Amir Sufi is the Bruce Lindsay Distinguished Service Professor of Economics and Public Policy at the University of Chicago Booth School of Business, where he has been a faculty member since 2005. He serves as a Research Associate at the National Bureau of Economic Research and co-director of its Corporate Finance Program. Bachelor’s Degree, Walsh School of Foreign Service, Georgetown University (1999) PhD in Economics, Massachusetts Institute of Technology (2005) His research focuses on finance , macroeconomics , and corporate finance . Key areas include household debt dynamics , credit market structure , income inequality , and interest rate impacts on productivity growth . Recent work examines customer capital investment and low-interest rate effects on market concentration . Selected scientific awards include the 2017 Fischer Black Prize, Econometric Society Fellow (2022), and American Academy of Arts and Sciences Fellow (2024). His peer-reviewed publications and working papers span topics from syndicated loans to global household debt cycles , with notable contributions to understanding credit-driven business cycles and government-led consumer credit programs . He teaches courses in leveraged finance , private credit , and corporate restructuring .
Pablo Parra Espada is an Associate Professor at the Department of Automática, University of Alcalá (Spain), affiliated with the Space Research Group (SRG-UAH). He holds a PhD from the University of Alcalá (2012) titled Integración de tecnologías de desarrollo y análisis basadas en componentes bajo un enfoque multi-plataforma , supervised by Dr. Sebastián Sánchez Prieto and Dr. Óscar Rodríguez Polo. His research focuses on space systems engineering , particularly in RISC-V processor design , embedded systems , and model-driven engineering . Key areas include hardware-software co-design for satellite systems, real-time computing, and fault-tolerant architectures. He has contributed to the Solar Orbiter mission through work on the Energetic Particle Detector (EPD) and its on-board software validation. His recent work emphasizes virtualization techniques for LEON processors, FPGA-based digital beamforming , and spaceborne phased array systems . He also explores model-driven approaches for automated configuration of ground support equipment. His interdisciplinary contributions bridge computer architecture with aerospace applications. Prof. Parra Espada has published extensively on topics such as hardware performance monitoring, memory management units for satellites, and system-level verification of space software. His work combines rigorous engineering methodologies with cutting-edge technologies to address challenges in space instrumentation and embedded systems.
Eduardo Antonio Ahedo Galilea is a Full Professor in the Department of Aerospace Engineering at Universidad Carlos III de Madrid (UC3M), leading the Plasmas and Space Propulsion Team (EP2). His research focuses on plasma propulsion systems, particularly Hall thrusters, magnetic nozzles, and helicon plasma thrusters, with expertise in plasma dynamics, wave-plasma interaction, and spacecraft-plume effects. Current projects: MLPLUS-CM (2025-2028), PROPULSION ELECTROMAGNETICA POR EFECTO HALL (2023-2026) International collaborations: European Commission, Airbus Defense and Space, SENER His recent publications analyze non-stationary plasma expansions, electron cooling mechanisms, and magnetic nozzle physics across multiple thruster designs. He supervises theses on turbulent transport, wave-plasma interaction, and fluid-kinetic thruster modeling. Grants include funding from the European Commission Research Executive Agency, Agencia Estatal de Investigación (AEI), and regional programs like RIS3-CAM.
