جستجوی استاد راهنمای دانشگاهی

Rosa Ferrer is a Serra-Hunter Associate Professor at Universitat Pompeu Fabra and an Associate Research Professor at the Barcelona School of Economics. She also serves as a Research Affiliate at CEPR in London and as Co-Director of the Master in Competition, Regulation & Markets program. Her academic journey began with a PhD from Vanderbilt University, establishing a strong foundation for her research career in economics. Dr. Ferrer's research spans several key areas within economics, with a particular focus on Law and Economics, Industrial Organization, and the Digital Economy. She combines empirical and theoretical methods to explore consumer choice, demand estimation, gender economics, and law enforcement challenges. Her work is inspired by Gary Becker's view on economic incentives beyond monetary rewards, applying industrial organization and applied microeconomics to diverse applications. She has made significant contributions to understanding gender gaps in professional performance, particularly in the legal profession, as well as consumer behavior in product-harm crises and digital markets. Her publication record demonstrates consistent scholarly output across high-impact journals including Management Science, Journal of Political Economy, and Review of Economic Studies. Her recent work focuses on digital economy challenges, gender differences in media consumption, and career trajectories in professional settings. This research portfolio shows a clear progression from foundational work in law enforcement and decision theory to contemporary issues in digital markets and gender economics. As Co-Director of the Master in Competition, Regulation & Markets, Dr. Ferrer plays a significant role in shaping the next generation of economists and policy experts. Her leadership in this program reflects her commitment to bridging academic research with practical policy applications in competition and regulatory frameworks.

• Law and Economics
• Industrial Organization
• Organization Economics
• +۶ مورد دیگر

Bryon Aragam is an Associate Professor of Econometrics and Statistics and Robert H. Topel Faculty Scholar at the University of Chicago Booth School of Business. His research focuses on the intersection of causality, statistical machine learning, and probabilistic modeling, with particular emphasis on applications to artificial intelligence systems including large language models like ChatGPT and generative models like DALL-E. Dr. Aragam completed his PhD in Statistics and a Masters in Applied Mathematics at UCLA, where he was an NSF graduate research fellow. Prior to joining the University of Chicago, he was a project scientist and postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University. Research Focus: Causal structure learning in probabilistic generative models Key Areas: Causal machine learning, deep generative models, latent variable models, statistical learning theory Applications: AI interpretability, ethics, and fairness in artificial intelligence systems Teaching: Business Statistics, Econometrics and Statistics Colloquium His recent publications demonstrate a strong theoretical foundation combined with practical applications, particularly in understanding and improving AI systems. His work spans causal discovery, graphical models, deep learning, and latent variable modeling, with particular attention to the theoretical properties of these methods and their applications to real-world AI challenges. The research shows a progression toward increasingly complex problems in causal representation learning and AI interpretability. Scientific Awards: Robert H. Topel Faculty Scholar NSF Graduate Research Fellow Dr. Aragam's work has been published in top statistics and machine learning venues including the Annals of Statistics, Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), and the Journal of Machine Learning Research (JMLR). His research group publishes broadly across both statistical and machine learning communities, demonstrating the interdisciplinary nature of his work at the intersection of statistics, machine learning, and causal inference. As a data science consultant for technology and marketing firms, Dr. Aragam has applied his expertise to problems in survey design, customer retention, logistics, and ranking, bridging the gap between theoretical research and practical applications.

• Causality
• Statistical Machine Learning
• Probabilistic Modeling
• +۶ مورد دیگر

Nicholas Polson is the Robert Law, Jr. Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His academic career centers on Bayesian statistics with applications in financial econometrics and machine learning. Polson's research interests span Bayesian statistics, financial econometrics, Markov chain Monte Carlo methods, particle learning, and deep learning applications in finance. His work has significantly contributed to understanding stochastic volatility models and developing new algorithms for Bayesian inference. He has pioneered applications of deep learning in asset pricing, portfolio management, and financial prediction, demonstrating how neural networks can detect complex patterns invisible to traditional financial models. His recent publication trends reveal a strong focus on integrating deep learning with financial econometrics, particularly in developing characteristics-sorted factor models, portfolio optimization techniques, and explaining the performance differences between active and passive investment strategies. His work consistently bridges theoretical statistical methods with practical financial applications, with a particular emphasis on nonlinear modeling and high-dimensional data analysis. His article 'Bayesian Analysis of Stochastic Volatility Models' was named one of the most influential articles in the 20th anniversary issue of the Journal of Business and Economic Statistics Polson teaches courses including 'Bayes, AI and Deep Learning' and 'Business Statistics' at Chicago Booth, with scheduled offerings for both 2024-2025 and 2025-2026 academic years. His work has been featured in Chicago Booth Review, where he has contributed insights on statistical analysis in chess, machine learning applications in money management, and the odds of cheating in competitive settings. His research demonstrates the powerful intersection of Bayesian statistics, financial modeling, and modern machine learning techniques.

