Find Academic Supervisors

• Autonomous Vehicles
• Intelligent Transportation Systems
• Robotics
• +7 more

Miguel Ángel Sotelo Vázquez is a full Professor at the University of Alcalá, leading the INVETT Research Group (Intelligent Vehicles and Traffic Technologies). He holds the Department of Automatic Control and specializes in autonomous systems, particularly in path planning, sensor fusion, and human-vehicle interaction. His research integrates machine learning, robotics, and control theory to address challenges in intelligent transportation systems. He earned his Ph.D. in 2001 with a thesis on autonomous vehicle navigation in partially known environments. His work emphasizes real-world deployment, explainable AI, and safety-critical systems. Recent projects focus on lane change prediction, pedestrian behavior modeling, and cybersecurity for autonomous systems. Key contributions include neuro-symbolic frameworks for decision-making, real-time multi-physics field reconstruction, and cross-cultural studies of pedestrian interactions. He collaborates internationally on urban mobility resilience and hydrogen refueling infrastructure. Research Highlights : Development of knowledge graph-based prediction architectures Experimental validation of human-vehicle interaction in VR environments Creation of the SCOUT trajectory prediction framework

• Bayesian Statistics
• Financial Econometrics
• Markov Chain Monte Carlo
• +5 more

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.

Alexander P. FrankelView profile

Professor

• Mechanism Design
• Game Theory
• Contract Theory
• +2 more

Alexander P. Frankel is the Isidore Brown and Gladys J. Brown Professor of Economics at the University of Chicago Booth School of Business. His research focuses on mechanism design, game theory, and contracting, with applications across various economic domains. Previously, he worked at Yahoo! Research and has published in top economics journals including the American Economic Review and Journal of Political Economy. Education: BS in Mathematics from the University of Chicago BA in Economics from the University of Chicago PhD in Economic Analysis and Policy from Stanford Graduate School of Business Frankel specializes in information economics, mechanism design, and contract theory. His work explores how information structures affect economic outcomes, with applications to delegation, signaling, and strategic communication. He has made significant contributions to understanding how information is designed and used in strategic settings, particularly in areas such as R&D investment, admissions policy, and central banking. Frankel's publication record demonstrates a consistent focus on information design and its applications across diverse contexts. His work spans theoretical developments in signal structures and information hierarchies to practical applications in education policy, corporate decision-making, and monetary policy. The research shows increasing sophistication in modeling information environments and their economic consequences, with recent work addressing contemporary issues like test-optional admissions while maintaining strong theoretical foundations. As a faculty member at Chicago Booth, Frankel teaches Microeconomics (33001) and The Economics of Contracts (33931). His research has received attention in major media outlets including the New York Times, Chicago Tribune, and Freakonomics blog, indicating the broader relevance of his theoretical work to practical economic issues.

Scott NelsonView profile

Associate Professor

• Consumer Credit Markets
• Financial Regulation
• Information Asymmetry
• +7 more

Scott Nelson is an Associate Professor of Finance at the University of Chicago Booth School of Business. His research bridges consumer credit markets, regulatory frameworks, and behavioral economics, with a focus on how information asymmetries and algorithmic decision-making shape market outcomes. He has contributed to understanding the impacts of the 2009 CARD Act, eviction protections in housing markets, and fairness in credit scoring systems. PhD in Economics, Massachusetts Institute of Technology BA (summa cum laude) in Economics and Mathematics, Yale College Nelson's work employs diverse data sources, including credit reports, court filings, and tax records, combined with structural models to analyze consumer and firm behavior. Key themes include regulatory efficiency, validity disparities in predictive models, and the welfare implications of policy interventions. His articles reveal trends in algorithmic regulation (2025), eviction dynamics (2025), credit scoring disparities (2024), and public finance impacts on Chinese real estate (2023). These publications highlight interdisciplinary methodologies integrating economics, law, and data science. Scientific awards include the AQR Top Finance Graduate Award (2018) and National Science Foundation Graduate Research Fellowship. He has held postdoctoral roles at the Consumer Financial Protection Bureau/Princeton University and visiting research positions at the Federal Reserve Bank of Boston.

