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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
• +5 more

Pedro Galeano is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid (UC3M) since 2009. He holds a PhD in Statistics (2004) under Prof. Daniel Peña, focusing on multiple time series. Previously, he served as Visiting Assistant Professor of Statistics and Econometrics at the University of Chicago’s Graduate School of Business and as a Postdoctoral Fellow at the Department of Statistics and Operations Research at Universidade de Santiago de Compostela. His research focuses on time series analysis, outlier detection, Bayesian inference in financial models, and functional data analysis with applications to missing data. He is an Associate Editor of the Journal of Time Series Analysis and advises the Heliyon journal. Key contributions include developing methodologies for detecting structural breaks, modeling systemic risk via copula approaches, and advancing robust statistical techniques for high-dimensional data. Active in academic leadership, Galeano co-organized the NICDA Workshop 2025 and has published extensively on topics like dynamic factor models, sequential parameter change detection, and functional data applications in energy markets. His work bridges theoretical statistics with practical applications in finance, economics, and environmental science.

• Outlier Detection
• Time Series Analysis
• Bayesian Inference
• +3 more

Francisco Manuel Bernal Martínez is an Associate Professor in the Department of Mathematics at Carlos III University of Madrid. His research focuses on numerical methods, partial differential equations, and computational mathematics, with applications in industrial engineering and materials science. He leads projects such as 'Financiación adicional 5º año (2022)' and collaborates on initiatives like 'Clustering Automático de Comportamientos de Invertebrados en Libertad mediante Imagen 3D.' His work emphasizes domain decomposition algorithms, radial basis functions, and stochastic control problems. He has advised at least one PhD thesis and holds grants from regional and national funding bodies. Key research interests include meshless methods, probabilistic domain decomposition, and uncertainty quantification in energy systems. Recent publications highlight advancements in hybrid algorithms for large-scale PDEs and volatility modeling. Bernal Martínez actively participates in academic networks and has presented at international conferences on computational methods and industrial mathematics.

• Numerical Methods
• Partial Differential Equations
• Computational Mathematics
• +3 more

Dr. Andrés Modesto Alonso is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid, affiliated with the Energy Analytics research group and Flores de Lemus Institute. His work spans computer science, economics, and statistics through advanced time series analysis and energy forecasting methodologies. Primary affiliation: Department of Statistics, UC3M Research groups: Energy Analytics, Flores de Lemus Institute His research focuses on time series analysis , energy forecasting , and statistical clustering with applications in electricity markets, smart grids, and environmental data. Recent publications emphasize deep learning models for energy prediction, dynamic factor models, and market-based distance metrics. Scientific output trends show 15 recent articles (2018-2024) covering topics like: Electricity market price forecasting Smart grid optimization through clustering Adaptive control charts for industrial processes Precision matrix estimation in high-dimensional statistics Extreme value analysis for environmental monitoring Dr. Alonso has supervised multiple theses on time series modeling and classification techniques, while collaborating on grants related to stochastic optimization and responsible AI applications in economic forecasting.

• Time Series Analysis
• Energy Analytics
• Statistical Clustering
• +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.

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

Dr. Laleh Tafakori is a Senior Lecturer in the Department of Statistics and Analytics at RMIT University's School of Science. Her research focuses on the intersection of statistical modeling, machine learning, and their applications in healthcare, finance, and environmental science. She is affiliated with the university's City Campus and can be contacted at laleh.tafakori@rmit.edu.au . Her teaching interests include Applied Analytics , Statistical Inference , Time Series Analysis , Mathematical Statistics , Stochastic Processes , and Probability Theory . She actively supervises research projects in areas such as: Healthcare modeling (e.g., diabetes onset prediction, maternal risk assessment) Environmental data analysis (e.g., extreme precipitation estimation via satellite data) Financial risk analysis (e.g., Value-at-Risk forecasting, credit portfolio management) Machine learning applications in complex networks and smart grids Her recent research trends emphasize predictive modeling for public health challenges, leveraging advanced statistical techniques (copula models, functional volatility) and machine learning (CNNs, random forests). She collaborates with institutions worldwide, addressing issues like Saudi Arabia's healthcare indicators and European financial systemic risk. While no formal awards are listed, her work demonstrates impactful contributions to interdisciplinary data science. Dr. Tafakori has advised over 16 students on topics ranging from diabetes epidemiology to edge computing optimization. Her research outputs include 28+ peer-reviewed articles, with a focus on methodological innovation and real-world problem-solving. She maintains active engagement in collaborative projects without explicitly listed grants.

