جست‌وجوی استاد و پژوهشگر

László Mérőمشاهده پروفایل

استاد

• Psychology
• Game Theory
• Cognitive Science
• +۴ مورد دیگر

László Mérő is a Professor and lecturer in the Department of Affective Psychology at Eötvös Loránd University's Institute of Psychology. His work focuses on the interplay between psychology, game theory, and complex systems, particularly exploring how humans perceive and rationalize rare events, emotions in decision-making, and the boundaries of rational thought. He has also contributed to artificial intelligence research and the philosophical underpinnings of scientific inquiry. His publications often bridge theoretical and applied domains, addressing topics like monetary behavior, social coordination, and cognitive biases. Mérő’s research interests prominently feature the study of 'miracles' as probabilistic anomalies, the evolution of economic systems, and the limits of rationality in both human cognition and AI. He has written extensively on these themes, including books translated into English such as The Logic of Miracles and Moral Calculations . His work integrates empirical psychology with mathematical models to explain phenomena ranging from emotional impulses to collective social behaviors. No scientific awards are explicitly mentioned in the provided texts. While no advising or grant details are available, his publications suggest a focus on interdisciplinary research at the intersection of psychology, philosophy, and computational sciences. His institutional affiliation includes the Institute of Psychology, where he maintains an office at Izabella u. 46, Budapest, and can be reached via email and listed phone number.

Nariman Sepehriمشاهده پروفایل

استاد

• Fluid Power Systems
• Robotics and Teleoperation
• Control Systems
• +۴ مورد دیگر

Dr. Nariman Sepehri is a Professor in the Department of Mechanical Engineering at the Price Faculty of Engineering, University of Manitoba, Canada. He has held significant administrative roles including Department Associate Head (Graduate Studies), Associate Dean of Engineering (Undergraduate Programs), and Acting Dean of Engineering. His research focuses on fluid power systems, robotics, and control with applications in rehabilitation and heavy machinery. Education: Post-Doctorate, Electrical & Computer Engineering, University of British Columbia, Canada (Tele-Robotics, Mechatronics) PhD, Mechanical Engineering, University of British Columbia, Canada (Control, Fluid Power Systems, Robotics) MSc, Mechanical Engineering, University of British Columbia, Canada (Computer-Aided Manufacturing Planning) BSc, Mechanical Engineering, Sharif University of Technology, Iran (Machine Design) Dr. Sepehri's research interests span Fluid Power Systems and Technology , Robotics and Teleoperation , Control Systems , Condition Monitoring , and Mechatronics of Rehabilitation Devices . His work integrates advanced control theory with practical applications in hydraulic and pneumatic systems, aiming to improve energy efficiency and reliability in robotics, manufacturing, aerospace, and healthcare. Notably, he has developed innovative rehabilitation devices using game-based interfaces for stroke and cerebral palsy patients. His recent publications (2022-2025) demonstrate a strong trend towards energy-efficient hydraulic systems, fault detection using machine learning, and the development of soft robotic actuators for rehabilitation. Key areas include electro-hydrostatic actuators, pump-controlled circuits, and the application of advanced algorithms for condition monitoring and control. Scientific Awards: Dean of Engineering’s Award for Superior Academic Performance University of Manitoba Rh Award for outstanding contributions to scholarships and research in Applied Sciences Fellow of the Canadian Academy of Engineering (CAE) Fellow of the American Society of Mechanical Engineers (ASME) Fellow of the Canadian Society for Mechanical Engineering (CSME) Dr. Sepehri has supervised over 100 graduate and postdoctoral students, contributing significantly to the field of fluid power and robotics. His research has been supported by major grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) and other sources, enabling the establishment of the Fluid Power Research Laboratory. This lab features state-of-the-art equipment including a human-robot-in-the-loop simulator and hardware-in-the-loop test facilities for condition monitoring. The Fluid Power Research Laboratory at the University of Manitoba, under Dr. Sepehri's leadership, is a hub for innovation in fluid power technology. The lab collaborates internationally with researchers in USA, Brazil, China, Hungary, Romania, Denmark, Sweden and France, and has developed interdisciplinary projects bridging engineering with healthcare applications.

