جست‌وجوی استاد و پژوهشگر

Rosario Delgado de la Torreمشاهده پروفایل

استاد

• Probabilistic Models
• Stochastic Processes
• Machine Learning
• +۶ مورد دیگر

Rosario Delgado de la Torre is a Professor in the Department of Mathematics at Universitat Autònoma de Barcelona (UAB). She has held academic positions since 1987, including roles at Polytechnic University of Catalonia. Her research focuses on probabilistic models, stochastic processes, and machine learning applications in criminology, healthcare, and other fields. She has contributed to projects on stochastic models in biosciences and Bayesian network-based methodologies. Education: PhD in Mathematics (1994), University of Barcelona Key Projects: STOCHASTIC AND DETERMINISTIC MODELS (2022–2025), Modelos estocásticos y aplicaciones (2019–2022) Her research interests include Bayesian networks, probabilistic classifiers, and ensemble methods. Recent work emphasizes overcoming data imbalance in machine learning and medical prognosis modeling. Awards: Contributions align with UN Sustainable Development Goals in education and health, though specific awards are not listed. Supervised 25 students and contributed to 81 research outputs, including software tools like the BOSME algorithm.

• Artificial Intelligence
• Machine Learning
• Time Series Forecasting
• +۷ مورد دیگر

Dr. Antonio Muñoz San Roque is an Ordinary Professor at the Institute of Technological Research (IIT), affiliated with the Comillas Pontifical University, where he also serves as Director of the ICAI School of Engineering since 2018 and previously as Director of IIT since 2008. He teaches in the Department of Electronics, Automation, and Communications and conducts research in Intelligent Systems, focusing on the application of Artificial Intelligence and Machine Learning to industrial monitoring, time series forecasting, and electricity market analysis. His research interests include Machine Learning , Time Series Forecasting , Industrial Process Diagnosis , and Electricity Market Optimization . He applies advanced data analysis techniques to problems in energy systems, predictive maintenance, and smart grids, often combining statistical modeling with domain-specific engineering knowledge. The most recent publications highlight a strong trend in functional data forecasting , neural network interpretability , and energy market modeling . His work spans high-impact journals such as Energy , Applied Soft Computing , and IEEE Transactions on Power Systems , demonstrating consistent contributions to both theoretical methodologies and real-world industrial applications. Director of the award-winning work in the 'Axpo Iberia Award for the best Final Year Project 2023-24' He has advised numerous PhD students, including G. Mestre, J. Portela, and P. Moreno Alonso, and leads multiple industry-funded research projects with companies such as Endesa and Enel. His work integrates academic rigor with practical implementation, particularly in forecasting models for electricity markets and AI-driven industrial diagnostics. He is also involved in academic dissemination through seminars and conference organization, and has contributed to the development of training programs in machine learning for industry partners.

• Functional Data Analysis
• Machine Learning
• Time Series Prediction
• +۵ مورد دیگر

Dr. José Portela González is an Associate Professor at the Institute for Research in Technology (IIT), affiliated with the School of Engineering at Comillas Pontificia University. He also teaches at the Faculty of Economics and Business Administration (ICADE). He has been a research member at IIT since September 2011. His research centers on advanced data science methodologies with a strong focus on energy systems. Key areas include Functional Data Analysis , Machine Learning (particularly neural networks and Explainable AI), and Time Series Prediction . His work applies these techniques to critical problems in electricity market analysis , forecasting, and monitoring the green transition, with additional applications in healthcare and finance. His recent publications, appearing in high-impact journals like Energy , IEEE Transactions on Power Systems , and Applied Soft Computing , demonstrate a consistent trend in developing interpretable AI models (e.g., the NeuralSens tool), forecasting energy demand and prices, and analyzing the economic and environmental impacts of energy markets. His research bridges theoretical statistical methods with practical, data-driven solutions for industry and policy. Business Club Award for the best paper presented at the 2nd Research Hackathon (Oct 2022) Dr. Portela actively advises PhD students, including those working on keyphrase extraction and neural network comprehension. He leads numerous research projects funded by the European Commission, national agencies, and major industry partners like Endesa, Enel, and Airbus. His work involves significant grant-funded research, such as the ATTEST project (Horizon 2020) and projects with the Inter-American Development Bank. He also contributes to his field by serving on PhD thesis committees and directing DBA research projects. His work is conducted within the research ecosystem of the Institute for Research in Technology (IIT), where he collaborates on projects related to smart energy systems, forecasting, and data analytics, often in partnership with industry through applied research contracts.