Francisco Javier Falcone Lanas is a Professor in the Department of Electrical, Electronic and Communication Engineering at the Public University of Navarre. He is affiliated with the Institute of Smart Cities and leads research in the Communication, Signals and Microwaves research group. He also participates in the Doctoral Program in Communications Technologies, Bioengineering, and Renewable Energy. His research focuses on applied and computational electromagnetics, with specializations in: Analysis and design of complex electromagnetic media and metamaterials Design of communication devices (filters, diplexers, couplers, antennas) Implementation of devices on flexible/paper substrates Wireless power transfer systems Computational electromagnetic code development (FDTD, 3D Ray Launching, Radar RCS) Implementation of devices for PLMN, WSN, LPWAN and Radar systems Radioelectric analysis at physical layer and system level His recent publications demonstrate a strong focus on millimeter-wave technology, MIMO antenna systems, 5G/6G communications, and wireless sensor networks. His work often addresses optimization challenges related to energy consumption, interference handling, and capacity/coverage in communication systems. He has made significant contributions to the fields of metamaterials and their application in antenna design and performance enhancement. With an H-index of 58 in Scopus, Professor Falcone Lanas has established himself as a leading researcher in his field. His research has practical applications in various domains including: 5G/6G mobile communication systems Wireless body area networks Vehicle-to-everything (V2X) communications Wireless sensor networks for industrial applications Digital twin modeling for UAV communications Earthquake disaster management systems
Dr. Diego Alejandro Tejada Arango serves as a Visiting Professor in the Energy Systems Modeling area at the Institute for Research in Technology (IIT) of Comillas Pontifical University's School of Engineering. His academic journey includes a BSc in Electrical Engineering from Universidad Nacional (Colombia, 2006), an MSc from Universidad de Antioquia (Colombia, 2013), and ongoing MSc studies in Research in Engineering Systems Modeling at Universidad Pontificia Comillas. BSc Electrical Engineering, Universidad Nacional, Medellin (2006) MSc Electrical Engineering, Universidad de Antioquia (2013) MSc Research in Engineering Systems Modeling, Universidad Pontificia Comillas (in progress) His research focuses on developing advanced modeling tools for energy sector analysis and planning, with particular expertise in optimization techniques for generation capacity expansion. Dr. Tejada's work bridges theoretical advancements with practical applications in power system operations and planning. His research interests span optimization methodologies, unit commitment formulations, generation and transmission expansion planning, and energy storage integration within modern power systems. Analysis of his publication record reveals a strong focus on improving computational efficiency in energy system modeling while maintaining accuracy, with recent work addressing temporal resolution flexibility, hydrogen infrastructure integration, and interpretable machine learning applications for power system operations. His research demonstrates consistent evolution from traditional power system planning toward addressing contemporary challenges of renewable integration and decarbonization. 4th EASE Student Award for PhD thesis on energy storage co-optimization Dr. Tejada has contributed extensively to collaborative research projects with industry partners including Endesa and Enel Iberoamerica, focusing on generation expansion, renewable integration, and strategic network planning. His technical reports for the Spanish Ministry of Economy demonstrate engagement with policy-relevant research. As a reviewer for leading power systems journals including IEEE Transactions on Power Systems and International Journal of Electrical Power & Energy Systems, he contributes to scholarly discourse in the field.
Álvaro Paricio García is an Assistant Professor at the Department of Automation within the School of Telematics Engineering at Universidad de Alcalá. He is affiliated with the NetIS Research Group (Networks and Intelligent Systems). His research focuses on smart city technologies, traffic engineering, optimization algorithms, and environmental engineering, with a particular emphasis on urban mobility, crowd evacuation systems, and emission reduction strategies. Education: He holds a PhD from Universidad de Alcalá, awarded in 2021 for his thesis Estrategias multi-mapa para el enrutamiento dinámico de tráfico urbano , supervised by Dr. Miguel Ángel López Carmona. Research Interests: His work integrates control systems, machine learning, and simulation-based optimization to address challenges in urban traffic management, crowd dynamics, and sustainable transportation. Key themes include: Design of low-emission zones for urban areas Development of adaptive evacuation systems using MPC (Model Predictive Control) Algorithmic innovations in traffic routing and multi-map strategies Article Trends: Recent publications (2021-2025) highlight his focus on: Wind farm layout optimization using metaheuristics Biometric identification via autoencoder-driven systems Dynamic low-emission zones and their policy implications Adaptive crowd evacuation systems like CellEVAC No scientific awards or grants were explicitly mentioned in the provided texts. He has not supervised any listed students. Labs/Teams: Active member of the NetIS Research Group, which develops networks and intelligent systems for urban and industrial applications.