José E. Chacónمشاهده پروفایل

استاد

• Nonparametric Kernel Smoothing
• Cluster Analysis
• Mathematical Statistics
• +۳ مورد دیگر

José E. Chacón is a Professor of Statistics at the Department of Mathematics, University of Extremadura, Spain. He is also a member of the Institute of Mathematics at the same university. His research focuses on nonparametric kernel smoothing, cluster analysis, and mathematical statistics. He earned his PhD in Statistics from the University of Extremadura in 2004. Chacón’s work emphasizes methodological advancements in density estimation, clustering algorithms, and statistical theory. His recent publications address topics like geodesic distributions, Bayesian taut splines for mode estimation, and bump detection via density curvature. He has contributed to applied areas such as animal home range estimation and data science for pandemic analysis. His articles often explore cross-validation techniques, bandwidth selection, and mixture model clustering. He co-authored the textbook Multivariate Kernel Smoothing and Its Applications (2018), consolidating his expertise in kernel-based methods. Chacón’s research bridges theoretical statistics with practical applications, influencing both academic and applied domains.

Jiong Tangمشاهده پروفایل

استاد

• System Dynamics
• Control
• Sensing and Automation
• +۷ مورد دیگر

Jiong Tang is a Pratt & Whitney Chair Professor in Design and Manufacturing at the University of Connecticut , where he also serves as Co-Director of the Management and Engineering for Manufacturing Program . He received his B.S. and M.S. in Applied Mechanics from Fudan University, China (1989 and 1992), and his Ph.D. in Mechanical Engineering from Pennsylvania State University (2001). Prior to joining UConn, he worked at the GE Research Center as a research engineer. Research Interests : System dynamics, control theory, smart materials, vibration suppression, uncertainty propagation, computational intelligence, and multi-physics system modeling. Current Projects : Digital twin development for aerospace materials, physics-informed machine learning in manufacturing, adaptive metasurface design, and optimization of cooperative robotics. Methodological Focus : Combines Bayesian deep learning , Gaussian process metamodeling , transformer-based architectures , and multi-fidelity data fusion for industrial applications. His work emphasizes smart sensing , electromechanical integration , and uncertainty-robust inverse analysis . Collaboration : Research funded by federal agencies and industrial partners , with particular emphasis on aerospace and manufacturing technologies. His recent publications highlight generative adversarial networks for defect detection , piezoelectric metamaterials , and physics-guided neural network architectures across mechanical, structural, and composite systems.

Umberto Michelucciمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Astrophysics
• +۵ مورد دیگر

Umberto Michelucci is a Professor of Scientific Machine Learning at Lucerne University of Applied Sciences and Arts (HSLU), Switzerland. He holds a PhD in Machine Learning applied to Physics and has over 20 years of industry experience. He is the Subject Head of Applied Data Intelligence in Continuing and Executive Education, Head of Certificates in Machine Learning/Data Engineering, and founder of TOELT LLC and the AI Center of Excellence at Helsana Versicherung AG. His research focuses on machine learning applications in science, astrophysics, uncertainty quantification, and sensor technology. Education PhD in Machine Learning applied to Physics (Portsmouth University) Master in Theoretical Physics (University of Florence) Postgraduate Certificate in Higher Education (Open University, UK) Research Interests Michelucci’s work bridges machine learning and scientific disciplines. Key areas include: Machine learning for astrophysics (INAF collaborations) Uncertainty analysis in high-stakes ML systems Deep learning for optical sensing (e.g., olive oil quality analysis) Foundational mathematical concepts for ML in science Awards & Recognition World’s Top 2% Scientists (Stanford List) Google Developer Expert in Machine Learning AI Global Ambassador (2022) TOP AI Influencer in Switzerland (2021) Grants & Collaborations He collaborates with institutions like INAF (Italy) and NVIDIA/Google, leading projects on AI for agrifood, medical imaging, and astrophysics. His work includes $multi-million industry partnerships and EU-funded research. Labs & Teams Director of the TOELT AI Lab and oversees HSLU’s Applied Data Intelligence programs. Active in open-source initiatives and global AI standardization efforts.

Kristof Coussementمشاهده پروفایل

استاد

• Deep Learning
• Machine Learning
• Text Mining
• +۳ مورد دیگر

Kristof Coussement is a Full Professor and Academic Director of the MSc in Big Data Analytics for Business at IÉSEG School of Management. He holds a HDR in Business Administration from University of Paris Dauphine and Ph.D. in Applied Economics from Ghent University. He directs the IESEG Center for Marketing Analytics (ICMA). His research focuses on big data analytics, machine learning applications in marketing, and explainable AI. He develops advanced analytical frameworks for customer behavior prediction, financial forecasting, and algorithmic decision-making. Coussement's recent work shows strong emphasis on ethical AI development, algorithmic bias mitigation, and industry-specific language modeling. His publications increasingly address the intersection of AI technology and business decision-making processes.