• Applied Analytics
• Statistical Inference
• Time Series Analysis
• +7 more

Carlo Sala is an Associate Professor of Finance at ESADE Business School (Ramon Llull University), serving as Director of the M.Sc. in Finance program. He holds a Ph.D. in Finance from the Swiss Finance Institute (SFI) at the University of Lugano. His research focuses on option pricing, risk management, econometrics, and mathematical finance. He has held visiting researcher roles at leading institutions including University of Oxford, University of Oslo, and Aalto Business School. Sala teaches advanced finance courses such as Derivatives, Asset Pricing, and Fintech, and advises on risk management strategies through his consulting work at Kakubi and CO.MO.I. His research explores topics like cryptocurrency market dynamics, volatility forecasting, and pension fund risk management. Education: Ph.D. in Finance, Swiss Finance Institute (SFI), University of Lugano, Switzerland Research Interests: Option pricing mechanisms and volatility modeling Risk management frameworks for financial institutions Econometric analysis of financial markets Application of machine learning in financial modeling (e.g., SABR model calibration) Cryptocurrency and DeFi market interconnectedness Professional Contributions: Cofounder of Kakubi, specializing in risk management consulting Designed CDS-based risk model for CO.MO.I's Milan branch Former quantitative analyst in European energy markets at Accenture Collaborations: Active in the Group of Research in Economics and Finance (GREF), leading projects funded by the Catalan Government (AGAUR) from 2017-2025.

• Option Pricing
• Risk Management
• Econometrics
• +6 more

Antonio Calomarde Palomino is an Associate Professor at the Department of Electronic Engineering, Polytechnic University of Catalonia (UPC), located at the Higher Technical School of Industrial Engineering of Barcelona. He holds a B.S. in Telecommunications Engineering (UPC, 1986), an M.S. in Electronic Engineering (Universitat Autònoma de Barcelona, 1993), and a Ph.D. in Electronic Engineering (UPC, 2007) with honors. His research focuses on low-power/high-performance digital circuits and soft-error resilient designs, addressing challenges in semiconductor technology and radiation-hardened electronics. Key research areas include FinFET-based memory cells, RRAM modeling, and radiation mitigation strategies for advanced nanoelectronics. He contributes to the High Performance Integrated Circuits and Systems Design (HIPICS) Group, advancing circuit design methodologies and educational initiatives such as integrating autonomous vehicle systems into undergraduate curricula. His work bridges theoretical advancements with practical applications in automotive electronics and embedded systems. Publications span topics from memristor-based associative memory to approximation techniques in object detection systems, reflecting a balance between fundamental research and applied engineering. His recent work emphasizes variability-aware design and energy-efficient architectures, as seen in 2023-2024 studies on RRAM models and automotive case studies. Calomarde actively organizes academic events like the 2022 ETS conference and develops educational materials for digital electronics and audiovisual systems courses. His contributions to both technical research and pedagogical innovation solidify his role as a key figure in electronic engineering education and innovation.

• Low Power Digital Circuits
• Soft Error Resilient Circuits
• Integrated Circuit Design
• +4 more

