جست‌وجوی استاد و پژوهشگر

Krzysztof Pancerzمشاهده پروفایل

استاد

• Machine Learning
• Computational Intelligence
• Knowledge Discovery from Data
• +۳ مورد دیگر

Krzysztof Pancerz is a Professor at the Department of Computer Science within the Faculty of Philosophy at John Paul II Catholic University of Lublin (KUL), Poland. His institutional affiliation is formalized as "prof. KUL" with contact address at Al. Racławickie 14, 20-950 Lublin. He holds dual doctorates: PhD in Computer Science (2006) and DSc in technical sciences (2018) from the Polish Academy of Sciences in Warsaw. His research spans Computational Intelligence and Medical Diagnostics , with emphasis on machine learning for cancer detection using Raman/FTIR spectroscopy. Key areas include: Knowledge discovery from biomedical spectral data Ontology-based information systems Unconventional computing models Deep learning for disease biomarker identification Recent publications (2022-2025) demonstrate consistent focus on cross-disciplinary applications, particularly in oncology and reproductive health diagnostics. He actively organizes scientific workshops including: International Workshop on AI in Medical Applications (FedCSIS conferences, 2011-2018) Knowledge and Data Engineering in Medicine (KES conferences since 2018) His research leadership includes managing the Ministry of Science-funded project on non-invasive laryngeal disease diagnostics and contributing to the EU FP7 PhyChip project on slime mould computing. Current thesis supervision includes topics like fruit leaf recognition and financial deposit forecasting using neural networks. Professional engagement: Member of Polish Fuzzy Sets Society (POLFUZZ) ORCID: 0000-0002-5452-6310 Publications indexed in arXiv, dblp, PubMed

• Statistical Modeling
• Robust Statistics
• Graphical Models
• +۴ مورد دیگر

Simona Pacillo is a Researcher in Statistics at the University of Sannio, affiliated with the Department of Law, Economics, Management and Quantitative Methods. Her office is located in the De Simone building, and she can be contacted via phone or institutional email. Research Focus: Dr. Pacillo specializes in developing robust statistical methodologies with particular expertise in: Flexible probability models including Skew Normal and Skew t distributions Graphical model theory and conditional independence testing Robust statistical procedures resistant to data contamination Algorithms for Generalized Linear Models with convex penalties Model selection techniques in high-dimensional settings Publication Trends: Her recent scholarly work demonstrates consistent focus on robust statistical methodologies, with publications spanning robust circle fitting algorithms, graphical model selection under non-normal distributions, and contamination-resistant statistical procedures. Her research frequently addresses challenges in model robustness and distributional flexibility.

Silvia Mikschمشاهده پروفایل

استاد

• Visual Analytics
• Information Visualization
• Time-Oriented Data Analysis
• +۳ مورد دیگر

Prof. Silvia Miksch is a full professor in the Department of Visual Analytics at TU Wien. Her research focuses on visual analytics, time-oriented data visualization, and human-computer interaction. She leads projects in cultural heritage analysis, fraud detection, and pandemic data visualization. Her work bridges computer science with digital humanities, emphasizing user-centric design and uncertainty modeling. Key contributions include guidance systems for VA environments and network visualization frameworks for art history. Affiliations: TU Wien (since 2000+) Research Labs: Network Lab, Visual Analytics Research Group Recent projects explore temporal patterns in artist exhibitions, parameter space exploration, and pandemic data communication. She has advised over 20 PhD/Master’s students, many contributing to VA systems like COVIS and NEVA. Awarded the VGTC Visualization Technical Achievement Award (2024) for foundational work in temporal visualization. Active in IEEE VAST and serves on journal editorial boards. Her labs develop open-source tools for interactive data exploration.

