جست‌وجوی استاد و پژوهشگر

• Financial Economics
• Credit Risk
• Sovereign CDS Markets
• +۱۰ مورد دیگر

Laura Ballester Miquel is an Associate Professor at the Universitat de València, affiliated with the Faculty of Economics and the Department of Financial and Accounting Studies. She is a member of the EFEN (Financial Economics and Energy Finance) research group, contributing to advanced research in financial risk, credit markets, and sustainability finance. Research Interests: Her work spans financial economics, with a focus on sovereign credit default swaps (CDS), interest rate risk in banking, green finance, and corporate governance. She also investigates pedagogical methods in finance and accounting education, particularly the impact of self-assessment on student performance. The recent publications indicate a strong trend toward integrating sustainability and environmental performance into financial risk models, alongside continued work on systemic risk, credit scoring using machine learning, and volatility spillovers in global markets. Her interdisciplinary approach combines econometric modeling, empirical finance, and educational innovation. Scientific Contributions: Extensive research on sovereign CDS and stock market volatility linkages Studies on green bond issuance and credit risk Development of credit scoring models using random forests Analysis of corporate governance and default risk Innovative work in finance and accounting pedagogy Advising and Grants: While specific grant details are not listed, her sustained publication record and PhD supervision suggest active involvement in research projects and academic mentoring. She supervised her doctoral thesis on interest rate exposure in Spanish banks and continues to contribute to the academic training of future economists. Labs and Teams: She is actively involved in the EFEN research group, which focuses on financial economics and energy finance, promoting collaborative research and international scholarly engagement.

• Algorithms
• Data Structures
• Combinatorics
• +۹ مورد دیگر

Conrado Martinez Parra is a Professor in the Department of Computer Science at the Faculty of Computer Science, Universitat Politècnica de Catalunya (UPC). He is a core member of the ALBCOM research group, which focuses on Algorithmics, Bioinformatics, Complexity, and Formal Methods. Affiliation : Department of Computer Science, Faculty of Computer Science (FIB), UPC Research Group : ALBCOM - Algorísmia, Bioinformàtica, Complexitat i Mètodes Formals Email : conrado@cs.upc.edu ORCID : 0000-0003-1302-9067 Researcher ID : G-4629-2015 His research spans theoretical computer science with a strong emphasis on the design and analysis of algorithms and data structures. His work includes average-case analysis of algorithms, combinatorial generation, probabilistic methods in algorithmics, and applications in information retrieval and data stream processing. He has extensively studied multidimensional data structures such as quadtrees, K-d trees, and skip lists, analyzing their performance under various query models including partial match and orthogonal range searches. His recent publications reveal a sustained focus on algorithmic efficiency, sampling techniques, and probabilistic modeling in data structures. Trends indicate a deep engagement with randomized algorithms, unbiased estimation, and cache-efficient selection methods, reflecting both theoretical rigor and practical applicability in modern computing environments. Scientific Contributions Extensive publication record spanning over three decades, from 1989 to 2024. Active in major algorithmic conferences such as ANALCO, AofA, and AAAI. Contributions to foundational algorithm analysis including Hoare’s FIND, Quickselect variants, and deletion in binary search trees. Collaborative research with prominent figures in theoretical computer science across Europe. Professor Martinez Parra has advised or collaborated with several doctoral students, including Gustavo Lau, whose thesis on partial match queries he supervised. He has participated in numerous competitive R&D projects funded by national and regional programs, focusing on large-scale information processing and graph-based computing models. His work is supported by long-standing grants from Spanish and Catalan research councils. He is affiliated with the ALBCOM research group, a leading team in algorithmic research at UPC, contributing to both theoretical advances and practical implementations in combinatorics and data structure optimization.

• Theoretical Computer Science
• Algorithms
• Computational Complexity
• +۶ مورد دیگر

Maria Jose Serna Iglesias is a Full Professor at the Faculty of Informatics of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC)-BarcelonaTech, where she is affiliated with the Department of Computer Sciences. She is a leading member of the ALBCOM research group, focusing on Algorithmics, Bioinformatics, Complexity, and Formal Methods, and serves as the coordinator of the PhD program in Computing. Her academic leadership and research excellence are central to UPC's contributions in theoretical computer science. Licenciado en Ciencias Matemáticas Licenciado en Informática Doctorat en Informàtica (Doctorate in Computing) Her research interests lie at the intersection of theoretical computer science and game theory, with a strong focus on algorithmic game theory , social network analysis , and computational complexity . She investigates influence propagation models, cooperative games, weighted voting systems, and graph algorithms. Her work often involves the design and analysis of algorithms for discrete structures, with applications in network science and decision-making systems. The analysis of her recent publications reveals a consistent trend in studying strategic interactions in networks, particularly through influence models and cooperative game theory . Her work spans both theoretical foundations—such as complexity and dimensionality of games—and practical applications in social networks, education, and synthetic data validation. The keywords across her articles highlight a deep engagement with discrete mathematics, optimization, and algorithmic decision theory. She has received scientific recognition, including competitive awards and active participation in scientific committees of major international conferences such as the International Conference on Algorithmic Decision Theory and the Discrete Mathematics Days. Premiada (Awarded) Scientific committee member for multiple international conferences Maria Jose Serna has led and participated in numerous competitive R+D+i projects, often in collaboration with other prominent researchers in the ALBCOM group. She has advised multiple students and researchers, contributing to the training of the next generation of computer scientists. Her collaborations span institutions in Spain and internationally, particularly in the domains of algorithms and game theory. She is a core member of the ALBCOM research group (Algorísmia, Bioinformàtica, Complexitat i Mètodes Formals) at UPC, which is a leading force in theoretical computer science in Spain. The group fosters interdisciplinary research in algorithms, complexity, and formal methods, with strong ties to mathematical and computational sciences.

