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Giovanni CompianiView profile

Associate Professor

• Industrial Organization
• Quantitative Marketing
• Econometrics
• +6 more

Giovanni Compiani is an Associate Professor at the University of Chicago Booth School of Business, specializing in Marketing. His research bridges industrial organization and quantitative marketing, focusing on advanced econometric methods. PhD, MPhil, MA in Economics from Yale University BSc, MSc in Economics from Bocconi University Previous Assistant Professor at Haas School of Business His work explores unstructured data integration in demand estimation, consumer search behavior on online platforms, risk preferences in cryptocurrency markets, and time perception in behavioral economics. He has published in top journals including Journal of Political Economy , Marketing Science , and Review of Economic Studies . Recent publications emphasize machine learning applications in econometrics, equilibrium modeling of lotteries, and crypto mining's economic impact. His research portfolio spans demand analysis, structural modeling, and behavioral insights. Editor's Choice Award, The Review of Asset Pricing Studies (2024) Developed nonparametric demand estimation frameworks Advances dynamic model identification with instrumental variables Compiani teaches Data Science for Marketing Decision Making at Booth and maintains active collaborations with researchers across econometrics, computer science, and behavioral disciplines.

Julian FierrezView profile

Professor

• Biometrics
• Signal and Image Processing
• Artificial Intelligence
• +6 more

Julian Fierrez is a Full Professor at the School of Engineering, Universidad Autonoma de Madrid. With an h-index of 74 and over 20,000 citations, his work spans biometrics, signal/image processing, artificial intelligence, and human-computer interaction. Key research areas include: Biometric anti-spoofing and DeepFakes detection Mobile and behavioral biometrics Bias/fairness in AI systems Biometric applications in e-health and education Security in multimodal biometric systems His recent publications show strong focus on deep learning applications for biometric security, with specific subfields including fake detection, keystroke authentication, facial analysis for Parkinson detection, and privacy-preserving AI. He serves as Associate Editor for multiple IEEE and Elsevier journals. Scientific distinctions include: IAPR Young Biometrics Investigator Award (2017) Miguel Catalan Award to Best Researcher under 40 (2017) EURASIP Best PhD Award (2012) EBF European Biometric Industry Award (2006) Prof. Fierrez leads the BiDA Lab and supervises students like Ruben Tolosana and Aythami Morales. Current projects include BBforTAI (Biometrics and Behavior for Unbiased & Trustworthy AI) and PRIMA (Privacy Matters). He also contributes to standardization efforts in biometric evaluation.

Carmela TroncosoView profile

Associate Professor

• Privacy Engineering
• Security Systems
• Cryptography
• +3 more

Carmela Troncoso is an Associate Professor at EPFL heading the SPRING Lab focused on Security and Privacy Engineering. Her work addresses technology's societal impact through machine learning security, privacy-enhancing technologies, and privacy engineering frameworks. She leads significant research in decentralized privacy systems, including contributions to the DP-3T contact tracing protocol adopted by Google/Apple during COVID-19. Her lab develops tools for privacy evaluation including Synthetic Data Evaluation frameworks and anonymous authentication libraries. Awards include the ERCIM Best Ph.D. Thesis Award, CNIL Privacy Protection Award, and IEEE Distinguished Paper Award. She was recognized as a Fortune 40 Under 40 Technology Leader in 2020.

