جستجوی استاد راهنمای دانشگاهی

Stefano Cabras is a Full Professor in the Department of Statistics at University Carlos III de Madrid (UC3M), where he serves as Director of the Flores de Lemus Institute and Deputy Director of the Master's Degree in Statistics for Data Science. His office is located at 10.1.10 - Campomanes (Getafe). Professor Cabras maintains broad interdisciplinary research interests spanning: Bayesian statistics and computational methods Biostatistical applications in nutrition, epidemiology, and medicine Causal inference and policy impact evaluation Machine learning applications in transportation and sports science Statistical modeling of complex biological systems His publication trends show strong emphasis on Bayesian methodologies applied to healthcare, public policy, and urban systems. Recent work demonstrates increasing integration of deep learning with traditional statistical approaches for problems in causal inference, pandemic forecasting, and transportation optimization. Professor Cabras leads several major research initiatives: Principal researcher for 'Statistical modelling for smart cities' (Beijing Metro Group, 2020-2023) Director of 'Contrato marco para servicios de consultoría estadística' (KROLL Advisory, 2022-2024) Co-researcher for 'Predicción de edad biológica mediante IA' (Fundación Mutualidad Abogacía, 2020-2021) He has supervised graduate research including theses on Bayesian metro occupancy modeling and maintains active collaborations through the Flores de Lemus Institute. His research has been applied in policy analysis, healthcare diagnostics, and urban planning internationally.

• Bayesian Statistics
• Biostatistics
• Epidemiology
• +۵ مورد دیگر

Ana Arribas Gil is a Professor at Carlos III University of Madrid , specializing in Functional Data Analysis , Longitudinal Data Analysis , and Statistical Modeling . Her work bridges theoretical statistics with applications in healthcare, demography, and computational biology. Research Focus : Functional data methods, Bayesian and penalized regression models, robust statistical techniques, and alignment algorithms. Methodology : Develops tools for time warping, outlier detection, and lasso-type regularization in mixed-effects models. Applications : Health expectancy estimation, children's well-being metrics, and pregnancy outcome prediction. Her recent publications emphasize penalized splines , dynamic time warping , and robust high-dimensional analysis . She has contributed open-source tools like the roahd R package for functional data.

• Functional Data Analysis
• Statistical Modeling
• Longitudinal Data Analysis
• +۴ مورد دیگر

Antonio Cuevas González is a Professor of Statistics and Probability at the Department of Mathematics, Faculty of Sciences, Autonomous University of Madrid. His office is located in Building 17, Room 503 at the university campus in Madrid (28049). He maintains an active research program in statistical methodology and has supervised numerous PhD students throughout his career. Professor Cuevas González specializes in several advanced areas of statistical theory, with primary research interests in set estimation, statistics with functional data, classification methods, and geometric approaches to statistical problems. His work bridges theoretical statistics with practical applications, particularly in high-dimensional and functional data settings. Over his career, he has developed novel methodologies for shape analysis, boundary estimation, and nonparametric classification that have become influential in the statistical community. An analysis of his recent publications reveals a strong focus on functional data analysis, with particular emphasis on regression models, classification techniques, and geometric approaches to statistical problems. His research increasingly integrates machine learning concepts with traditional statistical methodology, particularly through the use of reproducing kernel Hilbert spaces and other advanced mathematical frameworks. The trend shows growing interest in high-dimensional problems, dimensionality reduction techniques, and the development of computationally efficient statistical methods for complex data structures. SEIO-BBVA Foundation Award 2022 to the Best Methodological Contribution in the Statistics Field Medalla de oro 2025 de la Sociedad de Estadística e Investigación Operativa (SEIO) Professor Cuevas González has supervised numerous PhD students including Paloma Sanz (1988), Amparo Baíllo (2000), Alberto Rodríguez-Casal (2003), Alejandro Cholaquidis (2014), José L. Torrecilla (2015), Beatriz Bueno (2018), Luis A. Rodríguez (2024), and Antonio Coín (current). He is currently principal investigator for the research project 'Statistical techniques in high-dimensional spaces' (grant code PID2023-148081NB-I00) funded by the Spanish Ministry of Science, Innovation and Universities from September 2024 until September 2027, with Amparo Baíllo and Javier Cárcamo as main collaborators. As an active member of the statistical research community, Professor Cuevas González serves as Associate Editor for the journal ALEA and contributes to various research collaborations centered around functional data analysis, set estimation, and geometric statistics. His work often involves interdisciplinary collaborations, particularly with researchers in biomedical fields as evidenced by his publication on metabolic changes in mouse hearts.