• Cybersecurity
• Data Science
• Software Process Automation
• +۵ مورد دیگر

Farid Bagheri-Gisour Marandyn is a Predoctoral Researcher at the Institute for Research in Technology (IIT), Comillas Pontifical University, affiliated with the School of Engineering (ICAI) in Madrid, Spain. He began his research career in July 2024, focusing on intelligent systems and cybersecurity. His academic background includes degrees in Telecommunications Engineering and Cybersecurity from the Polytechnic University of Madrid, and a Master’s in Teacher Training from Villanueva University. His research interests span Cybersecurity , Data Science , Cloud Architecture , Blockchain , and Machine Learning . He integrates AI/ML techniques into cybersecurity frameworks, particularly for anomaly detection and risk modeling in critical infrastructure. The available publications show a strong trend in applying machine learning to cybersecurity challenges, especially in power grids and big data environments. His work combines theoretical modeling with practical implementation in distributed systems like Apache Spark. He has professional experience as an ML Researcher at the Polytechnic University of Madrid, Cybersecurity Analyst at Deloitte, Data Engineer at BBVA, and ICT Teacher at various institutions. He also works as a freelance specialist in data and cybersecurity. Professional Cloud Architect CCSP (Certified Cyber Security Professional) He has contributed to major EU-funded research projects such as eFORT (Horizon Europe) and RESPONSE (Horizon 2020), focusing on resilient and secure energy systems. His collaborative research involves modeling cyber threats in industrial and energy contexts. Farid is part of the Intelligent Systems research group at IIT, working on advanced cybersecurity solutions for critical infrastructure and digital transformation initiatives in Europe.

David Enrique Losada Carrilمشاهده پروفایل

استاد

• Information Retrieval (IR)
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

David Enrique Losada Carril is a Full Professor in Computer Science and Artificial Intelligence at the CiTIUS Research Center of the University of Santiago de Compostela (Spain). He holds a PhD in Computer Science (2001, University of A Coruña) and has been active in Information Retrieval (IR) research since joining the university in 2003 as a senior research fellow under the Ramón y Cajal program. BS and PhD (with honors) from University of A Coruña ACM Senior Member awardee (2011) Co-founder of the eRisk CLEF Lab for early risk detection on the internet Active in IR community with roles in ACM SIGIR, ECIR, and CLEF Research Focus spans probabilistic IR models, novelty detection, health search technologies, and mental health analysis via social media monitoring. His recent work explores large language models for depression symptom assessment and misinformation detection in health contexts. Scientific Leadership includes: Principal Investigator for projects like Big-eRisk and LudoTrack Co-author of over 20 top-tier publications (SIGIR, ECIR, ACL, ECIR, Springer series) Technical Contributions feature real-time social media analysis platforms (e.g., Catenae , eXtream ) and benchmark datasets like DepreSym for depression detection.

• statistical learning theory
• probability
• nonparametric statistics
• +۱ مورد دیگر

Gábor Lugosi is an ICREA Research Professor affiliated with the Department of Economics and Business at Universitat Pompeu Fabra (UPF) in Barcelona. He earned his PhD in Electrical Engineering from the Hungarian Academy of Sciences in 1991 and has been at UPF since 1996. Education: PhD in Electrical Engineering (Hungarian Academy of Sciences, 1991) His research focuses on statistical learning theory , probability , nonparametric statistics , and learning in games . These areas intersect machine learning, game theory, and mathematical statistics, emphasizing theoretical foundations and algorithmic approaches. While specific scientific awards are not listed, his ICREA Research Professor designation highlights his esteemed status in academia. He contributes to education through courses, seminars, and advising, though no student names are provided. His work spans theoretical exploration in learning algorithms and probabilistic models, with ongoing grants and collaborations at UPF’s Jaume I Building on Ciutadella Campus.

Steffen Grünewälderمشاهده پروفایل

دانشیار

• Statistics
• Machine Learning
• Data Science
• +۳ مورد دیگر

Dr. Steffen Grünewälder is an Associate Professor in the School of Mathematics at the University of York. His work focuses on intersectional research areas connecting statistics, machine learning, and data science. Research Interests Statistics and probabilistic modeling Machine learning theory and applications Data science ethics (algorithmic fairness) Neural computation Recent Activities 2024: Talk at Laboratoire Paul Painlevé, Université de Lille 2023: Talks at ENSAE Paris, University of Edinburgh, and Laboratoire de mathématiques d’Orsay 2022: Presentations at INRIA Paris and USI Lugano 2021-2020: Participation in workshops at University of Washington, UC Berkeley, Fields Institute, and Machine Learning Tokyo

• Mining Engineering
• Rock Mechanics
• Environmental Impact Assessment
• +۳ مورد دیگر

