جستجوی استاد راهنمای دانشگاهی

Luis Antonio Belanche Muñozمشاهده پروفایل

استاد

• Machine Learning
• Kernel Methods
• Neural Networks

Luis Antonio Belanche Muñoz is a Professor at the Department of Computer Science , Faculty of Informatics of Barcelona (FIB) , Universitat Politècnica de Catalunya (UPC) . He is affiliated with research groups SOCO - Soft Computing and IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Group . His career spans over 25 years, with 216 documented activities. His research focuses on Machine Learning , Kernel Methods , and Neural Networks . He has pioneered techniques in feature selection, similarity measures, and hybrid models connecting deep learning with kernel methods. His work applies to diverse domains including finance, microbiology, cancer diagnostics, and environmental engineering. Recent publications highlight trends in kernel matrix analysis using entropy, microbiome data integration , and drug resistance prediction in HIV. Earlier work includes knowledge-based systems for wastewater treatment diagnostics and educational technologies for MOOC environments. He has collaborated with 75+ researchers across UPC's research network, contributing to projects funded under Spain's State Research Plans and Catalonia's RIS3CAT strategy. His 2011 thesis on Feature selection in brain tumor MRS data demonstrates interdisciplinary applications.

Martha Ivon Cárdenas Domínguezمشاهده پروفایل

مدرس ارشد

• Intelligent Data Analysis
• Machine Learning
• Personalized Medicine
• +۳ مورد دیگر

Dr. Martha Ivon Cárdenas Domínguez is a Senior Lecturer at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Science. She is an active member of the IDEAI-UPC - Intelligent Data sciEnce and Artificial Intelligence Research Group, as well as the SOCO (Soft Computing) research group. Education: BSc in Mathematics PhD in Artificial Intelligence (UPC) Master's in Artificial Intelligence Her research focuses on applying computational intelligence and machine learning techniques to biomedical data analysis, particularly for pharmacoproteomic applications involving G protein-coupled receptors. She specializes in kernel-based methods and data visualization techniques for sequence analysis and classification tasks. Recent publications demonstrate her expertise in manifold learning, subtype discrimination of GPCRs, and phylogenetic tree integration with visualization techniques. Her work addresses challenges in classification errors, mislabeling, and sequence transformation for complex biomedical datasets. Dr. Cárdenas employs advanced visualization strategies to explore metabotropic glutamate receptors and analyze protein sequence overlaps through manifold-based approaches. Her research bridges computational methods with biomedical applications, particularly in personalized medicine contexts. She actively contributes to competitive R&D+i projects and participates in international conferences. Her professional activities include 5 conference presentations, 2 book chapters, and 2 journal articles focusing on data science applications in bioinformatics. Dr. Cárdenas utilizes computational intelligence for analyzing receptor sequences and has developed innovative approaches for visualizing complex biological data structures. Her work has implications for drug discovery and personalized treatment development through enhanced understanding of receptor classification.

• Signal Processing
• Wireless Communication Systems
• Power Amplifier Modeling
• +۴ مورد دیگر

María José Madero Ayora is a Professor at the Department of Signal Theory and Communications , Universidad de Sevilla , specializing in nonlinear system modeling and digital predistortion for wireless communication systems. Her research focuses on Volterra series applications in power amplifier linearization, microwave measurements , and machine learning techniques for signal processing. Principal Investigator for projects like Statistical Signal Modeling for Brain-Computer Interfaces (PID2021-123090NB-I00) Recipient of the Arftg Roger Pollard Student Fellowship in microwave measurement Her work spans 5G waveform linearization , I/Q modulator impairments , and thermal memory effects in RF amplifiers. Recent publications combine sparse Bayesian methods with Volterra models to address nonlinear distortion in OFDM and visible light communication systems. She has supervised doctoral theses and participated in international conferences across the U.S., Europe, and Asia.