• Set estimation
• Statistics with functional data
• Classification
• +۱ مورد دیگر

Dr. Andreas Leitherer is a Postdoctoral Researcher in the Department of Quantum Information Theory at the Institute of Photonic Sciences (ICFO). He holds a PhD in Physics from Humboldt University (Germany). His research focuses on interdisciplinary areas combining quantum systems analysis, materials science, and machine learning applications in crystallography and microscopy. Leitherer's work bridges theoretical quantum physics with computational methods, particularly in open quantum systems and algebraic quantum gravity. He also pioneers AI-driven approaches for automated crystal structure identification and materials characterization using electron microscopy. His publications since 2017 reflect a progression from foundational quantum gravity studies to applied machine learning in materials science. His research interests include quantum dynamics, computational materials science, Bayesian deep learning for crystallography, and data-centric materials discovery. Notably, he has contributed to developing robust machine learning frameworks for feature classification in atomic resolution images and exploring materials dataspaces through hybrid learning models. No scientific awards or formal student advisees are listed. His current research is affiliated with ICFO's Quantum Information Theory group, where he applies advanced computational techniques to solve problems at the intersection of quantum physics and materials innovation.

• Quantum Physics
• Materials Science
• Machine Learning
• +۳ مورد دیگر

Jesus Maria Rodriguez Presedo is a Professor in the Department of Electronics and Computing at the University of Santiago de Compostela (USC), affiliated with the Faculty of Physics and the Center for Research in Intelligent Technologies (CITIUS). He leads the GSI Group (Intelligent Systems Group) and focuses on biomedical signal processing, particularly in Heart Rate Variability (HRV) analysis, neural networks, and telemedicine solutions. His work integrates machine learning, stochastic modeling, and clinical applications to improve cardiac monitoring and diagnostic tools. Dr. Rodriguez earned his PhD from USC in 1994 with a thesis on intelligent ischemia monitoring, advised by Dr. Senén Barro and Dr. Ramón Ruiz Merino. His research emphasizes HRV analysis, biosignal interpretation, and software development (e.g., the RHRV package). He has contributed to telemonitoring systems for patients with chronic conditions like sleep apnea and COPD, leveraging multiagent systems and ubiquitous computing. His recent work explores nonlinear dynamics in time series, stochastic embeddings via variational autoencoders, and adaptive neural networks for multichannel signal analysis. Applications span cardiac event detection, ECG segmentation, and abductive frameworks for medical diagnostics.

• Heart Rate Variability Analysis
• Neural Networks
• Pattern Recognition
• +۳ مورد دیگر

Darío Ramos López is a Professor of Applied Mathematics at the Department of Mathematics, Universidad de Almería (UAL). He coordinates the Double Degree in Economics and Mathematics and teaches subjects such as Mathematics II and Numerical Methods and Optimization . His research focuses on Approximation Theory, Orthogonal Polynomials, Bayesian Networks, and Machine Learning applications in biomedical optics and data streams. He leads the research group on Approximation Theory and Orthogonal Polynomials (FQM229). His work integrates mathematical modeling with real-world applications, including corneal data analysis (via the rPACI R package) and environmental conservation projects. He developed the AMIDST toolbox , a Java library for scalable probabilistic machine learning, supporting analysis of massive data streams in finance and other domains. He has authored over 30 publications since 2010, with recent contributions to nonstationary data stream inference and keratoconus detection using Bayesian methods. Tutoring sessions are held in-person or online at CITE III INFORMATICA-MATEMAT (Office 1-400), with flexible scheduling via email appointment.

• Applied Mathematics
• Approximation Theory
• Orthogonal Polynomials
• +۵ مورد دیگر

Carlos David Zuluaga Ríos is a Postdoctoral Researcher at the Institute for Research in Technology (IIT) of the Comillas Pontifical University, affiliated with the Higher Technical School of Engineering (ICAI). He holds a B.Sc. and M.Sc. in Electrical Engineering from the Universidad Tecnológica de Pereira (2010, 2013), and a Ph.D. in Engineering (2018) from the same institution. His research focuses on electric power systems, renewable energy integration, distributed generation, and electric vehicle technologies. Key research areas include probabilistic modeling of energy systems, optimization algorithms for grid integration, and control systems for sustainable technologies. He has contributed to projects like improving tools for hybrid AC/DC power systems analysis under renewable energy penetration. Recent work includes studies on temperature effects on air quality solutions and neuro-probabilistic frameworks for EV energy demand forecasting. He has authored over 20 journal articles, conference papers, and a book chapter. His work has been published in journals like Applied Energy , IEEE Transactions on Power Systems , and Sustainability . He has presented at international conferences such as ICIAP and IREP. His current project (TED2021-130610B-C22) focuses on advancing hybrid power system tools with EU funding.