• Bayesian Statistics
• Financial Econometrics
• Markov Chain Monte Carlo
• +۵ مورد دیگر

Jorge Garcia Vidal is a Professor in the Department of Computer Architecture at the School of Computer Science, Universitat Politècnica de Catalunya (UPC). He is a key member of the CNDS - Computer Networks and Distributed Systems research group, with a sustained record of research activity from the late 1980s to the present, including publications projected into 2025. His work bridges theoretical network performance analysis and applied IoT systems, particularly in environmental monitoring. His research interests center on Computer Networks , Internet of Things (IoT) , Sensor Networks , and Data Quality in IoT . He has made significant contributions to ATM network performance, medium access control, and traffic modeling. More recently, his focus has shifted to air quality monitoring using low-cost sensor networks, employing techniques in Graph Signal Processing , Machine Learning , and Anomaly Detection to improve data reliability and estimate pollutants like black carbon. The recent article trends show a strong emphasis on developing data-driven frameworks, virtual sensors, and robust models for environmental IoT platforms. His work integrates advanced signal processing and machine learning to address the challenges of heterogeneous, low-cost sensor data in urban settings. His scientific achievements have been recognized with awards including the Premio Extraordinario de Doctorado and the Premio Mejor Tesis Doctoral . He has advised several doctoral students, including Pau Ferrer-Cid, David Fusté Vilella, Steluta Iordache, and Julian David Morillo Pozo. He is actively involved in numerous competitive and non-competitive R&D projects, such as those related to digital twins, IoT platforms for smart cities, and nature-based urban solutions, often funded by state and regional programs. He collaborates extensively within UPC and with external partners. His research is conducted primarily within the CNDS research group at UPC, a collaborative environment focused on computer networks and distributed systems, with connections to broader initiatives in smart cities and environmental monitoring.

• Computer Networks
• Internet of Things (IoT)
• Sensor Networks
• +۷ مورد دیگر

Domingo Savio Rodríguez Baena is a Professor at Pablo de Olavide University, affiliated with the Department of Computer Languages and Systems. His research focuses on data mining, bioinformatics, and computational biology, with a particular emphasis on biclustering algorithms, gene co-expression networks, and high-performance computing applications. PhD in Engineering, Data Science, and Bioinformatics (2012) from Pablo de Olavide University His work spans interdisciplinary domains, including recommender systems , livestock behavior analysis , and biological data interpretation . Recent articles highlight his contributions to multi-GPU optimization , ensemble learning , and historical database construction . Key collaborations include the DATAi Intelligent Data Analysis and DASE Data Analytics Science & Engineering research groups. He has developed tools like the CyEnGNet–App for gene network visualization and BIGO for gene enrichment analysis. Contact: dsrodbae@upo.es

• Data Mining
• Bioinformatics
• Machine Learning
• +۳ مورد دیگر

Juan Jesús Donaire Benito is an Assistant Professor in the Department of Mathematics at Universitat Autònoma de Barcelona (UAB). His research focuses on complex analysis, real analysis, and functional analysis, with particular emphasis on topics such as Hausdorff dimension, analytic functions, and Zygmund spaces. He holds a Ph.D. in Mathematics (1995) and a Degree in Sciences (1990), both from UAB. Donaire has been actively involved in research projects funded by grants from the European Regional Development Fund (FEDER), the Spanish Ministry of Economy and Competitiveness (MINECO), and other institutions. His work spans geometric function theory, singular integrals, and boundary value problems. Education: Ph.D. in Mathematics, Universitat Autònoma de Barcelona (1995) Degree in Sciences, Universitat Autònoma de Barcelona (1990) Research Projects (Selected): STOCHASTIC PROPERTIES IN GEOMETRIC FUNCTION THEORY (2022–2026, FEDER) Aspectos probabilísticos y geométricos de la teoría de funciones (2018–2022, MINECO/FEDER) Diversos aspectos de la teoría de funciones y aplicaciones (2009–2012, MEC) Research Interests: Focuses on complex analysis, including topics like singular integrals, Hausdorff dimension, analytic function theory, and boundary behavior of functions. He has contributed to studies on Zygmund spaces, removable singularities, and harmonic gradients.

• Complex Analysis
• Real Analysis
• Functional Analysis
• +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.