Nuno Almeida Camachoمشاهده پروفایل

دانشیار

• Marketing
• Consumer Behavior
• Health Economics
• +۳ مورد دیگر

Nuno Almeida Camacho serves as Associate Professor of Marketing at Erasmus School of Economics, Erasmus University Rotterdam, and has been affiliated with the ERIM Research Institute (Marketing field) since 2005. His research employs econometric modeling to decode consumer decisions, with specialization in health-related contexts and innovation marketing. Education: PhD in Marketing, Erasmus University Rotterdam (2011) His research spans marketing of innovations, health economics, and behavioral modeling, with specific focus on patient/physician decision-making, therapy adoption, and drivers of non-adherence in life sciences. Current work investigates customer insights frameworks and grassroots innovation dynamics, building on his doctoral research in health marketing. Publication trends reveal a progression from physician learning models (2011-2014) toward innovation management systems (2018-2024), consistently applying behavioral modeling to healthcare and pharmaceutical contexts. Recent articles emphasize customer-driven innovation frameworks and financial projection methodologies in selection processes. He has supervised two doctoral candidates: Elio Keko (2012-2017) on innovation generation in firms and Gert Jan Prevo (2014-) on customer-centric innovation resistance. Additionally, he coaches master's students in evidence-based marketing strategy and innovation research.

Frédéric BERTRANDمشاهده پروفایل

استاد

• Statistical Modelling
• Statistical Learning
• Artificial Intelligence
• +۵ مورد دیگر

Frédéric BERTRAND is a Full Professor at the Université de Technologie de Troyes (UTT), part of the Computer Laboratory and Digital Society (LIST3N). He previously held positions at the Université de Strasbourg and Institut de Recherche Mathématique Avancée (IRMA). His academic qualifications include a habilitation to direct research (HDR) from the Université de Strasbourg, a Ph.D. from Université Louis Pasteur, and membership in the École Normale Supérieure de Lyon. He specializes in statistical modeling, machine learning, and artificial intelligence, with applications in bioinformatics, biostatistics, and medical data analysis. Roles and Affiliations: Full Professor at UTT Director of the Mastère Spécialisé in Big Data Analytical and Decisional Aspects Responsible for the OSS ED361 SPI specialty Responsible for the PEA Impact program Member of the LIST3N laboratory Research Interests: His work focuses on statistical and machine learning methods applied to complex systems, including bioinformatics, medical data analysis, and process mining. Key areas include Bayesian networks, partial least squares regression (PLS), variable selection algorithms, and handling missing data in large-scale datasets. He has developed multiple R packages, such as selectBoost , bootPLS , and Patterns , which are widely used in statistical modeling and data analysis. Publications and Contributions: He has authored/co-authored over 38 scientific articles, 14 books, and 14 R packages. His research emphasizes methodological advancements in statistics and their practical applications in biology, healthcare, and industry. Notable contributions include developing algorithms for high-dimensional data analysis and collaborative projects on cancer genomics and proteomics. Teaching: He teaches advanced courses in statistics, mathematics, and data analysis at the graduate and postgraduate levels, including modules on uncertainty analysis and complex systems at UTT and the Université de Strasbourg.

Qi Tangمشاهده پروفایل

پژوهشگر ارشد

• Hydrogeology
• Data Assimilation
• Earth System Modeling
• +۳ مورد دیگر

Qi Tang is a Research Fellow in the Department of Environmental Science at the University of Basel and concurrently serves as Coordinator of the Swiss Water Earth Systems PhD School at the University of Neuchâtel. His expertise spans hydrogeology, data assimilation, and Earth system modeling. He holds a PhD in Hydrogeology from RWTH Aachen University (2017) and has held postdoctoral positions at institutions including the Alfred Wegener Institute (Germany), the University of Basel, and the Chinese Academy of Sciences. Education: PhD in Hydrogeology, RWTH Aachen University, Germany (2012–2017) MSc in Hydrology and Water Resources, Beijing Normal University (2009–2012) BSc in Applied Mathematics, China Agriculture University (2005–2009) Research Interests: Qi Tang focuses on advancing coupled Earth system models through data assimilation techniques. His work integrates hydrological, oceanographic, and climatic processes to improve predictive accuracy. Key areas include river-aquifer interaction dynamics, satellite data integration in ocean-atmosphere models, and cloud computing for real-time water resource management. His research bridges theoretical modeling with practical applications in environmental monitoring and climate prediction. Publications: His articles emphasize data-driven approaches to environmental systems. Recent work highlights coupled model improvements using satellite data (e.g., ocean-atmosphere interactions), ensemble Kalman filtering for flood simulations, and Bayesian networks for precipitation modeling. These studies underscore his expertise in both computational methods and field applications. Advising & Grants: While no formal advisees are listed, his postdoctoral roles suggest involvement in mentoring junior researchers. No specific grants are mentioned in the provided texts. Labs/Teams: Affiliated with the Hydrogeological Processes group at the Center for Hydrogeology and Geothermal Energy (CHYN), University of Neuchâtel. This group specializes in geothermal energy, hydrochemistry, and stochastic hydrogeology.