Alberte Regueira López is an Assistant Professor at the Department of Chemical Engineering, Higher Technical School of Engineering, University of Santiago de Compostela. He holds a Doctorate in Chemical Engineering from the same institution, completing his thesis on Mechanistic understanding of mixed-culture fermentations by metabolic modeling in 2020, supervised by Dr. Juan Manuel Lema Rodicio and Dr. Miguel Mauricio Iglesias. His research focuses on environmental biotechnology, metabolic engineering, and biorefinery design, with an emphasis on microbial fermentation systems for the production of valuable chemicals from waste streams. Key research interests include metabolic modeling of mixed microbial cultures, optimization of biorefinery processes for organic waste valorization, and development of targeted production systems for volatile fatty acids (VFAs). He leads the BIOGROUP research team, investigating the synergistic interactions in microbial communities and applying systems biology approaches to improve bioconversion efficiency. His recent work explores the use of Clostridium species for gas fermentation, hollow fiber membrane biofilm reactors, and the integration of metabolic models with stochastic optimization for biorefinery design. He has pioneered methods to steer microbial production of isobutyric/n-butyric acids and lactic acid through process parameters like pH and substrate composition, while also advancing in-situ product removal strategies for bioreactor systems. Notable contributions include defining protein composition effects on microbial fermentation pathways, developing kinetic models for protein-to-VFA conversion, and applying superstructure optimization techniques for bioprocess design. His work bridges fundamental microbiology with applied engineering solutions for sustainable chemical production.

• Environmental Biotechnology
• Metabolic Engineering
• Biorefinery Design
• +3 more

Stephanía Mosquera is a researcher at Orkestra – Basque Institute of Competitiveness, affiliated with the Energy and Environment Lab since November 2022. She holds a PhD in Engineering (Industrial Engineering) and a Master’s in Applied Economics from Universidad del Valle, Colombia. Her work bridges economics and engineering in the domain of energy markets. PhD in Engineering (Industrial Engineering), Universidad del Valle, Colombia Master’s in Applied Economics, Universidad del Valle, Colombia Bachelor’s in Economics, Universidad del Valle, Colombia Her research centers on energy market dynamics , particularly electricity and natural gas price modeling , market risk measurement and hedging , and the impact of climate and weather variables on energy pricing. She employs advanced econometric and statistical methods to analyze market integration, price volatility, and cross-commodity linkages. The recent publications of Stephanía Mosquera span high-impact journals such as Energy Economics , Applied Energy , and Energy Policy . Collectively, her work reveals a strong focus on weather-related risk in energy markets , price interdependencies between electricity and natural gas , and the economic implications of renewable energy integration . Her research increasingly emphasizes climate change impacts and market resilience during distress periods. She has received notable recognition, including: EEX Group Excellence Award (2019) for her paper on electricity price dynamics in spot and futures markets Stephanía has advised no formal students listed, but she actively collaborates with researchers across institutions such as the University of Barcelona, Open University of Catalonia (UOC), EAFIT University, and Universidad del Valle. She has contributed to research transfer initiatives, bridging academic findings with industry applications. While no specific grants are mentioned, her publication record in top-tier journals indicates strong research productivity and likely grant support. She is embedded in a dynamic research lab focused on sustainable territorial competitiveness. She is part of the Energy and Environment Lab at Orkestra, a transformative research laboratory dedicated to sustainable regional development. Her work contributes to broader efforts in understanding energy system resilience, market design, and climate adaptation strategies in energy economics.

• Energy Markets
• Electricity Price Modeling
• Natural Gas Pricing
• +7 more

Felix Wellschmied is an Associate Professor in the Department of Economics at the University Carlos III de Madrid and serves as Director of the Master in Economics program. He holds a PhD in Economics from Bonn University. His research focuses on Macroeconomics , with a particular emphasis on labor market dynamics, wage inequality, and public policy interventions. Key areas include the analysis of furlough schemes during economic recessions, the interplay between wage and employment risks, and the long-term impacts of structural economic policies. Recent work explores geographic mobility patterns over the life-cycle, monopsony effects in East Germany, and the welfare implications of asset-based income support programs. His publications frequently address policy-relevant questions using advanced econometric techniques. Publications span topics such as labor reallocation during crises, entrepreneurial wealth dynamics, and life-cycle earnings risk modeling. He has contributed to understanding the cyclical behavior of job and worker flows in Germany and Spain, with a focus on policy evaluations. Wellschmied's research has been published in top journals like the American Economic Journal: Macroeconomics and is supported by grants from the Ramón y Cajal Researcher Program. His work bridges theoretical macroeconomic frameworks with empirical policy analysis.