Pedro GaleanoView profile

Associate Professor

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

• Biometrics
• Biometric Systems
• Computer Vision
• +5 more

Raul Sanchez Reillo is a Full Professor at Universidad Carlos III de Madrid (UC3M), affiliated with the Grupo Universitario de Tecnologías de Identificación (GUTI). His research focuses on biometric systems, mobile authentication, and security technologies. Key areas include presentation attack detection, vein recognition, and ECG biometrics. He leads projects involving smartphone-based biometric solutions, 3D printed markers, and standards development for biometric interoperability. Research interests span multiple modalities: fingerprint authentication, dynamic signature verification, gait recognition, and vascular biometrics. He emphasizes usability and accessibility in mobile environments, exploring ergonomics and user interaction challenges. His work integrates machine learning (transformers, RNNs) with hardware solutions like FPGA-based systems. Publications highlight innovations in spoofing detection, medical applications (ECG/vein analysis), and low-cost hardware implementations. He contributes to European standards (BioAPI, Hand Data Interchange Format) and evaluates security practices for R&D compliance with EU data protection regulations. Current initiatives include enhancing biometric systems for critical infrastructure security and improving accessibility for elderly users. Active in interdisciplinary collaborations, he leads the Mobile Pass project evaluating user interaction in biometric systems. His lab (GUTI) develops open testing methodologies for biometric performance under Common Criteria and environmental stressors. Recent work addresses vulnerabilities in mobile fingerprint sensors and the ethical implications of biometric-as-a-service models.

• Quantum Safe Cryptography
• Threat Management
• Artificial Intelligence Security
• +5 more

Dr. Marc Ph. Stoecklin is a Principal Research Staff Member and head of the Security Research Department at IBM Research Europe in Zurich, Switzerland. He co-leads IBM's global research strategy on Quantum Safe Cryptography and Migration, focusing on cryptographic artifact identification and migration implementation. Previously, he directed Threat Management research, applying AI and automation to threat detection, investigation, and response. Education: PhD in Computer, Communication and Information Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Switzerland His research spans quantum safe cryptography, threat management, AI security, threat detection, identity management, cyber deception, big data analytics, and security visualization. He pioneered AI-powered security operations, contributing to Watson for Cyber Security and QRadar Advisor with Watson, and analyzes AI/quantum computing misuse in cyber attacks. His 2016-2020 publications emphasize advanced threat detection, malware analysis, and AI security, featuring evasive malware deactivation, threat intelligence computing, and neural network watermarking. These works bridge research and product development, directly influencing IBM security offerings. Dr. Stoecklin led IBM's Cognitive Cyber Security Intelligence group (2014-2019) and the Security Research Department since 2019. As tech lead for IBM's COVID-19 task force (2020-2022), he developed IBM Digital Health Pass, deployed in New York's Excelsior Pass with over 1 million passes issued in two months. He heads the Security Research Department at IBM Research Europe in Zurich and has been integral to the Global Security Analysis Lab (GSAL) and Cognitive Cyber Security Intelligence (CCSI) group at IBM T.J. Watson Research Center.

• Applied Data Analytics
• Machine Learning
• Computational Modeling
• +4 more

Maria Cristina Marinescu is a Professor in the Department of Mathematics and Data Analytics at IQS School of Engineering , with a focus on Applied Data Analytics and Modeling . She has an active research profile in interdisciplinary domains connecting computational methods with public health, social sciences, and medical applications.

• Financial Econometrics
• Statistics
• Empirical Asset Pricing
• +5 more

Dacheng Xiu is the Joseph Sondheimer Professor of Econometrics and Statistics at the Booth School of Business , University of Chicago, and an Affiliated Faculty in the Department of Statistics. He serves as a Research Associate at the National Bureau of Economic Research and holds editorial roles at journals like Journal of Business & Economic Statistics and Journal of Financial Econometrics . PhD and MA in Applied Mathematics from Princeton University BS in Mathematics from University of Science and Technology of China His research focuses on statistical methodologies for financial data , including risk measurement , portfolio management , and empirical asset pricing using high-frequency data and machine learning . Recent work analyzes text data and large language models for economic forecasting. Editorial leadership includes Co-Editor and Associate Editor roles at top journals like Journal of Finance and Annals of Statistics . His lab ( Risk Lab ) specializes in systemic risk assessment through transaction-level data analysis. 2024 Dimensional Fund Advisors Prize 2023 GSU-RFS FinTech Conference Best Paper Award 2022 Society for Financial Econometrics Fellow 2018 Swiss Finance Institute Outstanding Paper Award