Sarah Perezمشاهده پروفایل

پژوهشگر

• Mathematical modelling
• Numerical simulation
• AI-driven approaches
• +۵ مورد دیگر

Sarah Perez is a Postdoctoral Research Associate in the GeoEnergy research group at the Lyell Centre, part of the School of Energy, Geoscience, Infrastructure and Society at Heriot-Watt University. Holding a PhD in Applied Mathematics from the University of Pau in France, she specializes in developing numerical and machine learning models for geological carbon storage applications. Her research sits at the intersection of mathematical modeling, numerical simulation, and AI-driven approaches for subsurface energy systems. She develops frameworks that integrate pore-scale imaging with AI-based uncertainty quantification to improve reliability of subsurface predictions, with particular focus on fractured and reactive media. Her work bridges multiple scales to address leakage risks and subsurface integrity in carbon storage systems. Analysis of her publications reveals consistent focus on computational methods for geological applications, with increasing integration of AI techniques across her work from 2022-2025. Her research shows strong interdisciplinary connections between computational physics, machine learning, and geoscience. Beyond research, Perez actively contributes to academic community through science communication and early-career initiatives. She co-chairs the InterPore Student Awards Sub-Committee and organizes YouTube webinars on Porous Media research and AI for Net Zero, demonstrating commitment to knowledge exchange and interdisciplinary collaboration. Her professional network shows significant international collaboration, with research activities spanning multiple countries and institutions. She maintains active engagement with the scientific community through conference presentations and peer-reviewed publications in high-impact journals.

• Economics
• Statistics
• Composite Indicators
• +۷ مورد دیگر

Chiara Gigliarano is a Professor at the Università Carlo Cattaneo - LIUC, affiliated with the Faculty of Social Sciences and the Department of Economics, Econometrics and Finance. Her research focuses on composite indicators, poverty measurement, environmental wellbeing, and statistical methodology. Previously, she held an Associate Professor position at the University of Insubria. Her work emphasizes innovative approaches to aggregating multidimensional data, particularly in regional and socioeconomic contexts. Key areas include the construction of well-being indices, analysis of income polarization, and evaluating policy impacts using Bayesian networks and spatial econometric models. Recent studies address post-pandemic societal changes, environmental sustainability, and labor market dynamics in Italy. Awards and grants are not explicitly mentioned, but her extensive publication record reflects impactful contributions to economics and statistics. She advises students through methodological and applied research projects in poverty measurement, composite indicators, and public policy evaluation. Her research is supported by collaborations with institutions like ISER (University of Essex) and involves both national and international datasets. Labs/teams: Engaged in interdisciplinary projects at LIUC's Social Sciences faculty, focusing on sustainable well-being, regional disparities, and statistical innovation. Publications frequently explore methodological advances for policy-relevant indicators.

Tatevik Sekhposyanمشاهده پروفایل

دانشیار

• Macroeconomics
• Econometrics
• Forecasting
• +۳ مورد دیگر

Tatevik Sekhposyan is an Associate Professor in the Department of Economics at Texas A&M University, specializing in applied macroeconomics, time series econometrics, and forecasting. Her research focuses on monetary policy shocks, uncertainty, and model evaluation. She holds roles as President of the Society for Nonlinear Dynamics and Econometrics, a Board Member of the Armenian Economic Association, and a Research Fellow of the CEPR. She earned her PhD from the University of North Carolina at Chapel Hill and has served on editorial boards of major journals. Her work explores macroeconomic uncertainty indices and the implications of shocks, with notable contributions to forecasting frameworks and policy communication. During 2024-2025, she is on sabbatical at Princeton University. Her educational background includes a focus on econometric methods and macroeconomic theory. Research interests include the identification of monetary policy effects, temporal aggregation impacts, and predictive density evaluations. Awards include the International Journal of Forecasting's Outstanding Paper Award (2014). She teaches courses on economic forecasting and time series analysis, and collaborates with Amazon's Supply Chain Optimization Technologies (SCOT) team on inventory prediction challenges. Key contributions include developing uncertainty indices for the Euro Area and analyzing electricity demand as an economic indicator. Her work bridges academic research with practical applications, emphasizing the role of uncertainty in economic decision-making.