Carlos José Villagrá Arnedo is a full-time Professor in the Department of Computer Science and Artificial Intelligence at the University of Alicante, where he has been employed since 1999. He holds a PhD in Computer Engineering (2016) and a Bachelor's in Computer Science from the Polytechnic University of Valencia (1994). He coordinates the Digital Creation and Entertainment program and previously served as Head of Studies for Multimedia Engineering. His research integrates Artificial Intelligence , Educational Technology , and Game-Based Learning , with specific focus areas including learning analytics, adaptive educational systems, programming pedagogy, and accessibility in gaming. Recent work explores mental model analysis using AI and low-level programming approaches to enhance learning outcomes. His publications demonstrate a consistent emphasis on predictive learning models, educational gamification, and inclusive technology design. Key trends include AI-driven analytics (2020-2024), programming error analysis (2018-2023), and accessible game development (2017-2019). Research Projects: Integrating Adaptive Flipped Learning in Physical Education Students (2024-2025) Smart Learning Research Group (2024) Semantic Web for Cultural Heritage (2019-2021) European Collaborative Learning for Robot Skill Acquisition (2016-2018) He has directed or co-directed 75+ undergraduate/master's theses in the last five years and leads the Smart Learning Research Group focused on intelligent educational technologies.
David W. Abraham is a senior researcher at IBM Research - Yorktown Heights specializing in quantum computing and advanced materials. His work spans multiple decades with significant contributions to near-field microscopy, thermal imaging, MRAM technology, and most recently quantum processor development. His primary research interests focus on quantum computing hardware , particularly superconducting bump bonds, through-silicon vias (TSVs), and multi-level wiring systems critical for building scalable quantum processors like IBM's 127-qubit Eagle device. Earlier work included pioneering research in magnetic force microscopy (achieving 25 nm resolution) and thermal imaging applications for disk drives. Analysis of his publication history reveals consistent innovation in quantum hardware engineering, with recent work (2021-2024) concentrated on solving practical integration challenges for quantum processors. His research bridges fundamental physics with practical engineering solutions for next-generation computing systems. Dr. Abraham collaborates extensively with IBM Quantum leadership including Jay Gambetta (VP of Quantum), Matthias Steffen (IBM Fellow), and Oliver Dial (CTO, IBM Quantum), reflecting his integral role in IBM's quantum computing initiative. His laboratory work focuses on quantum processor fabrication and characterization, with particular emphasis on developing reliable interconnect technologies and mitigating decoherence sources in superconducting qubit systems. Current projects appear directed toward scaling quantum processors beyond current device limitations through novel packaging and integration approaches.
Fernando Carcavilla Puey serves as Professor at Universidad San Jorge's Faculty of Communication and Social Sciences, teaching Advertising Creativity, Branding, and Final Degree Projects in Advertising/PR and Multimedia Design in Journalism. He concurrently holds dual administrative roles as Coordinator of University Information and International Coordinator for the Advertising and PR degree. His Doctorate in Communication from Universidad San Jorge anchors research centered on branding and territorial branding. Key focus areas include: "Marca España" media representation across political, cultural, and business contexts Digital communication strategies on YouTube/Instagram Crisis management frameworks (e.g., Rubiales case) Political influencer engagement mechanics Publication trends reveal consistent nation branding scholarship since 2014, evolving from traditional press analysis toward contemporary digital platforms. His work demonstrates methodological progression from macro-level media coverage studies to granular social media analytics, particularly examining narrative construction in politically charged environments and luxury brand identity maintenance. Scientific recognition includes: Quinquenium of Teaching Excellence (Universidad San Jorge) While specific student advisement details remain undocumented, his active conference participation and administrative leadership indicate significant mentorship capacity. Current research trajectories suggest ongoing exploration of AI-driven brand perception analytics and cross-cultural digital diplomacy frameworks within Spanish institutional contexts.
Sebastián Sánchez Prieto is a Professor at the Department of Automatic Control, Universidad de Alcalá, Spain, affiliated with the Space Research Group (SRG-UAH). He holds a Doctorate from the same institution (1998) with a thesis on cosmic ion telescope control systems. His research focuses on space instrumentation, radiation detection systems, on-board data management, and embedded systems for space applications. He has contributed extensively to the Solar Orbiter mission, working on the Energetic Particle Detector (EPD) instrumentation and software validation. His technical expertise spans FPGA-based signal processing, RISC-V processor architectures, and model-driven engineering for space systems. Notable work includes advancing virtualization techniques for mixed-criticality space systems, digital beamforming architectures, and neural network applications in particle trajectory analysis. Publications emphasize cutting-edge topics like fault-tolerant computing in space environments, adaptive signal processing for space communications, and low-power positioning systems. He actively develops educational frameworks integrating on-board data management concepts across interdisciplinary university curricula. His contributions include over 50 peer-reviewed articles addressing hardware-software co-design, space software verification, and radiation effects mitigation. Current research explores AI-driven approaches for space instrumentation data analysis and next-generation on-board computing solutions.