• Electric Power Systems
• Renewable Energy Integration
• Distributed Generation
• +۵ مورد دیگر

Cristina Vilaplana Prieto is a Full Professor at the University of Murcia , affiliated with the Faculty of Economics and Business and the Department of Fundamentals of Economic Analysis . Her research spans interdisciplinary domains including health economics, hydrology, artificial intelligence applications, and economic education. PhD in Economics from Universidad Carlos III de Madrid (2005) Key research groups: Applications of Economic Analysis, Labor Economics Her work focuses on: Valuation of informal care in Europe AI and deep learning in hydrological prediction Long-term care policy in Spain Economic education and financial literacy Impact of health shocks on aging populations Hydrological design innovation Recent publications analyze hydrological mechanisms , AI-driven flood forecasting , and social costs of healthcare . She has contributed to debates on GDP as a growth indicator and sustainable water use. Her projects include collaborations on PM2.5 characterization in Mexico City and AI-enhanced regional rainfall-runoff modeling in the Basque Country.

• Health Economics
• Labor Economics
• Hydrological Economics
• +۴ مورد دیگر

Daniel G. Figueroa is a Research Professor at CSIC's Instituto de Física Corpuscular, leading investigations in gravitational wave physics, cosmic inflation, and early universe phenomena. His research develops theoretical frameworks and computational tools to probe fundamental physics through cosmological signatures. Dr. Figueroa pioneered the CosmoLattice simulation code for modeling field dynamics in expanding spacetimes, enabling precision studies of reheating mechanisms, topological defect evolution, and gravitational wave generation. Current projects examine axion inflation dynamics, cosmic string signatures, and gravitational reheating scenarios. Recent publications analyze pulsar timing array data, nonlinear axion field behavior, and LISA mission science goals. His work supports next-generation gravitational wave observatories including LISA and Einstein Telescope through theoretical predictions and data interpretation frameworks. Dr. Figueroa maintains active collaboration within international consortia including the LISA Cosmology Working Group, advancing cosmic frontier research through interdisciplinary approaches.

• Gravitational Waves
• Cosmology
• Particle Physics
• +۲ مورد دیگر

Richard McElreath is a Full Professor and Director of the Department of Human Behavior, Ecology, and Culture at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany. He holds an honorary professorship in Human Evolutionary Ecology at Leipzig University. His research focuses on how culture shapes human evolution and adaptation, alongside Bayesian statistical methods. He authored the influential textbook Statistical Rethinking . McElreath earned his Ph.D. in Anthropology (2001) from UCLA and B.S. from Emory University (1995). He held academic positions at UC Davis from 2002–2018, progressing from Assistant to Full Professor. His work integrates theory with empirical studies on cultural dynamics, including fieldwork in diverse populations like the Peruvian Amazon and Faroe Islands. Research Interests: McElreath investigates cultural evolution, social learning strategies, and human behavioral ecology. He develops computational models to bridge theory and data, emphasizing interdisciplinary methods. His Bayesian statistics work provides tools for analyzing complex cultural processes. Grants & Awards: NSF grants (2015, 2014, 2012, 2010) UC Davis Chancellor’s Fellow ($25k, 2007) Max Planck postdoctoral fellowship (2001–2002) MacArthur Foundation grants (2000, 1998) Advising & Mentorship: Supervised PhD students including Tim Waring (now at University of Maine) and Bret Beheim (University of New Mexico). Supported postdocs like Kari Schroeder (Boston University) and John Bunce (Indiana University). Labs & Collaborations: Leads the Max Planck Department, collaborating on projects like cultural transmission in primates and urban settlement evolution in Ulaanbaatar. Develops software like R packages rethinking and glmer2stan .