Santiago Montoya-Blandonمشاهده پروفایل

مدرس

• Theoretical Econometrics
• Bayesian Econometrics
• Applied Microeconomics
• +۲ مورد دیگر

Santiago Montoya-Blandon is a Lecturer in Econometrics at the University of Glasgow's Adam Smith Business School. He holds a PhD from Emory University, preceded by undergraduate and master's degrees from Universidad EAFIT, Colombia. His research focuses on theoretical/applied econometrics, Bayesian methods, and econometric challenges in network and spatial data. He teaches Bayesian data analysis and research methods at both undergraduate and graduate levels. Education: PhD in Economics, Emory University (2021) MA in Economics, Emory University (2019) MSc in Economics, Universidad EAFIT (2015) BA in Economics, Universidad EAFIT (2013) Research Interests: Bayesian econometrics Network and spatial data analysis Applied microeconomics Semiparametric models Awards: Economics Graduate Student Research Award of Excellence (Emory University, 2020) Fulbright-Colciencias Scholarship (2016–2020) Colciencias Junior Researcher Scholarship (2014–2015) Advising: Supervised three PhD students in applied microeconometrics and Bayesian methods. Active in mentoring research on network formation and fractional outcomes.

• Artificial Intelligence
• Security and Privacy of Artificial Intelligence
• Distributed Learning

Dr M Aristodemou is a Research Associate in the Department of Computer Science at the University of York, affiliated with the Cyber Security & Privacy Research Group. Their work focuses on Artificial Intelligence security, distributed learning, and privacy-preserving technologies. They hold a PhD in Security of Artificial Intelligence from Loughborough University, preceded by an MEng in Systems Engineering. Their research emphasizes real-world applications of AI in healthcare monitoring and edge computing environments, with a particular interest in federated learning and energy-efficient systems. Key research themes include securing AI systems against adversarial attacks, designing low-energy edge devices for on-device personalization, and applying machine learning to analyze healthcare time-series data for neurological conditions. Their contributions span theoretical frameworks for privacy metrics and practical implementations in IoT and wearable technologies. Recent projects include the PRISM framework for healthcare IoT security and AI auditing methodologies. Their work bridges academic research with clinical applications, aiming to improve patient outcomes through intelligent monitoring systems while maintaining strict privacy standards. Collaboration on EU-funded initiatives and industry partnerships underscores their commitment to translating research into actionable solutions.

• Machine Learning
• Artificial Intelligence
• Speech Processing
• +۶ مورد دیگر

Saeed Amizadeh is a prominent researcher specializing in artificial intelligence and machine learning, currently affiliated with Microsoft's research division. His work spans multiple domains of AI including speech processing, natural language understanding, computer vision, and time series analysis, with a particular focus on developing novel frameworks that bridge symbolic reasoning with neural approaches. Over the past decade, he has established himself as a significant contributor to the field through publications in top-tier conferences including ICASSP, ICLR, AAAI, KDD, and IJCAI. Dr. Amizadeh's research interests primarily center around advancing the theoretical foundations and practical applications of machine learning systems. His work on neuro-symbolic visual reasoning has contributed to understanding how to effectively disentangle visual perception from logical reasoning in AI systems. He has made significant contributions to differentiable programming, particularly in making classical machine learning pipelines fully differentiable, which enables end-to-end optimization of complex ML workflows. His research on speech enhancement using GANs represents cutting-edge work in audio processing, while his time series anomaly detection frameworks have practical applications in numerous industry settings. Analysis of his publication trends reveals a consistent trajectory from theoretical machine learning foundations toward increasingly applied research with practical industrial relevance. His early work (2010-2015) focused on fundamental algorithms and probabilistic modeling approaches, while his more recent publications (2019-2025) demonstrate a shift toward practical AI systems with direct applications in speech processing, audio separation, and enterprise machine learning. A notable theme throughout his career is the development of frameworks that enable more efficient, scalable, and interpretable AI systems. As a key contributor to Microsoft's ML.NET framework, Dr. Amizadeh has played an important role in developing tools that make machine learning more accessible to enterprise developers. His collaborative work spans academia and industry, with notable partnerships with researchers from various institutions as well as within Microsoft Research.