Andrea Meilán-VilaView profile

Assistant Professor

• Nonparametric Estimation
• Directional Statistics
• Kernel Smoothing
• +3 more

Andrea Meilán-Vila is an Assistant Professor in the Department of Statistics at Universidad Carlos III de Madrid since 2021, holding a Juan de la Cierva Fellowship since 2023. She earned her PhD in Statistics from Universidade da Coruña (2021) and previously served as a Postdoctoral Fellow at Universidade de Santiago de Compostela's Department of Statistics, Mathematical Analysis and Optimisation. Her research focuses on nonparametric methods for analyzing complex data types, including directional, spatial, and functional data. Key areas include kernel smoothing techniques, goodness-of-fit testing for regression models, and spatial trend estimation. She serves as an Associate Editor for the Journal of Nonparametric Statistics . Recent work emphasizes applications in climate science (temperature curve modeling), fluid dynamics (wake flow control), and biomedical imaging (hippocampus shape analysis). Her methodologies address challenges like sparse data estimation and spatial correlation in regression frameworks. Key Projects: STENED (Stein-based goodness-of-fit tests for non-Euclidean data) Awards: Juan de la Cierva Fellowship (2023) Publications span journals like Journal of Fluid Mechanics , Statistical Papers , and TEST , with a focus on methodological advancements in statistical modeling and computational validation.

Sebastian Tornil SinView profile

Associate Professor

• Automatic Control
• Fault Detection and Diagnosis
• Water Distribution Networks
• +6 more

Sebastian Tornil Sin is an Associate Professor in the Department of Automatic Control at Universitat Politècnica de Catalunya (UPC), affiliated with the Institut de Robòtica i Informàtica Industrial (IRI), a joint center of UPC and CSIC. His work focuses on fault detection, diagnosis, and monitoring in industrial and water distribution systems. His research interests lie at the intersection of automatic control , data-driven modeling , and industrial applications . He specializes in leak detection and localization in water networks using sensor data, classifiers, and reasoning frameworks like Dempster-Shafer and Bayesian methods. His work also extends to sensor placement optimization and human-robot task design in academic environments. The recent publications (2017–2022) show a consistent focus on applying machine learning and control theory to real-world infrastructure problems, particularly in water networks. Trends include hybrid data-expert systems, classifier-based fault localization, and incremental sensor deployment strategies. Scientific Awards: No awards mentioned in the provided text. Sebastian Tornil Sin actively contributes to research through collaborations with experts such as V. Puig, J. Blesa, and A. Soldevila. While no formal advising or grant information is available, his extensive publication record in journals like IEEE Transactions on Control Systems Technology and Computers and Chemical Engineering indicates strong research leadership and project involvement. He is part of the research team at the Institut de Robòtica i Informàtica Industrial (IRI) , where he contributes to projects in fault detection, monitoring systems, and robotics applications in industrial and urban infrastructure.

Sanjay K DharView profile

Professor

• Strategic Marketing Management
• Brand Management
• New Product Development
• +12 more

Sanjay K. Dhar, the James M. Kilts, Jr. Professor of Marketing at the University of Chicago Booth School of Business, is a leading scholar in strategic marketing management and retail dynamics. His work bridges empirical economic modeling with actionable business insights. Education: B.Tech (IIT), MBA (IIM), PhD (UCLA Anderson School) Affiliation: Faculty member at Chicago Booth since 1992 Research interests span brand management , consumer behavior , and retail strategy , with a focus on how economic factors and marketing mix variables influence household budget allocations and brand performance . His empirical work leverages massive datasets like Nielsen Homescan to uncover geographic patterns and long-term consumer trends . Recent publications examine on-screen cigarette product placement effects, WIC program impacts on cereal consumption, and retail format competition . His 2025 under-review paper extends these insights to multi-category budget reallocation during macroeconomic shifts. Scientific Recognition includes: William O’Dell Award Finalist (2012) Paul Green Award (2008) John D. C. Little Award (1995) McKinsey Teaching Excellence Award (2000) Hillel Einhorn Teaching Awards (2008, 2012) Dhar’s teaching philosophy emphasizes mastering fundamentals to solve complex marketing problems. He advises firms on strategic marketing and new product development while maintaining active research in consumer packaged goods (CPG) industries.