• Non-parametric Inference
• Regression Models
• Missing Data Analysis
• +۲ مورد دیگر

Ana Perez Gonzalez is a researcher at the Department of Statistics and Operational Research, Faculty of Business Sciences and Tourism, University of Vigo. She earned her PhD from the University of Santiago de Compostela in 2003, focusing on non-parametric inference for regression models with missing responses. Her research centers on robust statistical methods for missing data, functional regression, and non-parametric estimation. Key collaborations with Wenceslao Gonzalez Manteiga, Graciela Boente, and Ana Bianco Contributions to wild bootstrap calibration, bandwidth selection, and imputation techniques Her work addresses applications in biomedical statistics, climate modeling, and spectrometric studies. Notable methodologies include goodness-of-fit tests for missing data models, principal component-based estimators, and asymptotic variance analysis.

Juan Carlos Vidal Aguiarمشاهده پروفایل

استادیار

• Knowledge Discovery
• Semantic Annotation
• Workflow Modeling
• +۷ مورد دیگر

Juan Carlos Vidal Aguiar is an Assistant Professor in the Department of Electronics and Computing at the University of Santiago de Compostela (Spain), affiliated with the Higher Technical School of Engineering and the Center for Research in Intelligent Technologies (CITIUS). His research focuses on knowledge discovery, semantic technologies, process mining, and AI-driven business intelligence. He holds a PhD from the University of Santiago de Compostela (2010) and a Bachelor's in Computer Science from the University of La Coruña (2000). His work integrates formal methods like Petri nets with machine learning and semantic web technologies, addressing challenges in workflow modeling, predictive monitoring, and healthcare analytics. Key projects include Predictive monitoring and causality for cardiac rehabilitation , Responsible AI for Process Mining 2.0 , and AQUATECHInn 4.0 (digital aquaculture training). He has led research on glucodensity analysis for diabetes monitoring and developed frameworks like OPENET for Petri net-based workflow execution. His contributions span 200+ publications in venues like IEEE Transactions, ACM T-KDD, and Advances in Data Analysis and Classification. Juan Carlos collaborates with institutions across Europe and Latin America on learning analytics, smart manufacturing, and health informatics. His lab's work emphasizes practical applications of AI in education and industry, including gamification analytics and cloud-based process monitoring tools.

Chiara Amorinoمشاهده پروفایل

استادیار

• Statistical inference for stochastic differential equations
• High-frequency data analysis
• Malliavin calculus
• +۷ مورد دیگر

Chiara Amorino is an Assistant Professor at the Department of Economics and Business, Universitat Pompeu Fabra (Barcelona, Spain), starting April 2024. Prior to this role, she was a postdoctoral researcher at the University of Luxembourg (2020–2024), working under Prof. Mark Podolskij. She holds a PhD in Mathematics from Université Paris-Saclay (2020), supervised by Prof. Arnaud Gloter. Her research focuses on statistical inference for stochastic differential equations (SDEs), with particular emphasis on high-frequency data, Malliavin calculus, volatility estimation, and minimax theory. She also investigates McKean-Vlasov equations, Hawkes processes, and local differential privacy. Her work bridges theoretical statistics, probability, and applications in mathematical finance and data science. Amorino has taught courses such as 'Probability and Statistics' at UPF and 'Continuous Time Models in Mathematical Finance' at the University of Luxembourg. She has supervised multiple Master's and Bachelor's theses, including projects on kernel density estimation and stochastic processes. Her recent publications (2023–2025) emphasize parameter estimation in complex stochastic systems, leveraging techniques like Malliavin calculus and deconvolution. She actively contributes to academic conferences, including the Bachelier World Congress and the European Young Statisticians Meeting, and reviews for journals like Annals of Statistics and Stochastic Processes and Applications. Amorino collaborates with institutions globally and leads research initiatives on topics like nonparametric estimation in McKean-Vlasov SDEs and privacy-aware statistical methods.

• Numerical Analysis
• Approximation Theory
• Multiresolution Analysis
• +۷ مورد دیگر