Yinghao Panمشاهده پروفایل

استادیار

• Personalized Medicine
• Survival Analysis
• Machine Learning
• +۲ مورد دیگر

Yinghao Pan is an Assistant Professor of Statistics at the University of North Carolina at Charlotte , affiliated with the School of Data Science and the Department of Mathematics and Statistics . Prior to his faculty role, he was a Postdoctoral Research Fellow at Fred Hutchinson Cancer Research Center and earned his Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill. Education: Ph.D. in Biostatistics (2017, UNC Chapel Hill) M.S. in Biostatistics (2012, Yale University) B.S. in Mathematics (2010, Fudan University) Pan's research focuses on personalized medicine , survival analysis , and machine learning to develop statistical methods for optimal treatment rules. His work bridges causal inference and cost-effective sampling in healthcare contexts, including depression treatment and diabetic patient monitoring. His recent articles address semiparametric models , interval-censored data , Alzheimer's mortality trends , and machine learning for treatment optimization , reflecting his interdisciplinary approach to healthcare analytics. Funding from the National Science Foundation supports his work. Scientific Awards: ENAR Distinguished Student Paper Award (2023) Pan co-advised Xi Ning (Ph.D., now Assistant Professor at Colby College). He contributes to biostatistical methodology and clinical applications , with ongoing research in survival analysis and electronic health record analysis .

• Time Series Analysis
• Seasonality Modeling
• Applied Econometrics
• +۲ مورد دیگر

Tomás Del Barrio Castro is a University Professor in the Department of Economics at the University of the Balearic Islands (UIB). He has been a faculty member at UIB since 2007, previously serving as a professor at the University of Barcelona from 1993 to 2007. His academic career includes positions as assistant professor (1993-1995), full professor at university school (1995-2001), and full professor at university (2001-2007) at the University of Barcelona. Dr. Del Barrio Castro was born in Madrid in 1968. He received his Bachelor's degree in Economics and Business Administration from the Autonomous University of Madrid in 1992 and completed his PhD in Economics from the University of Barcelona in 1998. He has also been a visiting researcher at the University of Manchester (UK, 2003) and Banco de Portugal (Portugal, 2013). His primary research interests focus on Time Series Analysis (Econometric), Seasonality Modeling, and Applied Econometrics. Dr. Del Barrio Castro has made significant contributions to the understanding of seasonal unit roots, periodic integration, and time series econometrics. His work has advanced methodologies for analyzing economic data with seasonal patterns and has provided new insights into the properties of various econometric tests under different data generating processes. Dr. Del Barrio Castro's publication record demonstrates a consistent focus on time series econometrics, particularly in the areas of seasonal unit root testing, periodic integration, and the properties of econometric tests in the presence of various data characteristics. His work spans theoretical developments and practical applications, with publications in top econometrics journals including Econometric Theory, Journal of Time Series Analysis, and Econometric Reviews. Among his notable achievements is the Econometric Theory Fine Scripsit Award received in 2014. Additionally, he has earned recognition for 4 research sections by CNEAI. These honors reflect the high quality and impact of his research contributions to the field of econometrics. Dr. Del Barrio Castro has served as principal investigator for Competitive Research Projects since 2007. He is currently the principal investigator of the "Econometría y Ciencia de Datos (ECD)" consolidated R&D group at UIB. His teaching portfolio includes courses such as Macroeconometrics, Time Series Analysis, and Economics Thesis supervision for both undergraduate and graduate programs at UIB. He is actively involved in the "Econometría y Ciencia de Datos (ECD)" research group, where he leads research efforts in econometrics and data science. This group focuses on advancing methodologies in time series analysis, econometric modeling, and the application of these techniques to economic and business problems.