• Anthropology
• Cultural Evolution
• Bayesian Statistics
• +۴ مورد دیگر

Maria Garcia de la Banda is a distinguished Professor at Monash University's Faculty of Information Technology, where she serves in the Department of Data Science and Artificial Intelligence (DSAI). With over 25 years of academic experience, she has held significant leadership roles including Deputy Dean (Research) until July 2022, overall Deputy Dean of the Faculty (2013-2016), and Head of the Caulfield School of Information Technology (2009-2011). She is currently a member of the ARC College of Experts and Co-Chair of the Monash-Woodside FutureLab. Her educational background includes a Doctor of Philosophy in Computer Science from the Universidad Politecnica de Madrid (awarded July 7, 1994) and an Ingeniero Informatico degree from the same institution (awarded March 1, 1992). Her PhD received the university's Best PhD Award. Garcia de la Banda's research spans multiple disciplines with a strong focus on constraint programming, combinatorial optimization, program analysis, and bioinformatics. She leads the Optimization research group within DSAI and has made significant contributions to declarative programming languages, parallelism, and automatic parallelization. Her interdisciplinary work bridges computer science with biological applications, particularly in protein structure analysis and computational drug design. Her publication record shows consistent contributions across constraint programming, optimization, and bioinformatics. Recent work demonstrates increasing interdisciplinary collaboration, with a notable expansion into bioinformatics applications alongside her core constraint programming research. She has maintained a strong presence at major conferences like CP (International Conference on Principles and Practice of Constraint Programming) while also building impactful industry collaborations. Her scientific recognition includes: Logan Fellowship (1997) - the first and only prestigious award of its kind in the Faculty of IT International Constraint Modelling Challenge winner (2005, with Peter Stuckey) Universidad Politecnica de Madrid's Best PhD Award (1994) Induction into the Monash Honour Roll (2021) Vice-Chancellor's Diversity and Inclusion Award (2020) As a research leader, Garcia de la Banda has secured over $20M in industry funding and $14M in nationally competitive funding, including $8M as Chief Investigator in 11 ARC grants (5 as lead). She has served as Area Editor of the Journal of Theory and Practice of Logic Programming since 2010 and on the Editorial Board of the Constraints journal since 2019. Her leadership extends to professional organizations, having served on the Executive Committees of both the Association of Logic Programming (2005-2008) and the Association of Constraint Programming (2017-2020), where she was President (2019-2020). She leads the Optimization research group within DSAI and collaborates extensively across Monash University and with industry partners. Her current major projects include HARNESS (Hierarchical Abstractions and Reasoning for Neuro-Symbolic Systems), the ARC Training Centre in Optimisation Technologies, and the Building 4.0 CRC project focused on better buildings through technology. These initiatives demonstrate her commitment to translating theoretical research into practical applications with real-world impact.

• Constraint Programming
• Combinatorial Optimization
• Program Analysis
• +۵ مورد دیگر

Ramon Sangüesa Sole is a Professor at Universitat Politècnica de Catalunya (UPC) within the School of Computer Science and Department of Computer Science . He is a member of the IDEAI-UPC (Intelligent Data Science and Artificial Intelligence Research Group) and KEMLG (Knowledge Engineering and Machine Learning Group). Fields of Interest : Artificial Intelligence, Machine Learning, Technoculture, Science Communication, Social Innovation Education : Doctor in Computer Science (UPC) His research focuses on integrating AI with social and environmental contexts, developing algorithmic transparency frameworks, and creating sociotechnical systems. Key projects include democratizing AI through citizen participation and applying machine learning to urban planning and educational innovation. His work emphasizes ethical considerations and interdisciplinary collaboration. Recent publications analyze topics like algorithmic transparency, urban AI applications, and student engagement technologies. Awards include the DISTINCIÓ JAUME VICENS VIVES for research excellence and the 8è Premi a la Qualitat en la Docència Universitària for educational innovation. Sangüesa leads projects in AI-driven design, environmental decision support systems, and personalized learning frameworks.