Fernando María Garcia Bastante is a Full Professor at the University of Vigo, affiliated with the College of Mining Engineering and Department of Natural Resources and Environmental Engineering. His research focuses on mining engineering, rock mechanics, environmental impact assessment, and slope stability analysis. He has developed empirical models for blasting operations and collaborated on sustainable mineral resource management projects. His recent publications highlight advancements in granite strength analysis, blast-induced damage prediction, and AI applications for mining safety. These works span disciplines such as mining technology, computational modeling, and geospatial analysis. Specific subfields include detonation physics, rock fracture mechanics, and environmental compatibility of mining operations. Professor Garcia Bastante has contributed to quarry safety design, slope stability, and resource evaluation using statistical methods. He leads the GESSMin research group on Safe and Sustainable Management of Mineral Resources and has been involved with the Center for Industrial Process, Energy, and Technology Research (CIETI) at the Vigo campus.

SANDRA MARIA GOMEZ CANAVALمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Distributed Computing
• +۷ مورد دیگر

Sandra María Gómez Canaval is an Associate Professor at the Universidad Politécnica de Madrid , affiliated with the Department of Computer Systems . Her research focuses on: Artificial Intelligence and Machine Learning Bio-Inspired Computational Models Distributed and High-Performance Computing Generative Models and Network Security Data Mining and Time Series Forecasting Her recent work explores machine learning applications for: Network traffic prediction Harmful algal bloom forecasting Energy-efficient deep neural networks Automated guided vehicle control Cryptomining attack detection Her publications highlight expertise in Generative Adversarial Networks (GANs), cloud-based security, and Industry 4.0 systems. She actively contributes to: Data augmentation techniques Parallel computing architectures Network digital twin development

Thomas W. Repsمشاهده پروفایل

استاد

• Program Analysis
• Abstract Interpretation
• Model Checking
• +۴ مورد دیگر

Thomas W. Reps is the J. Barkley Rosser Professor & Rajiv and Ritu Batra Chair Emeritus at the University of Wisconsin-Madison , where he has been a faculty member since 1985. He is also President of GrammaTech, Inc., and a co-founder of the company. Education: Ph.D. in Computer Science from Cornell University (1982), winner of the 1983 ACM Doctoral Dissertation Award. Reps’s research spans program analysis , abstract interpretation , model checking , and computer security . His recent work focuses on quantum computing verification , probabilistic program analysis , and symbolic methods for static analysis . His publications (over 225) include foundational contributions to program slicing (1988 paper with Horwitz and Binkley, cited >1,780 times), machine-code analysis (ETAPS Best-Paper Awards in 2004 and 2008), and programming environments (co-author of The Synthesizer Generator ). Key awards include the ACM SIGPLAN Programming Languages Achievement Award (2017) , Guggenheim and Packard Fellowships , and ACM Fellow (2005) . Students: Mentored award-winning graduates like Akash Lal (SIGPLAN Outstanding Dissertation) and Venkatesh Srinivasan (Outstanding Graduate Student Research Award).

Thomas Wiesمشاهده پروفایل

استاد

• Program Analysis
• Verification
• Automated Deduction
• +۴ مورد دیگر

Thomas Wies is a Professor of Computer Science at the Courant Institute of Mathematical Sciences, New York University, and Chair of the Computer Science Department. He is a member of the Analysis of Computer Systems Group and has a personal website. Affiliation: New York University Research Focus: Program Analysis, Verification, Automated Deduction, Concurrency Teaching: Principles of Programming Languages (undergraduate), Programming Languages (graduate), Rigorous Software Development Research Interests: His work centers on program analysis, verification of concurrent systems, automated deduction, and applications of separation logic. He develops tools like Raven, GRASShopper, and Sprout for verifying concurrent programs and search structures. Recent Articles (2025-2023): Focus on SMT-based verification, concurrent data structures, temporal safety effects, multiparty protocols, and separation logic extensions. These span conferences like OOPSLA, CAV, IEEE S&P, and TACAS. Scientific Awards: Best Paper at ISSRE 2019 Best Paper at ICFP 2014 Professional Activities: Organizer/Co-organizer of VerifyThis 2025, Program Chair roles in NETYS 2023, VMCAI 2022, and others. Active in PC roles for POPL, PLDI, CAV, and VMCAI. Students & Group: Advises PhD students including Elaine Li, Devora Chait-Roth, and Ekanshdeep Gupta. Group alumni include Kshitij Bansal, Siddharth Krishna, and Wei Wang.