• Macroeconomics
• Labor Economics
• Public Policy
• +3 more

George M. Constantinides is the Leo Melamed Professor of Finance at the University of Chicago Booth School of Business. His research focuses on asset pricing theory, the equity premium puzzle, transaction costs, derivatives, and the term structure of interest rates. He has held visiting positions at Harvard University and taught at Carnegie Mellon University prior to joining Chicago Booth in 1979. BA, Oxford University MBA and DBA, Indiana University His work explores the interplay between consumption risk, market sentiment, and asset pricing anomalies. Recent publications analyze the mispricing of index options, the supply and demand dynamics of S&P 500 options, and empirical tests of asset pricing models. His research has been published in top journals including the Journal of Finance , Review of Financial Studies , and Quarterly Journal of Economics . Scientific honors include serving as President of the American Finance Association and the Society for Financial Studies. He is a Research Associate at the National Bureau of Economic Research and a Director/Trustee for Dimensional Fund Advisors. Outside academia, he enjoys travel, scuba diving, and exploring cultural experiences.

• Asset Pricing Theory
• Equity Premium Puzzle
• Transaction Costs
• +5 more

Pedro Galeano San Miguel is an Associate Professor at the Department of Statistics, Universidad Carlos III de Madrid. He is affiliated with the Nonparametric Inference for Complex Data and its Applications (NICDA) research group and contributes to the Flores de Lemus Institute and UC3M-Santander Big Data Institute. His work bridges statistics, computer science, and economics with a focus on financial and high-dimensional data. Research Interests: Functional data analysis and outlier detection Bayesian nonparametric methods and stochastic volatility models Copula models for systemic risk and portfolio selection High-dimensional statistical inference and dynamic correlation Big data applications in economics and finance Publication Trends: His recent work (2024–2016) emphasizes copula models for financial risk, functional regression techniques with missing data, and Bayesian inference for high-dimensional time series. He explores systemic banking risks, volatility prediction, and correlation structure changes across economic and financial domains. Grants & Projects: He leads or contributes to projects on computational statistics for complex dependencies, big data customer network analysis, and multivariate asymmetric GARCH modeling, funded by institutions like the State Research Agency (AEI) and Banco Santander.

• Statistics
• Computer Science
• Economics
• +6 more

Sara Lumbreras Sancho is a Senior Associate Professor at the ICAI School of Engineering, Universidad Pontificia Comillas, where she graduated in 2006 as the Graduation Prize Winner. She serves as Deputy Director of Research Results at the Technological Research Institute and co-leads the Chair of Science, Technology, and Religion with Jaime Tatay. Her research focuses on decision support techniques for complex problems in energy, healthcare, and finance, leveraging optimization methods like Benders decomposition, heuristics, and AI. She also explores the philosophy of technology, transhumanism, and AI's impact on anthropology. With over 70 academic publications and leadership in over 20 projects, she bridges technical innovation with ethical and anthropological considerations. Education: Bachelor’s Degree in Engineering from ICAI School of Engineering (2006) PhD in Decision Support Methods for Large-Scale Flexible Transmission Planning (2014) Research Interests: Optimization techniques (stochastic, Benders, metaheuristics) Predictive healthcare modeling Interpretable machine learning AI ethics and responsible science Energy systems planning (wind farms, transmission networks) Philosophical implications of technology Grants & Projects: Lead researcher in EU-funded initiatives like DIAMOND and OPEN_ENTRANCE Collaborations with industries like Iberdrola, Enel, and Pharoes Advisory Transnational projects addressing energy transition challenges Awards: Global Shaper (World Economic Forum) Marshall Memorial Fellow Outreach: Chair of Cátedra de Ciencia, Tecnología y Religión Keynote speaker at conferences like EURO 2025 and Science and Religion Forum Author of books and book chapters on AI ethics, transhumanism, and science-religion dialogue

• Optimization applied to complex problems
• Predictive modeling in healthcare
• Interpretable Machine Learning
• +9 more

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.

• Statistical inference for stochastic differential equations
• High-frequency data analysis
• Malliavin calculus
• +7 more