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

Álvaro Heredia Lidón serves as an Associate Professor in the Department of Engineering at Universitat Ramon Llull, specializing in Human-Environment Research with a focus on advanced computational methods for medical applications. His academic position bridges engineering, computer science, and clinical research, with particular emphasis on developing accessible diagnostic tools through innovative image analysis techniques. Dr. Heredia Lidón's research centers on the development and application of computer vision and deep learning methodologies for 3D facial biomarker analysis in medical diagnostics. His work addresses critical challenges in rare genetic disorders, particularly Turner syndrome and Down syndrome, through advanced facial phenotyping techniques. He has pioneered approaches for automated 3D landmarking of anatomical structures, orientation detection of head reconstructions, and the extraction of diagnostic biomarkers from various imaging modalities including MRI and smartphone-based 3D reconstructions. His publication portfolio reveals a strong trend toward creating low-cost, accessible diagnostic solutions that can be deployed in resource-limited settings. The research demonstrates significant interdisciplinary collaboration between engineering, genetics, and clinical medicine, with particular focus on translating computational advances into practical clinical applications. His work on multi-view consensus networks and geometric morphometric methodologies represents cutting-edge innovation in the field of medical image analysis. Dr. Heredia Lidón has secured substantial research funding, including serving as Principal Investigator for the FI-2022 Joan Oró grant for predoctoral researcher training (2022-2025) from the Catalan Department of Research and Universities. He is actively involved in multiple collaborative research projects, most notably the BeNeXT project which uses Turner syndrome as a model for developing biomarker-enhanced diagnostic tools for rare disorders. His laboratory work focuses on the Human-Environment Research group at Universitat Ramon Llull, where he collaborates with an extensive interdisciplinary team including medical researchers, geneticists, and computer scientists. This collaborative environment fosters innovation at the intersection of engineering and healthcare, with particular emphasis on making advanced diagnostic capabilities more widely accessible through cost-effective imaging solutions and automated analysis pipelines.

• Computer Vision
• Machine Learning
• 3D Facial Biomarkers
• +۴ مورد دیگر

Guillem Alenyà Ribas is a Researcher and Director of the Perception and Manipulation group at the Institut de Robòtica i Informàtica Industrial (IRI), a joint center of the Spanish National Research Council (CSIC) and the Polytechnic University of Catalonia (UPC), Barcelona. His research focuses on integrating robots into human environments, particularly in assistive robotics and the manipulation of deformable objects such as garments. His research interests span Human-Robot Interaction (HRI) , assistive robotics , explainable AI , robot personalization , deformable object manipulation , and benchmarking . He aims to make robots more transparent, adaptive, and safe in real-world applications. His work combines AI planning, vision, learning from demonstration, and user-centered design to develop systems that can assist in healthcare, domestic, and industrial settings. Recent publications reveal a strong trend in explainability and personalization in HRI, with a focus on frailty assessment in elderly care , real-time explanations , and counterfactual reasoning . His team also advances benchmarking in cloth manipulation , 3D reconstruction of clothed humans , and ontology-based reasoning for robot plans . This reflects a multidisciplinary approach combining robotics, AI, and social sciences. Coordinator of ROB-IN, CLOE-GRAPH, and BURG projects Principal Investigator in SeCuRoPS and DEMETER 5.0 Former coordinator of SIMBIOTS, HuMoUR, and SOCRATES He has supervised numerous PhD students, many of whom have received prestigious awards such as the Georges Giralt PhD Award and the AIHUB.CSIC Prize. His leadership in technology transfer and European projects highlights his role in bridging academic research with real-world applications. He leads the Perception and Manipulation group at IRI, fostering collaboration across disciplines and mentoring a large team of researchers, PhD students, and technical staff. The group actively contributes to open science through standardized datasets and reproducible methodologies.

• Robotics
• Human-Robot Interaction
• Assistive Robotics
• +۹ مورد دیگر

Jose Apesteguia is an ICREA Research Professor at Universitat Pompeu Fabra, affiliated with the Department of Economics and Business. He received his PhD in Economics from the Public University of Navarra in 2001, followed by postdoctoral work at the University of Bonn and a Ramón y Cajal fellowship at UPF. His research integrates behavioral economics, decision theory, and political economy, with a focus on modeling human choice anomalies and stochastic behavior. Apesteguia's work spans: Behavioral adaptations in economic decisions Random utility models for heterogeneous preferences Language-driven moral cognition in cross-cultural contexts His publications emphasize quantitative rigor, employing field experiments and theoretical frameworks to dissect irrationality in human behavior. Honors include the competitive Ramón y Cajal Fellowship and the prestigious ICREA Research Professorship . He teaches courses like 'Bounded Rationality in Choice' (PhD) and 'Behavioral Decision Theory' (BSc), and mentors students through CREi-Fundacio Catalunya La Pedrera's economics outreach program.

