical Learning, Artificial Intelligence in Marketing, and Laboratory of Data Science and Technology.

• Data Science
• Machine Learning
• Graph Signal Processing
• +۵ مورد دیگر

Prof. Baltasar Beferull-Lozano is Professor, Chief Research Scientist/Research Professor and Head of the Signal and Information Processing for Intelligent Systems Department at Simula Metropolitan . His leadership role spans directing cutting-edge research at the intersection of data science, networked systems and artificial intelligence. Research Interests: His group pursues a broad agenda that includes Data science and machine learning Online optimization and streaming algorithms Graph signal processing and higher-order networks Intelligent sensing, signal processing and inference Cyber-physical systems and IoT In-network distributed and cooperative intelligence AI-driven networks and communication systems Across these themes, his work consistently develops mathematically rigorous yet computationally efficient frameworks that enable learning and inference on complex, dynamic networked data. Publication Trends: Over 2021-2025 he has produced a prolific stream of journal and conference papers. Recurrent topics include topological signal processing , graph neural networks , transfer learning for wireless radio mapping , online kernel methods , simplicial and higher-order models , and non-linear topology identification . These works collectively advance both theoretical foundations and practical algorithms for next-generation networked AI systems. Scientific Awards: No awards are listed in the provided text. Advising & Grants: While no explicit grants or student lists are supplied, the volume and senior authorship of publications suggest Prof. Beferull-Lozano leads a sizeable research group comprising PhD candidates and post-doctoral researchers working on externally funded projects. Labs & Teams: He heads the Signal and Information Processing for Intelligent Systems Department at Simula Metropolitan, a multidisciplinary unit focused on machine learning, signal processing and networked intelligence.

• Signal Processing
• Machine Learning
• Graph Theory
• +۵ مورد دیگر

Rohan Thekkemarickal Money is a Postdoctoral Fellow at Simula, affiliated with the Department of Signal and Information Processing for Intelligent Systems within the Simula Metropolitan Centre. His research focuses on advanced signal processing, graph theory, and machine learning applications to dynamic networks and topological data analysis. Recent research trends include developing simplicial vector autoregressive models for network dynamics, online topology identification for higher-order systems, and privacy-aware learning frameworks . His work addresses challenges in time-series forecasting, edge flow backcasting, and capacity estimation for lithium-ion batteries using graph-based methodologies. Rohan collaborates with researchers like Dr. B. Beferull-Lozano and Dr. E. Isufi, contributing to interdisciplinary projects at the intersection of signal processing , network science , and topological data analysis . His publications demonstrate expertise in handling nonlinear systems with sparse data and designing scalable algorithms for real-time applications.

• Missing Data Imputation
• Machine Learning
• Healthcare Informatics
• +۳ مورد دیگر

Thu Nguyen is a Research Fellow in the Department of Holistic Systems at Simula Metropolitan, a leading Norwegian research institute specializing in data science and computing. Her work focuses on developing robust methodologies for handling missing data across critical domains including healthcare diagnostics and environmental monitoring systems. Her primary research interests include: Advanced missing data imputation techniques Machine learning explainability under incomplete data Healthcare informatics and medical data analysis Environmental data processing for air quality prediction Multimodal data integration challenges Analysis of her publication record reveals a strong trajectory in developing computationally efficient imputation algorithms with real-world applications. Key trends include the application of principal component analysis and generative models to healthcare datasets (sperm tracking, medical imaging), environmental monitoring (air quality prediction), and multimodal systems. Her work consistently bridges theoretical statistical foundations with practical implementation needs, emphasizing model transparency and performance validation. Dr. Nguyen actively collaborates within Simula Metropolitan's data science ecosystem, contributing to interdisciplinary projects that address fundamental challenges in data completeness and usability across scientific domains.

• Economic Statistics
• Machine Learning
• Bayesian Statistics
• +۴ مورد دیگر

Naoki Awaya is an Assistant Professor at the School of Political Science and Economics , Waseda University. His research focuses on economic statistics, computational statistics, and financial econometrics with applications in machine learning and Bayesian inference. Education : Not explicitly stated Current Projects : Developing Bayesian estimation methods for nonstationary economic time series and structural changes His work includes tree-based models for probability distributions and advanced MCMC sampling techniques. He teaches graduate-level econometrics courses (Econometrics I/II) using R programming, emphasizing methodological rigor and theoretical foundations. Research interests span high-dimensional data analysis, density ratio estimation, and financial market applications through multivariate Hawkes processes. Recent publications highlight innovations in: Unsupervised tree boosting Hidden Markov Pólya trees Particle rolling MCMC algorithms Nonstationary errors-in-variables models

Miguel D. Mahechaمشاهده پروفایل

استاد

• Environmental Data Science
• Remote Sensing
• Ecosystem Responses to Climate Extremes
• +۶ مورد دیگر

Miguel D. Mahecha is a Full Professor of Environmental Data Science and Remote Sensing at the University of Leipzig's Faculty of Physics and Earth System Sciences, where he serves as Institute Head of the Institute for Earth System Science and Remote Sensing. He is also a key figure at the Remote Sensing Centre for Earth System Research, a collaborative initiative between Leipzig University and the Helmholtz Centre for Environmental Research (UFZ). His academic positions include being a Principal Investigator in the Centre for Scalable Data Analytics and Artificial Intelligence and co-spokesperson for the National Research Data Infrastructure for Earth System Sciences (NFDI4Earth). Mahecha's research focuses on understanding ecosystem responses to climate extremes and human-environment relationships during these extreme events. His work spans macro-ecological dynamics, ecosystem functioning, and the development of Earth System Data Cube methodologies that combine empirical methods with theoretical understanding. His research employs data-driven approaches and high-dimensional Earth observations to unravel complex interactions within the Earth system. His recent publications reveal a strong emphasis on compound climate extremes, Earth system data cubes, and AI applications in environmental science. Mahecha's work frequently addresses the intersection of biodiversity, climate extremes, and ecosystem functioning, with particular attention to developing novel methodologies for analyzing spatiotemporal patterns in Earth system data. Fellow of the European Laboratory for Learning and Intelligent Systems Member of the German Centre for Integrative Biodiversity Research (iDiv) Co-spokesperson for NFDI4Earth Principal Investigator in the Centre for Scalable Data Analytics and Artificial Intelligence Mahecha leads multiple significant research projects including Digital Forest (real-time forest monitoring), NFDI4BioDiversity (biodiversity data infrastructure), and XAIDA (AI for detection and attribution of extreme events). His teaching portfolio covers fundamental and advanced topics in physical geography, Earth system components, and geospatial data analysis, reflecting his commitment to training the next generation of Earth system scientists.