Francesc Arandiga Llau is a Professor in the Department of Mathematics at the Faculty of Mathematics, Universitat de València, Spain. He is affiliated with the ANIMS (Numerical Analysis, Images, Multiresolution and Simulation) research group, where he conducts research in applied mathematics with a focus on numerical methods and their applications. Education: PhD from Universitat de València (1992), thesis on operator approximation and spectral radius continuity, supervised by Dr. Vicent Caselles Costa. His research interests center on Numerical Analysis , Approximation Theory , and Multiresolution Methods , with significant contributions to WENO schemes , nonlinear interpolation , and image and signal compression . His work often bridges theoretical developments with practical implementations in computational mathematics and engineering. He has made notable advances in the stability, accuracy, and adaptability of reconstruction techniques for piecewise smooth and discontinuous functions. The analysis of his recent publications reveals a consistent focus on high-order numerical methods, particularly in the context of image processing and data compression . His work leverages multiresolution analysis , radial basis functions , and adaptive interpolation to improve accuracy and efficiency. Themes across his articles include monotonicity preservation, error control, and the design of nonlinear schemes that avoid spurious oscillations near discontinuities. There are no scientific awards explicitly mentioned in the provided text. Francesc Arandiga has extensive collaborative research, particularly with scholars such as Rosa Donat, Dionisio F. Yáñez, Pep Mulet, and Antonio Baeza. His work has been supported through various research projects, though specific grants are not detailed in the text. He has advised students, including those who have completed theses under his supervision, although a full list is not provided. He is a key member of the ANIMS research group, which focuses on Numerical Analysis, Images, Multiresolution, and Simulation. This team works on developing and analyzing advanced computational methods for scientific and engineering applications, particularly in the areas of data representation, image processing, and numerical solutions to differential equations.

• Machine Learning
• Remote Sensing
• Artificial Intelligence
• +۹ مورد دیگر

Jordi Muñoz Mari is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia. He is a key member of the Image and Signal Processing (ISP) research group within the Image Processing Laboratory (IPL), a recognized research institute (ERI) at the university. His research focuses on the intersection of artificial intelligence, remote sensing, and environmental science. Key areas include machine learning for Earth observation, biophysical parameter retrieval, crop yield forecasting, climate modeling, and causal inference in ecological systems. He also explores applications of AI in education, particularly generative models for assessment and digital learning tools. The most recent publications highlight a strong trend toward physics-aware machine learning, uncertainty quantification, and large-scale applications using platforms like Google Earth Engine. His work spans climate extremes (medicanes, hurricanes, droughts), agricultural monitoring, biodiversity, and educational technology, demonstrating interdisciplinary innovation. Jordi Muñoz Mari completed his PhD at the University of Valencia in 2004 on telemonitoring systems for cardiac patients. His ongoing research involves advanced signal processing, kernel methods, and AI-driven geoinformation systems.

• Quantile Regression
• Statistical Inference
• Biostatistics
• +۴ مورد دیگر

Mercedes Conde Amboage is a Researcher at the University of Santiago de Compostela's Faculty of Mathematics, affiliated with the Department of Statistics, Mathematical Analysis and Optimization. She holds a PhD in Statistics (2017) from the same institution, focusing on Statistical Inference in Quantile Regression Models , advised by Dr. Wenceslao González Manteiga and Dr. César Andrés Sánchez Sellero. Her research interests center on quantile regression methodologies, statistical inference, and their applications in biomedical and environmental data. She contributes to the Galician Mathematical Research and Technology Center (CITMAga) and collaborates with research groups like GRID-BDS (Biostatistics and Biomedical Data Science) and MODESTYA (Optimization and Statistical Models). Her work bridges theoretical statistics with practical applications in clinical trials, proteomics, and environmental monitoring. Recent publications focus on quantile regression innovations in censored data analysis, biomedical biomarker identification, and environmental toxicology. She develops statistical software tools like the 'BwQuant' R package and has contributed to meta-analyses in dentistry and oncology. Notable collaborations include work on medication-related osteonecrosis of the jaw, HER2-positive breast cancer responses, and systematic reviews of surgical techniques. Her methodological advancements address high-dimensional covariates and lack-of-fit testing in complex models.