• Financial Econometrics
• Time Series Analysis
• Econometric Theory
• +۴ مورد دیگر

Dr. Victor Emilio Troster is an Associate Professor at the Department of Applied Economics at the University of the Balearic Islands (UIB), a position he has held since 2015. He earned his PhD in Economics from the Carlos III University of Madrid, Spain in 2015. His teaching portfolio spans undergraduate and graduate programs across multiple disciplines, including Business Administration, Economics, Law, and Tourism. For the 2025-26 academic year, he teaches Survey Analysis and Multivariate Techniques across various dual degree programs, Econometrics across multiple Business Administration programs on different Balearic Islands campuses, and Data Reduction Techniques in the Master's in Tourism Economics. Dr. Troster's research focuses on financial, theoretical and time series econometrics, with special emphasis on causality tests, specification tests of quantile regression models, and financial asset forecasting models. His work demonstrates a consistent pattern of high-quality publications in leading journals across econometrics and financial economics, showing particular strength in methodological innovation applied to real-world financial problems. The research spans theoretical econometrics, financial market analysis, risk management, and energy economics applications. He has participated in competitive national research projects funded by Spain's Ministry of Science and Innovation and is an active member of the Econometría y Ciencia de Datos (ECD) research group at UIB. Dr. Troster supervises final degree projects for Economics and Business Administration students and has taught specialized courses including Big Data Econometrics in the Master's Degree in Big Data Analysis. His international collaborations span multiple continents, reflecting the global relevance of his research contributions to econometric methodology and financial applications.

• Complex Systems
• Dynamical Systems
• Network Science
• +۱۳ مورد دیگر

Luc Berthouze is a Professor of Complex Systems (Informatics) at the School of Engineering and Informatics, University of Sussex, where he leads interdisciplinary research at the intersection of mathematics, engineering, and neurophysiology. He also holds an honorary appointment at the Great Ormond Street Hospital Institute of Child Health, University College London. His educational background includes a PhD in Applied Mathematics and Computer Science from the University of Evry Val d’Essonne (1996), followed by research positions at the Electrotechnical Laboratory (ETL) and the National Institute of Advanced Industrial Science and Technology (AIST) in Japan, before joining the University of Sussex faculty in 2006. His research focuses on dynamical processes on networks, including neuronal synchronization, epidemic spreading, and fault detection in IT infrastructures. He develops mathematical models and analysis methods rooted in dynamical systems, random processes, graph theory, control theory, and signal processing. Applications span neuroscience (EEG, EMG, MEG), robotics, and large-scale network management. The trend in his recent publications reveals a strong emphasis on network dynamics across biological and technological systems. Key themes include functional connectivity inference, metastability in oscillatory networks, epidemic modeling on structured networks, and scalable observability in microservices. His work increasingly bridges neuroscience and computer engineering, exemplified by projects like 'Rethinking large-scale network management through the lens of neuroscience.' Mathematical Neuroscience Mathematical Epidemiology Time Series Analysis Network Analysis Synchronization and Criticality Motor Control and Coordination He has received substantial research funding from EPSRC, Innovate UK, Moogsoft, Aviva, and the Leverhulme Trust, supporting work on network controllability, data-driven decision making, and Bayesian inference. His grants often involve collaborations with industry partners, indicating applied and translational impact. He supervises research projects and teaches courses such as 'Intelligence in Animals and Machines,' contributing to both undergraduate and postgraduate education. His lab and research team work on developing scalable mathematical frameworks for network analysis, with applications in healthcare, robotics, and IT infrastructure.

• International Finance
• Financial Econometrics
• Derivative Securities
• +۵ مورد دیگر