Carlos Cglez-Morcillo is an Associate Professor at the Higher School of Computer Science, University of Castilla-La Mancha (Spain), with an active career in academia since 2002. He holds a European PhD in Computer Science (2007) and a degree in Computer Science (2002) with an Extraordinary End-of-Degree Award. Currently, he serves as Academic Director of the Vice-Rectorate of Digital University and Director of the G9 Shared Digital Campus, while also being a founding member of the AIR Research Group and Creative Director of the university spin-off Furious Koalas. PhD in Computer Science (2007) BSc in Computer Science (2002) Technical Engineer in Computer Systems (2000) Research interests include Artificial Intelligence, Computer Graphics, and their applications in rehabilitation technology and educational innovation. His work spans mixed reality systems for stroke recovery, gamification in learning environments, and 3D rendering optimization. He has led 9 research projects with over €550,000 in funding and contributed to over 30 technology transfer initiatives. Recent publications focus on VR/AR applications for neurological rehabilitation, e-commerce metaverse environments, and AI-driven educational tools. His 15 most recent articles reflect interdisciplinary work bridging computer science with healthcare and business innovation. Scientific recognition includes being the first Spaniard to obtain the Blender Foundation Certified Trainer (2009) and leading award-winning projects like ICEIS 2025 Best Student Paper. He actively participates in conferences and media outreach, demonstrating commitment to public engagement through platforms like Naukas and CMM Radio.
Dr. Joan Duran Grimalt is an Associate Professor of Applied Mathematics in the Department of Mathematics and Computer Science at the University of the Balearic Islands (UIB). He serves as a member of the Mathematical Image Processing (TAMI) research group and the Institute of Applied Computing and Community Code (IAC3). From July 2021 to June 2024, he held the position of deputy director of the Higher Polytechnic School and head of studies for the Degree in Mathematics program. His academic career at UIB began in 2015 following the completion of his PhD. Dr. Duran Grimalt earned his academic credentials at prestigious institutions: BSc in Mathematics (2010) from the University of the Balearic Islands (UIB) MSc in Advanced Mathematics and Mathematical Engineering (2011) from the Polytechnic University of Catalonia PhD in Mathematics (2016) from UIB with thesis on variational models for ill-posed inverse problems in digital imaging His research spans the intersection of mathematical theory and practical applications in imaging science. Dr. Duran Grimalt specializes in nonlinear analysis, calculus of variations, partial differential equations, and deep unfolding architectures. His work focuses on developing mathematical frameworks that bridge traditional variational methods with modern deep learning approaches, particularly for image processing and computer vision applications. This hybrid methodology allows for both the interpretability of model-based approaches and the performance benefits of data-driven techniques. Analysis of his recent publications reveals a clear research trajectory toward integrating classical mathematical models with deep learning architectures, particularly through the technique of deep unfolding. His work consistently addresses challenging problems in satellite image processing, pansharpening, hypersharpening, and low-light image enhancement. The publications demonstrate a progression from purely variational approaches to increasingly sophisticated hybrid models that incorporate attention mechanisms, nonlocal operations, and specialized network architectures designed specifically for imaging problems. His notable scientific recognition includes: Fellowship from the Govern de les Illes Balears for PhD research (2011-2015) Dr. Duran Grimalt has secured research funding for multiple projects, leading two major initiatives. He has established significant international collaborations with the National Centre for Space Studies (CNES) in France, where he contributed to the image restoration chain for Earth observation satellites, and with the Oceanographic Centre of the Balearic Islands, focusing on deep unfolding architectures for remote sensing data fusion and marine object detection. His academic mentorship includes supervising PhD candidates M. Francesc Alcover (working on nonlocal theory for variational problems) and Daniel Torres (researching the combination of variational models and deep learning for image processing). He has also been a visiting researcher at leading institutions including the Technical University of Munich, ENS Paris-Saclay, and New York University. His research is conducted through the Mathematics, Imaging and Learning (MIA) Consolidated R+D+I Group, where he is an active member, and leverages resources from the Institute of Applied Computing and Community Code (IAC3). These research structures provide the computational infrastructure and collaborative environment necessary for his work on advanced image processing algorithms and their applications in satellite imaging and computer vision.