Alfonso José López RiveroView profile

Professor

• Statistics
• Operations Research
• Strategic Management
• +12 more

Dr. Alfonso José López Rivero is a Professor at the School of Computer Science , Pontifical University of Salamanca , specializing in Statistics and Operations Research . He earned his PhD from the Universidad Pontificia de Salamanca in 2004 with a thesis on Software Quality in Information Selection and Classification , supervised by Dr. Luis Joyanes Aguilar. Education: PhD in Computer Science (2004), Universidad Pontificia de Salamanca. His research spans Digital Transformation , Machine Learning , and Sustainable Mobility , with a focus on applications in healthcare, battery recycling, and strategic management. Recent work includes IoT systems for voice-based disease detection and AI-driven sustainability in SMEs. Key trends in his publications include Electric Vehicle (EV) Optimization , IoT in Healthcare , and Ethical AI . He leads the Gestión tecnológica y ética del conocimiento research group, emphasizing technological ethics and data-driven decision-making.

• Data Mining
• Bioinformatics
• Machine Learning
• +3 more

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

• Industrial Engineering
• Maintenance Optimization
• Industry 4.0/Digital Transformation
• +6 more

Aitor Goti Elordi is a Professor in the Department of Mechanics, Design and Industrial Management at the Faculty of Engineering, University of Deusto. His extensive research portfolio spans industrial engineering, maintenance optimization, Industry 4.0 implementation, and sustainable manufacturing practices. He leads multiple EU-funded and industry-collaborative research projects focused on digital transformation in manufacturing sectors. His research interests center around the intersection of industrial engineering and digital transformation, with particular focus on maintenance optimization using evolutionary algorithms, predictive maintenance systems, and the development of future skills requirements for evolving industrial sectors. His work bridges theoretical research with practical industrial applications, particularly in the Basque manufacturing ecosystem. Analysis of his recent publications reveals a strong trend toward interdisciplinary research addressing Industry 4.0 challenges across multiple sectors. His work consistently focuses on practical applications of data science and AI in industrial contexts, with growing emphasis on sustainability and circular economy principles. A distinctive pattern in his research is the development of competency frameworks to identify future skills requirements across various industrial sectors including steelmaking, renewable energy, and supply chain logistics. Professor Goti Elordi leads multiple significant research projects including SUSTASKILLS (2023-2025) focused on industrial symbiosis skills, REshaping Supply CHAins for Positive social impact (2022-2025), and Real-time acoustic sensorS and artificial Intelligence appLications (2023-2026). His work has secured funding from the European Commission, Basque Government, and major industrial partners including SIDENOR, NEMAK, and ETXE-TAR. He actively supervises student projects, particularly in the area of machinery redesign and additive manufacturing applications. His teaching and research integrate practical industrial experience with academic rigor, emphasizing the development of both technical and transversal skills needed for future industrial challenges.

Mohamed H. DoweidarView profile

Professor

• Computational Biomechanics
• Cell Simulation
• Hyperelastic Materials
• +7 more

Mohamed H. Doweidar is a Full Professor in the Mechanical Engineering Department at the University of Zaragoza, Spain. He is affiliated with the School of Engineering and Architecture (EINA), the Bioengineering Division of the Aragón Institute of Engineering Research (I3A), and the CIBER-BBN (Bioengineering, Biomaterials, and Nanomedicine). His academic career spans over three decades, with teaching roles since 1994 and significant contributions to computational biomechanics and biomedical engineering. Education: Ph.D. in Computational Fluid Mechanics (2004), University of Zaragoza M.Sc. in Engineering Mathematics (2001), Ain Shams University Bachelor's in Statistics & Computer Science (1997), Mansoura University Industrial Engineering Degree (1993), Benha Higher Institute of Technology Research Interests: Focus on Computational Biomechanics , Cell Simulation , Hyperelastic Materials , Finite Element Method , and Error Estimation . His work integrates computational models to study cellular behavior, tissue regeneration, and biomedical applications. Key Contributions: Authored/co-authored books on biomechanics and computational modeling, including "Digital Human Modeling and Medicine: The Digital Twin" (2022). Supervised numerous doctoral students, some of whom received university awards. Active in editorial boards, project evaluations (ANEP), and international conferences. Labs & Groups: Member of the Applied Mechanics and Bioengineering Group (AMB), CIBER-BBN, and I3A. Research spans cell migration , biomaterials , and computational oncology .