Yizhou Zhangمشاهده پروفایل

استادیار

• Programming Languages
• Probabilistic Programming
• Compiler Design
• +۴ مورد دیگر

Yizhou Zhang is an Assistant Professor at the Cheriton School of Computer Science, University of Waterloo. He specializes in programming language design and implementation, focusing on balancing expressive power with strong guarantees through language abstractions and compiler verification. Designing modular, type-safe language systems Probabilistic programming and inference optimization Algebraic effects and control flow management His recent publications explore lexical effect handlers, certified compiler frameworks, and nested polymorphism models. Awards include the ACM SIGPLAN Distinguished Paper Award (2023) for his work on probabilistic program compilation. Current advisees include PhD candidates Cong Ma, Zhaoyi Ge, Jianlin Li, and Ende Jin. He has served on program committees for POPL, PLDI, and OOPSLA conferences.

• Bayesian Networks
• Probabilistic Graphical Models
• Machine Learning
• +۵ مورد دیگر

Rafael Rumí Rodríguez is a Professor in the Mathematics Department at the University of Almería, Spain, with extensive experience in probabilistic modeling and data analysis. He leads the 'Análisis de datos' research group and serves as Principal Investigator for multiple research projects, including the current 'Hacia una Inteligencia Artificial Probabilística Confiable (TOPAI-UAL)' (2023-2026). Dr. Rumí Rodríguez's research focuses on Bayesian networks and probabilistic graphical models , with applications spanning environmental science, ecology, agriculture, and education. His work includes developing methods for continuous distributions in hybrid Bayesian networks, flood risk assessment in Mediterranean regions, climate change impact modeling for Andalusian agriculture, and educational assessment analytics. He has created practical tools like the 'hcrBN' R package for hybrid Bayesian network applications. His publication record includes 43 articles , 2 books or chapters , 3 theses , and 31 conference papers , with an h-index of 16 (Scopus) and 15 (Web of Science). Many publications appear in Q1 journals, reflecting significant research impact across computer science, environmental science, and statistics. Scopus h-index: 16 Web of Science h-index: 15 Scopus i10-index: 27 Web of Science i10-index: 23 Dr. Rumí Rodríguez has secured substantial research funding throughout his career, including as Principal Investigator for the current TOPAI-UAL project (€120,100) and previous projects like the European AMIDST initiative (€323,490). His research group provides opportunities for students to engage in both theoretical advances and practical applications of probabilistic modeling, with strong connections to environmental and agricultural domains in the Mediterranean region.

• Bayesian Networks
• Probabilistic Graphical Models
• Machine Learning
• +۵ مورد دیگر

Antonio Salmerón Cerdán is a Professor in the Mathematics Department at the University of Almería, where he has established himself as a leading researcher in probabilistic artificial intelligence and Bayesian networks. With over 25 years of academic experience, he leads the 'Análisis de datos' research group and serves as Principal Investigator for multiple nationally and internationally funded projects, including the current 'Hacia una Inteligencia Artificial Probabilística Confiable (TOPAI-UAL)' project (2023-2026). His research expertise spans theoretical and applied aspects of probabilistic graphical models, with particular focus on Bayesian networks, causal inference, and their applications across diverse domains. His work demonstrates a consistent trajectory from foundational theoretical contributions to practical implementations in software engineering, genomics, sports analytics, and trustworthy autonomous systems. Professor Salmerón's publication portfolio reveals a strong emphasis on methodological innovations in probabilistic reasoning, with recent work exploring divide-and-conquer approaches for causal computation, noise-robust classification methods, and the integration of observational and randomized data sources. His research shows increasing interdisciplinary reach, connecting computer science methodologies with applications in plant genomics, software maintenance, and healthcare. Journal Publications: 105 articles in high-impact venues including Ecological Informatics (Q1), International Journal of Approximate Reasoning (Q2), and ACM Transactions Research Funding: Principal Investigator for 9 major projects since 2001 totaling over €800,000 in funding Thesis Supervision: Director of 7 doctoral theses on probabilistic graphical models and their applications Metrics: h-index 22 (Web of Science), i10 index 59 His research program demonstrates a unique combination of theoretical rigor in probabilistic reasoning with practical applications across diverse scientific domains, positioning him at the forefront of reliable probabilistic AI development.

• Programming Language Semantics
• Computational Effects and Coeffects
• Relational Reasoning
• +۳ مورد دیگر

Francesco Gavazzo is an Assistant Professor at the Department of Mathematics, University of Padua. His research focuses on theoretical computer science, particularly in programming language semantics, computational effects, relational reasoning, and inductive/coinductive methods. PhD in Computer Science and Engineering, with Honor Mention MSc in Logic and Computer Science BA in Philosophy His work bridges formal methods with practical applications in program equivalence, quantitative semantics, and effect systems. Recent contributions include frameworks for effectful program distancing and relational theories of effects. Scientific awards include recognition by the Accademia delle Scienze dell'Istituto di Bologna (Top 10 in Science) and the Best Italian PhD Thesis in Theoretical Computer Science (EATCS Italian Chapter). He serves on program committees for POPL and ICFP.