• Behavioral economics
• Decision theory
• Bounded rationality
• +۱ مورد دیگر

Alessandro Tarozzi is a Full Professor at Universitat Pompeu Fabra (UPF) and the Barcelona School of Economics. He holds a PhD from Princeton University and has been on leave since 2021. His research focuses on Development Economics, Microeconometrics, and Health Economics, with a particular emphasis on poverty measurement, health interventions, and public policy evaluation in low- and middle-income countries. Education: PhD in Economics from Princeton University. Research interests include the intergenerational transmission of health, microcredit impacts, environmental health risks (e.g., arsenic contamination in Bangladesh), and socioeconomic status gradients in health outcomes. He has conducted field experiments in India and Bangladesh on topics like malaria prevention and arsenic risk communication. Editorial roles include Co-Editor of the World Bank Economic Review (2024–present), and Associate Editor for Journal of Development Economics , Journal of Human Resources , and others. He is affiliated with BREAD, CEPR, and J-PAL. Key contributions include studies on child nutrition in India, microfinance effects in Ethiopia, and the effectiveness of information campaigns on health behaviors. His work bridges econometric rigor with real-world policy applications in development settings.

• Development Economics
• Microeconometrics
• Health Economics
• +۳ 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.

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

Daniel Díaz Sánchez is an Associate Professor at the Telematics Engineering Department of Carlos III University of Madrid , where he serves as Deputy Director of Laboratories . His research focuses on IoT security , Post-Quantum Cryptography , and network protocols . Email: daniel.diaz@uc3m.es Office: 4.0.F04 - Quevedo Towers (Leganés) His recent work explores DNSSEC soft delegation for microservices, quantum random number generators , and machine learning applications in cybersecurity. Key publication themes include IoT credential management , secure communication protocols , and control system optimization .

• Internet of Things (IoT)
• Network Security
• Post-Quantum Cryptography
• +۶ مورد دیگر

Nikita Zhivotovskiy is a tenure-track Assistant Professor in the Department of Statistics at the University of California, Berkeley. He previously held postdoctoral positions at ETH Zürich and Google Research, Zürich, and was affiliated with the Technion I.I.T. His academic background includes a PhD from the Moscow Institute of Physics and Technology, with affiliations during his studies at the Institute for Information Transmission Problems, Higher School of Economics, and Skoltech. His research lies at the intersection of mathematical statistics, probability theory, and learning theory . Key areas include robust estimation, online learning, statistical learning theory, algorithmic stability, and high-dimensional statistics. His work emphasizes theoretical foundations of machine learning, with a focus on generalization, risk bounds, and learning under non-standard assumptions. The recent publications reflect a strong trend in theoretical machine learning , particularly in understanding the limits and optimality of learning algorithms. Topics span PAC learning, online classification, private estimation, and clustering, often achieving dimension-free or high-probability guarantees. His work frequently appears in top venues such as NeurIPS, COLT, and FOCS, indicating significant impact in the field. Scientific Awards: Best Paper Award at Conference on Learning Theory (COLT), 2020 Nikita Zhivotovskiy has served as a reviewer for leading journals including Annals of Statistics , Probability Theory and Related Fields , and IEEE Transactions on Information Theory , and as a senior program committee member for COLT and ALT. He has co-taught courses at ETH Zürich and has advised or collaborated with numerous researchers, though formal students are not listed. His research has been supported through academic and industrial collaborations, including at Google Research. His work is embedded within the theoretical machine learning community, with active participation in workshops such as those at BIRS, and a growing body of work that bridges statistical theory and algorithmic design. While no formal lab is mentioned, his research group at UC Berkeley likely focuses on foundational aspects of learning and inference.

• Mathematical Statistics
• Probability Theory
• Learning Theory
• +۷ مورد دیگر

Josep Cuguero Escofet is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Systems Engineering, Automation, and Industrial Informatics, and the School of Industrial, Aerospace, and Audiovisual Engineering (ESEIAAT) in Terrassa. His research focuses on control systems, automation, water network management, robotics, and fault detection. He has led or contributed to numerous R&D projects, including those on water distribution network modeling, UAV perception, and robust fault detection in industrial systems. His work emphasizes practical applications in infrastructure health monitoring, energy efficiency, and environmental sustainability. Key projects include the development of advanced monitoring systems for water networks in Terrassa and contributions to autonomous vehicle control. He has advised doctoral students like Julian Cayero Becerra and collaborated with groups like CS2AC and SIC. Notable publications span topics like leak detection algorithms, UAV safety protocols, and robust control strategies for uncertain systems. His research consistently bridges theoretical advancements with real-world engineering challenges.

• Control Systems
• Automation Engineering
• Water Network Management
• +۳ مورد دیگر