• Statistics
• Nonparametric Estimation
• Set Estimation
• +۴ مورد دیگر

Alberto Rodriguez Casal is a Professor in the Department of Statistics, Mathematical Analysis and Optimization at the Faculty of Mathematics, University of Santiago de Compostela. He is affiliated with the Galician Mathematical Research and Technology Center (CITMAga) and leads the MODESTYA research group focused on Optimization, decision, statistical models and applications. His research primarily focuses on nonparametric statistics, with special emphasis on: Set and boundary estimation Density level sets and shape analysis Statistical methods for geometric inference Nonparametric estimation under shape constraints Analysis of his publication record spanning nearly two decades shows a consistent focus on geometric and shape-related statistical problems. His work bridges theoretical statistical developments with practical applications in areas such as environmental statistics and spatial analysis. Recent publications demonstrate continued innovation in data-driven methods for shape and boundary estimation, particularly through the application of alpha-shapes and other computational geometry approaches to statistical problems. Professor Rodriguez Casal has established significant collaborative networks, particularly with researchers including Saavedra-Nieves, Paula; Crujeiras, Rosa María; de Uña-Álvarez, Jacobo; Pateiro-López, Beatriz; and Cuevas, Antonio. His work has been published in top statistical journals including The Annals of Statistics, Journal of the American Statistical Association, Test, and Journal of Nonparametric Statistics, with over 25 publications since 2004 that have been cited more than 300 times.

Alastair Reidمشاهده پروفایل

پژوهشگر

• formal verification
• security
• functional languages
• +۱ مورد دیگر

Alastair Reid is a researcher at Intel (2021–present) focusing on formal instruction set architecture (ISA) specifications and model checking processor pipelines. His career spans roles at Google Research (2019–2021) developing Rust verification tools, Arm Ltd (2004–2019) working on formal ISA specifications and vectorizing compilers, University of Utah (1998–2004) on component-based operating systems, Yale University (1994–1998) contributing to Haskell functional reactive programming, and University of Glasgow (1988–1994) in formal specification and verification. Ph.D. in Defining interfaces between hardware and software: Quality and performance , Glasgow University M.Sc. in A precise semantics for Ultraloose Specifications , Glasgow University B.Sc., University of Strathclyde His research interests include formal verification, security analysis, functional programming languages (particularly Haskell and Rust), and computer architecture. He has contributed to the development of machine-readable ISA specifications for ARM, RISC-V, and Intel architectures, and worked on symbolic execution tools like KLEE for Rust verification. Key publications include work on RISC-V specification improvements (2024), formal methods for persistent programming (2023), PLARCH workshop contributions on ISA specification (2023), and foundational papers on ARM processor verification with ISA-Formal (2016). His technical blog posts cover topics in symbolic execution, SMT solvers, and practical verification challenges. Contributions to conferences include committee roles in PLDI (2024), PriSC (2024), and workshop organization in formal methods and programming language design.

Bruno Ebnerمشاهده پروفایل

پژوهشگر ارشد

• Statistical Methodologies
• Goodness-of-Fit Tests
• Multivariate Statistics
• +۶ مورد دیگر

Bruno Ebner is a Senior Research Fellow at the Karlsruhe Institute of Technology (KIT), specializing in statistical methodologies and their applications in applied mathematics and mathematical statistics. He obtained his PhD in Mathematics from KIT in 2010 and has been a permanent faculty member since 2012. His research focuses on developing statistical methods for uni-, multi-, and infinite-dimensional data, including non-Euclidean and metric spaces. Key research interests include goodness-of-fit testing, multivariate statistics, directional statistics, functional data analysis, and Stein's method. Notable contributions include the development of Sobolev tests on hyperspheres and the analysis of eigenvalues in covariance operators. Ebner has secured grants such as the STENED project, supporting PhD students like Paul Axmann and Vinícius Litvinoff. He has authored over 50 peer-reviewed articles in journals like TEST and the Journal of Nonparametric Statistics, with a focus on multivariate normality testing, exponentiality tests, and distributional characterization. His work emphasizes affine-invariant tests and neighborhood model validation. Ebner’s research bridges theoretical statistics with practical applications in data analysis and computational methods.

Philipp Sibbertsenمشاهده پروفایل

استاد

• Time Series with Long Memory
• Non-linear Time Series Econometrics
• Statistics on Financial Markets
• +۲ مورد دیگر