Raul Susmel is an Associate Professor in the Department of Finance at the C. T. Bauer College of Business, University of Houston, where he has been a faculty member since 1993. Prior to this, he taught at the University of South Florida and the University of Virginia. Research Interests: International Finance Financial Econometrics Derivative Securities Volatility Modeling Regime-Switching Models Emerging Markets His research primarily investigates volatility dynamics across global equity and commodity markets, with a focus on modeling time-varying variances, cross-market correlations, and structural shifts. His publications appear in leading journals such as the Journal of Econometrics , Journal of International Money and Finance , and Journal of Development Economics . The 15 most recent articles (up to 2001) reflect a strong methodological emphasis on econometric modeling of financial volatility, particularly using GARCH and regime-switching frameworks. These works explore themes such as volatility spillovers, contagion in emerging markets, and the behavior of international equity and energy markets. The research is deeply rooted in empirical finance and time series econometrics, often applied to real-world financial phenomena like bank stock repurchases and ADR pricing. Scientific Awards: No awards listed in the provided text. Advising and Grants: No information available on student advising or research grants in the provided text. Labs and Research Teams: No specific labs, research centers, or teams are mentioned in association with Raul Susmel in the provided profile. However, given his focus on financial econometrics and international markets, he may be affiliated with or contribute to finance research initiatives within the Bauer College of Business.

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

Nan Qu is a Lecturer in the Department of Econometrics & Business Statistics at Monash University's Faculty of Business and Economics. With a research focus on quantitative methods in econometrics, Dr. Qu contributes to the advancement of statistical modeling techniques, particularly in financial time series analysis. The research interests of Nan Qu center on econometrics and time series analysis , with a specialization in multivariate GARCH processes and influence diagnostics . These areas are crucial for modeling financial volatility and assessing model sensitivity in high-dimensional settings. The methodological focus supports applications in financial risk assessment and econometric model validation. The available publication record shows a concentrated contribution in the area of statistical diagnostics for financial time series models. The 2010 article in the Journal of Time Series Analysis represents a technical advancement in evaluating the robustness of multivariate volatility models, indicating a research trajectory aligned with methodological rigor in econometric theory. Nan Qu has contributed to peer-reviewed research in econometrics, with a notable article published in 2010. While no formal awards or fellowships are listed in the provided information, the publication reflects scholarly engagement in a specialized and impactful domain of quantitative economics. Dr. Qu has advised no students listed in the available data and there is no mention of research grants, collaborative teams, or laboratory affiliations. The academic activity appears centered on individual research contribution in theoretical econometrics, with a focus on model diagnostics and statistical inference for financial data.

• Biostatistics
• Medical Statistics
• Clinical Trials
• +۷ مورد دیگر

Birgit Debrabant is an Associate Professor at the Department of Mathematics and Computer Science, University of Southern Denmark, with additional roles in Data Science and the STEM Center for Educational Research – FNUG. Her interdisciplinary work bridges statistical methodology with applications in neuroscience, clinical medicine, and public health. Department: Department of Mathematics and Computer Science Research Centers: STEM Center for Educational Research – FNUG Professional Affiliation: Active in Danish Society for Theoretical Statistics Her research focuses on biostatistics in clinical contexts, particularly neurosurgical outcomes, chronic subdural hematoma, intraventricular hemorrhage, and public health interventions. She employs advanced statistical methods including randomized trials, observational studies, meta-analyses, and genetic association studies. Her work often involves large-scale registry data and methodological innovation in handling complex health data. The 15 most recent publications reveal a strong trend in medical statistics, especially in neurosurgery and public health. Topics include optimal drainage times, antithrombotic therapy, BMI policy impacts, and statistical methods for DNA methylation and hidden population estimation. These reflect a cohesive research program applying rigorous statistical science to pressing clinical and public health questions. She has no listed scientific awards in the provided text. Birgit Debrabant actively contributes to teaching and curriculum development, particularly in biostatistics and applied statistics for health sciences. She has led or co-taught courses such as 'Advanced Biostatistical Methods in Health Sciences' and 'Theory of Science and Statistics'. While no formal student advising or grant information is mentioned, her involvement in multi-center trials and educational initiatives suggests collaborative leadership. She participates in national and international conferences, including DAGStat and the International Workshop on Applied Probability. She is involved in collaborative research networks, particularly in theoretical statistics and health data science, and contributes to professional events as both organizer and presenter.