Puig Oriol, XavierView profile

Assistant Professor

• Bayesian data analysis
• Model Based clustering
• Multinomial model
• +7 more

Xavier Puig is an Assistant Professor at the Universitat Politècnica de Catalunya (UPC) , affiliated with the Department of Statistics and Operations Research and the School of Mathematics and Statistics (FME). He is a member of the ADBD - Analysis of Complex Data for Business Decisions and GRBIO - Biostatistics and Bioinformatics Research Group . His research focuses on Bayesian data analysis , with applications in Epidemiology Ecology Public health Political science Industrial quality control Marketing analytics Recent publications reveal a strong trend in Bayesian spatiotemporal modeling for health data, alcohol-migraine interaction studies, and industrial error rate monitoring . His work combines methodological innovation with real-world applications across diverse sectors. Collaborations include researchers from biostatistics, clinical epidemiology, and industrial engineering. He has contributed to 44 indexed journal articles and participated in 49 congress presentations , with recent projects focusing on competitive research and non-competitive industrial collaborations in statistical modeling.

• Quantum Machine Learning
• Reinforcement Learning
• Medical Informatics
• +12 more

Jose David Martin Guerrero is a Professor in the Department of Electronic Engineering at the School of Engineering, Universitat de València. His research is conducted within the Intelligent Data Analysis Laboratory (IDAL), focusing on the intersection of quantum computing, machine learning, and biomedical applications. His research interests span Quantum Machine Learning , Reinforcement Learning for Quantum Control , Deep Learning for Medical Image Analysis , and Long-COVID Symptom Trajectory Modeling . He applies advanced AI techniques to solve complex problems in physics, healthcare, and engineering, particularly in modeling prostate cancer, hemodialysis, and post-COVID neurological and functional impairments. The recent publications highlight a strong trend toward integrating quantum computing with classical machine learning, especially in developing quantum neural networks, quantum autoencoders, and reinforcement learning for quantum gate optimization. Simultaneously, a significant body of work uses clustering, deep learning, and Bayesian modeling to analyze longitudinal clinical data from multicenter studies like LONG-COVID-EXP, aiming to understand symptom persistence and recovery patterns in post-COVID patients. Dr. Martin Guerrero earned his PhD from the Universitat de València in 2004 with a thesis on unsupervised techniques for web trend detection and collaborative filtering-based recommendation systems, supervised by Dr. Emilio Soria Olivas and Dr. Paulo Jorge Gomes Lisboa. His work demonstrates active collaboration across disciplines, particularly in clinical research involving multicenter studies, and in quantum computing applications in physics and finance. While no specific grants are mentioned, his involvement in large-scale clinical studies and quantum computing research suggests significant project leadership and funding acquisition. He leads research within the Intelligent Data Analysis Laboratory (IDAL) , which focuses on self-organizing maps, clustering, learning vector quantization, and their applications in security, healthcare, and quantum systems.

• Financial Econometrics
• Macroeconomic Forecasting
• Stochastic Volatility Models
• +3 more

Maria Helena Lopes Moreira da Veiga is a Full Professor at Universidad Carlos III de Madrid, affiliated with the Macroeconomic and Financial Prediction and Analysis Research Group within the Statistics Department. Her work bridges economics, statistics, and financial engineering with focus on volatility modeling and macroeconomic forecasting. Principal investigator for geopolitical risk and financial contagion projects (2024-2026, 2019-2020) Key research areas: stochastic volatility models, energy markets, Bayesian methods, outlier detection Collaborates with institutions across Denmark, Portugal, France, Spain, and Canada Her publications demonstrate expertise in financial econometrics, particularly asymmetric volatility models and their applications in energy and stock markets. She employs advanced computational techniques like data cloning and wavelet analysis to address model uncertainty and risk measurement. Current projects focus on climate risk indices, AI-driven economic valuation, and high-dimensional time series prediction. She has received continuous funding from national agencies for uncertainty modeling in macroeconomic and financial contexts since 2006.

José E. ChacónView profile

Professor

• Nonparametric Kernel Smoothing
• Cluster Analysis
• Mathematical Statistics
• +3 more

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.