Mariano Lopez Garcia is a researcher at the Polytechnic University of Catalonia (UPC), affiliated with the Department of Electronic Engineering at the Polytechnic School of Engineering of Vilanova i la Geltrú (EPSEVG). He is part of the SARTI research group, focusing on technological development in remote sensing and information processing, as well as the SARTI-MAR subgroup addressing marine environment applications. His work emphasizes embedded systems, cryptography, and biometric security, with a strong emphasis on FPGA-based hardware-software co-design for cybersecurity and post-quantum cryptography solutions. His research spans over 150 activities, including peer-reviewed publications, competitive R&D projects, and conference presentations. Key areas include post-quantum cryptography (e.g., McEliece cryptosystem implementations), biometric authentication systems, FPGA acceleration for real-time processing, and control systems for power electronics. Notable projects include developing secure cryptographic key protection against side-channel attacks and creating low-cost embedded systems for signature verification. He has collaborated on initiatives such as the Projecte R+D+I competititu for vector processors in biometric identification and the TEC2015-68784-R project on scalable identification systems. His contributions also extend to power electronics, including sliding mode control for resonant converters and electronic transformer designs. Recent publications (2025) highlight advancements in post-quantum cryptography using FPGA hardware accelerators and constant-weight code-based encryption. His work bridges theoretical research with practical applications in embedded systems and cybersecurity, often leveraging reconfigurable hardware for efficient and secure solutions.
Rafael Ballester-Ripoll is an Assistant Professor at IE University's School of Science & Technology, specializing in Data Science and Machine Learning. Before joining IE in 2019, he held postdoctoral positions at ETH Zurich and the University of Zurich (UZH). He earned his PhD in Computer Science from UZH (2017), and BSc/MSc degrees in Mathematics and Computer Science from the Technical University of Catalonia-UPC (2012). His research focuses on low-rank tensor decompositions, explainable AI, sensitivity analysis, and scientific visualization. Key contributions include advancements in large-scale visualization, data compression, and uncertainty quantification. Recent work (2025) explores CSR authenticity in AI-era social media, hexagonal A-Star algorithms for weather routing, and entropy coding for tensor networks. Publications span journals like Journal of Machine Learning Research, IEEE Transactions on Visualization and Computer Graphics, and SIAM/ASA Journal on Uncertainty Quantification. He actively participates in conferences such as IEEE VIS and EuroVis, and co-developed tools like tntorch and VIAN. His research trends emphasize tensor-based methods applied to interdisciplinary domains, including quantum computing, Bayesian networks, and probabilistic graphical models. Collaborative projects highlight applications in ocean engineering, film analysis, and recommender systems. Ballester-Ripoll has contributed to IE's Research Datalab initiatives, though no formal awards or grants are explicitly noted in the provided texts. His work bridges theoretical foundations with practical tools for data-driven decision making.
Óscar Rodríguez Polo is an Associate Professor at the Department of Automática, Universidad de Alcalá (UAH), and a member of the Space Research Group (SRG-UAH). His research focuses on embedded systems, real-time control, FPGA applications, and space systems engineering. He holds a Ph.D. from Universidad Complutense de Madrid (2003), with a thesis on real-time control system code generation using the ROOM methodology. Key contributions include work on the Solar Orbiter mission’s Energetic Particle Detector (EPD), LEON processor virtualization, RISC-V space-oriented architectures, and model-driven engineering for satellite software. His research emphasizes hardware-software co-design, fault tolerance in on-board systems, and space instrumentation validation. He has pioneered educational initiatives integrating hands-on training in satellite formation flying and on-board data management through low-cost hardware-in-the-loop systems. His publications span over two decades, with notable contributions to processor architecture optimization, safety-critical embedded systems, and space mission software reliability.