Isabel Cristina Maciel Natárioمشاهده پروفایل

دانشیار

• Spatio-Temporal Statistics
• Bayesian Analysis
• Hierarchical Modeling
• +۷ مورد دیگر

Isabel Cristina Maciel Natário is an Associate Professor in the Department of Mathematics at the Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa (FCT-UNL). She is an integrated member of the Centro de Matemática e Aplicações (CMA), specifically within the Statistics and Risk Management research group. Her academic and research profile is deeply rooted in statistical theory and applied data analysis, with strong interdisciplinary applications. Her educational background includes a Doctorate in Statistics and Operational Research (2005), a Master’s in Probability and Statistics (1999), and a Bachelor’s in Applied Mathematics and Computation (1995), all from Universidade de Lisboa. Doctorate: Hierarchical Bayesian Models for Epidemiological Analysis of Rare Events, Universidade de Lisboa (2005) Master’s: Spatial, Temporal and Spatio-Temporal Distribution of Rare Diseases, Universidade de Lisboa (1999) Bachelor’s: Avaliação de Erros em Inquéritos: Entrevista/Reentrevista, Instituto Superior Técnico (1995) Her research interests center on Spatio-Temporal Statistics, Bayesian Analysis, Hierarchical Modeling, Stochastic Processes, and Big Data. She applies these methodologies to diverse domains such as environmental risk, fisheries, epidemiology, and climate modeling. Her recent work demonstrates a strong focus on geostatistical inference, stochastic partial differential equations, and risk assessment in maritime and ecological contexts. Her 15 most recent publications reflect a consistent trajectory in advanced statistical modeling, particularly in spatial and spatio-temporal frameworks. The research spans theoretical developments in stochastic processes and Bayesian methods, as well as applied studies in health, marine biology, and environmental science. Key themes include maritime surveillance, species distribution modeling, wind velocity analysis, and clinical outcome prediction using statistical models. She has received no explicitly mentioned scientific awards in the provided text. Isabel Cristina Maciel Natário has supervised or collaborated with several researchers, including Sílvia Isabel Belo Guerra and Paula Cristina Pires Simões. While no formal list of advisees is provided, her collaborative publications and leadership in research groups suggest active mentoring. She has been involved in interdisciplinary research projects, particularly those involving environmental and health data. There is no mention of specific grants, but her sustained publication record and research group affiliation indicate active research funding. She is affiliated with the CMA – Centro de Matemática e Aplicações, a recognized research center at FCT-UNL, where she contributes to the Statistics and Risk Management group. This center supports collaborative, interdisciplinary research in mathematical modeling and statistical applications.

• Agricultural Economics
• Productivity Analysis
• Technical Efficiency
• +۴ مورد دیگر

Eva Scheichel , Senior Lecturer at the Institute of Production and Logistics within the Department for Economics and Social Sciences at the University of Natural Resources and Life Sciences, Vienna (BOKU), specializes in Agricultural Economics with a focus on productivity analysis, technical efficiency, and eco-efficiency in dairy and crop farming systems. Her work applies advanced methodologies like Data Envelopment Analysis (DEA) and stochastic frontier analysis to investigate efficiency determinants across Austrian agricultural sectors. Education MSc in Economics & Social Sciences, Bocconi University Milan (2007-2009) BSc in Agricultural Sciences, University of Natural Resources and Life Sciences, Vienna (2014-2017) BA in Business Consultancy International, University of Applied Sciences Wiener Neustadt (2004-2007) Research Interests Productivity dynamics in dairy farming Technical vs. eco-efficiency trade-offs Land use efficiency and rental impacts Scale economies in agricultural production Wine grape grower inefficiency analysis Policy implications for agricultural sustainability Publication Trends : Her 2018-2021 work spans dairy productivity analysis, land use efficiency, and eco-technical efficiency linkages, with geographic focus on Austria and methodological emphasis on DEA and longitudinal modeling. Research addresses both micro-level farm operations and macro-level policy impacts.