Professor Philipp Sibbertsen is a distinguished faculty member at the Institute of Statistics within the Faculty of Economics and Management at Leibniz University Hannover. He holds the position of Professor of Statistics since 2005 and has maintained an active research and teaching career throughout his academic journey. Professor of Statistics at Leibniz University Hannover (since 2005) Visiting Professor at CREATES, Aarhus University, Denmark (2011) Heisenberg Fellow of the Deutsche Forschungsgemeinschaft (DFG) (2004-2005) Research stay at Cardiff Business School, Cardiff University, UK (2001-2002) University Assistant at Technical University of Dortmund (2001-2004) Professor Sibbertsen's research primarily focuses on time series analysis with emphasis on long memory processes, nonlinear time series econometrics, and financial market statistics. His work bridges theoretical statistical methods with practical applications in economics and finance, particularly in the areas of fractional integration, cointegration, structural breaks, and volatility modeling. His research has significant implications for understanding market dynamics, forecasting economic trends, and developing robust statistical methodologies for complex data structures. His extensive publication record demonstrates a consistent focus on long memory processes and their applications across various domains. Recent publications (2021-2025) show his work expanding into climate data analysis, carbon pricing mechanisms, coastal risk assessment, and medical statistics, while maintaining his core expertise in time series econometrics. His research shows a strong pattern of interdisciplinary collaboration, with publications spanning economics, finance, environmental science, and medical research. Heisenberg Fellowship from DFG (2004-2005) Member of the Editorial Board of 'Statistical Papers' and 'ASta' Member of the Board of the German Statistical Society (since 2016) Professor Sibbertsen has held significant administrative roles including Spokesperson for the research focus 'Innovation and Learning' at Leibniz University Hannover (since 2018), Dean of Studies at the Faculty of Business and Economics (2011-2013), and Chairman of the Committee for Statistical Theory and Methodology of the German Statistical Society (2008-2012). His teaching portfolio includes Descriptive Statistics, Inductive Statistics, Advanced Statistics, and Time Series Analysis. While specific details about his advisees are not provided in the available materials, his extensive publication record suggests active mentorship of graduate students in statistics and econometrics.

• Signal Processing
• Image Processing
• Remote Sensing
• +۹ مورد دیگر

JAVIER CALPE MARAVILLA is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia, Spain. He is a key member of the Image and Signal Processing (ISP) research group and affiliated with the ERI Image Processing Laboratory (IPL). His work bridges academic research and industrial applications in electronics and signal processing. Research Interests: His research spans signal and image processing, remote sensing, hyperspectral imaging, machine learning for classification, biophysical parameter estimation, analog microelectronics, and sensor systems. He focuses on developing algorithms for cloud screening, vegetation stress detection, ECG signal analysis, and industrial sensing applications. The recent publications (2009–2021) highlight a strong trend in applying kernel methods, neural networks, and adaptive filters to remote sensing and biomedical signals. His work emphasizes practical implementations in industrial and environmental monitoring, including touchscreens, AOTF-based imaging, and low-power circuit design. Education: Ph.D. in Electronics, Universitat de València (1994), with thesis on spectral estimation techniques. Scientific Awards: Advising and Grants: While no explicit list of students or grants is provided, his academic rank and publication record suggest experience in supervising postgraduate research. He has contributed to educational curriculum development, particularly in analog microelectronics. No funding sources are mentioned in the text. Labs and Teams: He is actively involved in the ISP (Image and Signal Processing) research group and the ERI Image Processing Laboratory (IPL) at the University of Valencia, focusing on both theoretical and applied research in electronic systems and data analysis.

• Parallel Computing
• High-Performance Computing (HPC)
• Programming Models
• +۶ مورد دیگر

Maria Luisa Gil Gómez is a researcher at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture and the Barcelona Higher Technical School of Telecommunications Engineering. She holds a PhD in Computer Science and leads research in parallel computing, high-performance computing (HPC), and programming models. Her work focuses on optimizing computational workflows, fault tolerance in distributed systems, and leveraging multi-GPU architectures for scientific simulations. She has contributed to projects like the Programming Models group (PM) at UPC and collaborates with the Barcelona Supercomputing Center (BSC-CNS). Affiliations: UPC Department of Computer Architecture, BSC-CNS, PM Research Group Research Interests: Parallel Programming Models, HPC, GPU Computing, Cellular Automata, Education Technology Her research spans over 147 documented activities, including journal articles, conference presentations, and funded projects. Notable contributions include studies on BCI in music education, distributed algorithms for ARM-based clusters, and the OpenCAL++ framework for parallel cellular automata simulations. She has also explored educational methods integrating creativity and game-based learning in engineering.