• Artificial Intelligence
• Machine Learning
• Technoculture
• +۴ مورد دیگر

MARIA CARMEN FERNANDEZ ROSELL is a Professor in the Department of Mathematical Analysis at the Faculty of Mathematics, University of Valencia. She is a member of the ESALDI research group (Spaces and Algebras of Differentiable Functions), focusing on functional analysis, operator theory, and time-frequency analysis, with significant interdisciplinary contributions in Bayesian statistics and econometrics. Her research spans Functional Analysis , Operator Theory , Harmonic Analysis , and Bayesian Modeling . She investigates ultradifferentiable functions, pseudodifferential operators, composition operators on function spaces, and time-frequency localization. Her statistical work includes Bayesian inference for fat tails, skewness, model averaging, and spatial modeling, often in collaboration with prominent statisticians like Mark F. J. Steel and Peter J. Green. The most recent publications highlight her expertise in composition operators on Gelfand-Shilov classes, spectral analysis, and characterizations of function spaces via harmonic oscillators. Her work bridges pure mathematical analysis with applied statistical modeling, particularly in econometrics and biological systems. On Bayesian modeling of fat tails and skewness (1998) Benchmark priors for Bayesian model averaging (2001) Annihilating sets for the short time Fourier transform (2010) Dynamics and spectra of composition operators on the Schwartz space (2018) She has supervised doctoral research, including Dr. Manuel Valdivia Ureña. Her work is supported by extensive collaborations, particularly with Antonio Galbis, José Bonet, and Mark F. J. Steel. She has contributed to major journals such as the Journal of Mathematical Analysis and Applications, Journal of Econometrics, and The Journal of Fourier Analysis and Applications. She is active in the research group ESALDI, which focuses on the theory of spaces and algebras of differentiable functions, exploring structural properties, duality, and applications to partial differential equations and signal analysis.

• Functional Analysis
• Operator Theory
• Harmonic Analysis
• +۷ مورد دیگر

Francesc De Paula Martori Adrian is an Adjunct Lecturer in the Department of Mathematics and Data Analytics at IQS School of Engineering, Universitat Ramon Llull. His work bridges data analytics and education, with a focus on learning technologies and statistical education. His research interests include: Learning Analytics and Educational Data Mining Student Modeling and Cognitive Tutors Interpretability in AI for Education Active Learning and Inference Models Statistics and Data Interpretation Education STEM and Business Education Innovations His publications reflect a strong interdisciplinary trend, combining computer science, education, and social sciences. Recent works explore automated assessment in statistics, the impact of corporate events on investors, and social media activism. The recurring themes in his research involve the use of data traces, modeling of learning processes, and ethical considerations in education and finance. Notable scientific contributions include: Development of trace-based assessment tools in statistics education Analysis of investor reactions in European merger waves Investigation of Twitter activism during Catalan political mobilization Innovative teaching methods for data interpretation Integration of Bayesian models in educational systems He has been actively involved in multiple research projects such as SEE: Sustainability, Economics, and Ethics, and DERULAG 2017, focusing on analytics and inquiry in STEM and business education. His collaborations span across disciplines and institutions, indicating a strong network in educational research and data science. While no formal advisees are listed, his role in research teams suggests mentorship and academic guidance. He is part of the ASISTEMBE research group, contributing to discipline-based education research using learning analytics and gamification. His work emphasizes ethical, sustainable, and justice-oriented education within Jesuit institutions.

• Learning Analytics
• Educational Technology
• Student Modeling
• +۱۲ مورد دیگر

Ana Alejandra Cabaña Nigro is a Professor in the Department of Mathematics at Universitat Autònoma de Barcelona, specializing in Advanced Stochastic Modelling. She holds a Ph.D. in Mathematical Sciences from the Central University of Venezuela (1993), a Master's in Mathematics from Universidad Simón Bolívar (1986), and a Bachelor's degree in Mathematics from the same institution (1985). Her research focuses on empirical processes, goodness-of-fit testing, time series analysis, and statistical modeling with applications to epidemiology and public health. She has contributed to methodologies for analyzing under-reported data, particularly during the COVID-19 pandemic, and has developed techniques for transforming empirical processes (TEPs) to enhance statistical testing power. Current interests include integrating neural networks with empirical processes to study variable importance. She has collaborated on projects such as stochastic modeling for biosciences (2022–2025) and statistical analysis of health risks (2016–2022), addressing topics like epidemiological data under-reporting and environmental health risks. She has supervised 47 academic works and actively participates in interdisciplinary initiatives like the Ecodep project. Key contributions include publications on ARCH models for under-reported time series, Bayesian analysis of pandemic burdens, and methods to quantify data misreporting in public health contexts. Her work contributes to UN Sustainable Development Goals related to health and well-being.

• Empirical Processes
• Goodness-of-Fit Testing
• Time Series Analysis
• +۳ مورد دیگر