Shan Huangمشاهده پروفایل

پژوهشگر

• Computational Geomechanics
• Probabilistic Geotechnics
• Soft soil consolidation
• +۳ مورد دیگر

Dr. Shan Huang is a Researcher at the School of Engineering, University of Newcastle. She holds a BE in Civil Engineering from Hunan University of Science and Technology, an MS in Road and Railway Engineering from Central South University, and a PhD in Civil Engineering from the University of Newcastle. Her research focuses on computational geomechanics and probabilistic geotechnics, particularly in soft soil consolidation and geotechnical risk assessment. Dr. Huang's work integrates numerical simulation and advanced probabilistic methods, with notable contributions to Bayesian back analysis for settlement prediction and parameter calibration in soft soils. Her research has been published in journals such as Computers and Geotechnics , ASCE Journal of Geotechnical and Geoenvironmental Engineering , and Soils and Foundations . Key projects include the analysis of embankments in Ballina, Australia, where she applied Bayesian methods to predict long-term settlements using monitored data. Her work emphasizes computational efficiency and practical applications in geotechnical engineering.

Jonas Hedlundمشاهده پروفایل

استادیار

• Microeconomic theory
• Information economics
• Game theory
• +۳ مورد دیگر

Jonas Hedlund is an Assistant Professor of Economics at the University of Texas at Dallas (UTD), affiliated with the School of Economic, Political and Policy Sciences. He holds a PhD in Economics from the Universidad de Alicante (2011). His research focuses on microeconomic theory, information economics, and game theory, with a particular emphasis on Bayesian persuasion, contagion management, and strategic communication. His work explores topics such as information disclosure in uncertain environments, imitation dynamics in economic systems, and the role of ambiguity in decision-making processes. Hedlund’s contributions address theoretical frameworks for understanding strategic interactions, market structures, and policy design. His research areas include: Microeconomic Theory Information Economics Game Theory Bayesian Persuasion Mechanisms Contagion Dynamics Recent publications highlight his engagement with advanced topics such as signaling through Bayesian persuasion (2024), contagion management strategies (2024), and ambiguity in persuasion models (2021). While no specific awards or grants are listed, his work reflects a rigorous focus on theoretical and applied economic analysis.

Aad van der Vaartمشاهده پروفایل

استاد

• High-dimensional statistics
• Nonparametric Bayesian statistics
• Statistical genetics
• +۵ مورد دیگر

Aad van der Vaart is a distinguished Professor of Statistics at Delft University of Technology (since 2021). Previously, he held Full Professorships at Leiden University (2012–2021) and Vrije Universiteit Amsterdam (1996–2012). His research focuses on foundational statistical theory and applications, including high-dimensional statistics, Bayesian methods, inverse problems, and genomics. He has made seminal contributions to nonparametric Bayesian inference, empirical processes, and semiparametric theory. Van der Vaart has authored influential textbooks such as Asymptotic Statistics (1998) and Fundamentals of Nonparametric Bayesian Inference (2017, with S. Ghosal). His work bridges theoretical rigor and practical applications, with over 34,830 citations (Google Scholar, 2023) and an H-index of 60. Key honors include the Spinoza Prize (2015, Netherlands’ highest science award), DeGroot Prize (2020), and membership in the Royal Netherlands Academy of Sciences. His academic journey includes roles such as Miller Fellow at UC Berkeley (2000), visiting positions at leading universities, and leadership in statistical societies. His research group actively explores modern challenges in statistical theory and methodology, including causal inference, adaptive estimation, and large-scale data analysis. Notable grants include an ERC Advanced Grant (2012) for Bayesian inverse problems. Collaborations span academia and industry, emphasizing interdisciplinary impact. While specific lab affiliations are not explicitly stated, his work is rooted in foundational mathematical statistics with broad applicability.