• Artificial Intelligence
• Wireless Communications
• Machine Learning
• +5 more

Giovanni Geraci is a Tenured Professor at the Department of Information and Communications Technologies, Universitat Pompeu Fabra. Specializing in applying AI/ML to wireless communications, he focuses on GenAI for data augmentation and LLMs for telecom applications. His expertise spans 5G/6G cellular and Wi-Fi (IEEE 802.11be/bn) technologies, informed by roles at Nokia Standards, Telefónica, and Bell Labs. He holds the IEEE Communications Theory Early Achievement Award (2023) and the IEEE Outstanding Young Researcher Award (2018). Research interests include AI-driven network design, UAV communications, and next-generation wireless standards. He has authored over 100 publications, including Best Paper Awards at IEEE ICC (2025), Globecom (2022), and PIMRC (2019). Geraci has led academic roles like Head of Telecommunications Engineering and is an IEEE Distinguished Lecturer, delivering over 100 keynotes globally. He created the IEEE course 'Fundamentals of Wi-Fi' and teaches Signals and Systems, praised for his 'fantastic, passionate, and supportive' teaching style. Geraci’s work bridges academia and industry, addressing challenges in cellular networks for UAV corridors, millimeter-wave channel modeling, and integrated terrestrial/non-terrestrial networks. His recent research explores AI/ML applications for 6G, Wi-Fi 7/8, and data-driven optimization of telecom infrastructure.

Christian IgelView profile

Professor

• Deep Neural Networks
• Kernel-Based Methods
• Evolution Strategies for Single- and Multi-Objective Optimization
• +6 more

Christian Igel is a Professor at the Department of Computer Science (DIKU) and Director of the SCIENCE AI Centre at the University of Copenhagen. His academic journey includes a Computer Science degree from the Technical University of Dortmund, a Doctoral degree from Bielefeld University, and a Habilitation degree from Ruhr-University Bochum. He has held academic positions since 2003, including a W1 Professorship at Ruhr-University Bochum before joining DIKU in 2010 as a Professor with Special Duties in Machine Learning and becoming a Full Professor in 2014. Research Interests: Deep learning, kernel methods, evolutionary optimization, reinforcement learning, PAC-Bayesian analysis, and ML applications for sustainability. Affiliations: SCIENCE AI Centre (Director), European Lab for Learning and Intelligent Systems (ELLIS Fellow). Editorial Roles: Editor of KI – Künstliche Intelligenz , Associate Editor of Evolutionary Computation Journal and Artificial Intelligence Journal . Education: Technical University of Dortmund (Computer Science), Bielefeld University (Doctorate), Ruhr-University Bochum (Habilitation).

• Quantitative Methods
• Computational Finance
• Bayesian Statistics
• +9 more

Cristian González Martel is a full Professor at the Department of Quantitative Methods in Economics and Management, University of Las Palmas de Gran Canaria. He leads multiple research projects analyzing tourism markets, financial systems, and complex network applications through big data and Bayesian statistical techniques. Current research focuses on maritime connectivity, international trade, and environmental emissions (2024) Developing tourism intelligence platforms for Gran Canaria (2023) Maintaining tourism data dashboards for Lanzarote (2022) His work integrates machine learning with economic modeling, particularly in foreign exchange markets and health economics. Projects demonstrate consistent application of spatial interaction models, complex network analysis, and optimization techniques across regional and international contexts. Key collaborations include agreements with the Inter-American Development Bank and Canary Island regional governments. Current email contact: christian.gonzalez@ulpgc.es

• Machine Learning
• Bandit Algorithms
• Reinforcement Learning
• +2 more

Hamish Flynn is a Researcher in the Department of Engineering Artificial Intelligence and Machine Learning. His work focuses on developing advanced algorithms for sequential decision-making systems, with a strong emphasis on theoretical guarantees and practical implementations. His research interests span several core areas of machine learning: Bandit algorithms (contextual, linear, and multi-armed variants) PAC-Bayesian theory and applications Reinforcement learning systems Online optimization under uncertainty Statistical learning theory Flynn's recent publications (2022-2025) demonstrate consistent focus on improving sequential learning methods. Key research themes include: non-iid noise modeling in bandits, confidence bound optimization for sequential regression, sparse nonparametric methods, and hardware-efficient machine learning implementations. His work frequently combines theoretical frameworks (PAC-Bayes, martingale theory) with practical applications in reinforcement learning and adaptive systems. No scientific awards, students advised, grant activities, or lab affiliations are mentioned in the available information.