• Earth Physics
• Climate Science
• Remote Sensing
• +3 more

Francisco J. Tapiador is a Professor at the Universidad de Castilla-La Mancha , affiliated with the Department of Environmental Sciences. His research spans Earth Physics, Climate Science, and Remote Sensing, with a focus on precipitation modeling, climate classification systems, and data fusion techniques. He leads the Earth Physics research group at UCLM and collaborates extensively with international institutions. Key Research Areas : Earth Physics Climate Modeling Satellite Meteorology Data Fusion Algorithms Environmental Uncertainty Quantification Publication Trends : Dr. Tapiador's recent work emphasizes high-resolution precipitation merging via radar corrections, machine learning for storm detection, and uncertainty quantification in climate models. His articles integrate Remote Sensing , Machine Learning , and Climate Informatics to address extreme weather and hydrometeorological hazards. Peer Review Leadership : Atmospheric Research Chaos, Solitons and Fractals Chemosphere Heliyon Journal of Computational Science Meteorology and Atmospheric Physics Water Resources Research Collaborative Networks : Andrés Navarro Eduardo García-Ortega Gyuwon Lee Kyo-Sun Sunny Lim Ziad S. Haddad Technological Impact : His work bridges fluid dynamics , entropy-based algorithms , and neural networks to advance satellite precipitation estimation and climate model validation. Current projects include multi-satellite data correction and climate-human system couplings.

• Electric Power Systems
• Renewable Energy Dispatch
• Isolated Power Systems
• +3 more

Mohammad Rajabdorri is an Assistant Professor at the Institute for Research in Technology (IIT) of Comillas Pontifical University, holding a position since 2020 and currently serving as an Assistant Research Collaborator since 2024. He earned a BSc in Electrical Power Engineering from Shiraz University (2016), an MSc in Electrical Power Systems from Shiraz University of Technology (2018), and a PhD in Electrical Engineering from IIT (2023). His research focuses on isolated power systems, electricity markets, renewable energy dispatch, and power system operation. Key projects include modeling grid-forming converters for system stability, blackstart strategies for renewable plants, and UFLS optimization in island systems. He has collaborated with entities like Iberdrola and the European Commission. Rajabdorri’s work spans 9 journal articles and 8 conference papers, emphasizing data-driven approaches in power system optimization. He advises PhD student M. Sarvarizadeh Kouhpaye on resilience and reliability of island systems. His professional networks include international exchanges at Durham University and invited seminars at IIT.

Sergio Gómez JiménezView profile

Associate Professor

• Complex Networks
• Complex Systems
• Neural Networks
• +4 more

Sergio Gómez Jiménez is an Associate Professor in the Department of Computer Engineering and Mathematics at Rovira i Virgili University (URV), Tarragona, Spain. He joined URV in 1995 and has held his current position since 1997. He obtained degrees in Physics (1990) and Mathematics (1995) and a PhD in Physics (1994) from the Universitat de Barcelona. His research focuses on complex networks, including community structure analysis, epidemic spreading, urban congestion, and applications to biology, medicine, and social systems. He has authored over 100 publications in high-impact journals like Nature Methods and Physical Review Letters. He coordinates the interuniversity Master's in Biomedical Data Science and the PhD Program in Bioinformatics. His editorial roles include Associate Editor of Complexity and Review Editor of Frontiers in Physics. Notable awards include the American Physical Society's Outstanding Referee (2015) and the Web Science Trust's Test of Time Award (2024). His work on modeling the spatiotemporal spread of epidemics, such as the 2020 COVID-19 pandemic, has received significant attention. He also contributed to urban traffic congestion analysis and developed algorithms for hierarchical clustering (e.g., MultiDendrograms). Collaborations span institutions like the University of Oxford and CERN, reflecting his interdisciplinary approach to complex systems.