• Computational Complexity
• SAT Solvers
• MaxSAT Algorithms
• +۳ مورد دیگر

M. Luisa Bonet Carbonell is a researcher in the Department of Computer Science at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB). Her work focuses on computational complexity, SAT and MaxSAT solvers, proof systems, and algorithmic optimization. She has extensive collaborations within the LOGPROG and ALBCOM research groups, contributing to projects like TASSAT3 and MULOG-2. Her research spans theoretical computer science, with emphases on proof complexity, polynomial calculus, and the analysis of industrial SAT instances. Notable contributions include studies on Sherali-Adams and Nullstellensatz proof systems, as well as the fractal dimension of SAT formulas. Over 90 publications include articles in Artificial Intelligence , Journal of Computer and System Sciences , and conferences like SAT and IJCAI. Her work bridges theoretical foundations with practical applications, such as improving MaxSAT algorithms and analyzing community structures in problem instances.

• Organometallic Chemistry
• Catalysis
• Inorganic Chemistry
• +۶ مورد دیگر

Professor José Tomás Cuenca Agreda is a distinguished University Professor in the Department of Organic Chemistry and Inorganic Chemistry at the Faculty of Pharmacy, University of Alcalá (UAH). With a career spanning several decades, he has established two major research lines: Sustainable Catalytic Processes with Organometallic Compounds and the Design, Interaction and Synthesis of Bioactive Compounds. His research has been supported by numerous grants from the University of Alcalá, the Ministry of Economic Affairs and Digital Transformation, and the Community of Madrid. Professor Cuenca Agreda's research interests focus on organometallic chemistry, particularly titanium-based systems, with applications in sustainable catalysis and medicinal chemistry. His work includes activation of CO 2 and other small molecules, C-H bond activation, development of catalysts for polymerization processes, and synthesis of metal-based compounds with potential anticancer activity. His research bridges fundamental organometallic chemistry with practical applications in sustainable chemistry and pharmaceutical development. Analysis of his recent publications (2018-2020) reveals a strong focus on titanium and aluminum-based catalysts for polymerization processes, chiral complexes for asymmetric catalysis, and water-soluble metal complexes with biological activity. His work demonstrates continued innovation in catalyst design, with particular attention to sustainability and potential biomedical applications. Professor Cuenca Agreda has secured significant research infrastructure for his group, including a 400 MHz NMR spectrometer (2019) and various other equipment through multiple infrastructure grants. His research group maintains active collaborations with other departments at UAH and with external research institutions. His laboratory environment supports advanced synthesis of air-sensitive organometallic compounds, catalyst testing, polymerization studies, and biological evaluation of potential therapeutic compounds. Students in his group gain experience with state-of-the-art characterization techniques and participate in national and international conferences.

• Dental Caries Prevention
• Oral Microbiology
• Biofilm Research
• +۴ مورد دیگر

Pilar Baca García is a Professor at the Department of Stomatology , University of Granada , with over three decades of contributions to oral microbiology , caries prevention , and endodontic research . Her work spans clinical trials, biomaterials evaluation, and community oral health initiatives. Key affiliations: University of Granada, Dialnet CRIS Academic identifiers: ORCID, Scopus, OpenAlex Research Interests: Her work focuses on biofilm dynamics in oral infections, antimicrobial therapies for caries and periodontal diseases, and preventive dentistry strategies for vulnerable populations. She has pioneered studies on chlorhexidine varnishes and fissure sealant longevity . Article Trends: From 1987-2020, her research evolved from bacteriocinotyping of Streptococcus mutans to bioethical dental practices and 3D bioprinting applications . Recurring themes include biofilm analysis , dental caries epidemiology , and endodontic irrigation solutions . Advising Legacy: She has supervised 15 doctoral theses on topics like biofilm eradication , root canal disinfection , and temporomandibular joint prosthetics . Her students now lead research in oral oncology and dental biomaterials .

• 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.

• Nursing
• Geriatric Care
• Chronic Disease Management
• +۷ مورد دیگر

José Manuel Hernández Padilla is a Professor in the Department of Nursing, Physiotherapy and Medicine at the University of Almería's School of Health Sciences. With an h-index of 20 (Scopus) and 19 (Web of Science), he maintains an active research program with 107 articles, 13 book chapters, and 6 theses to his name. His current research is supported by multiple projects including PID2020-117579RA-I00 as Principal Investigator and several 'Transfiere' research transfer grants. Dr. Hernández Padilla's research primarily focuses on geriatric nursing care for older adults with chronic conditions, migrant health (particularly irregular migrants and gender-based issues), end-of-life care and dignity preservation, nursing education methods, and compassion in healthcare. His work frequently examines the intersection of social determinants of health with clinical care, with a strong emphasis on qualitative research methods to understand lived experiences of vulnerable populations. Recent publications demonstrate his commitment to addressing health disparities among older adults living in poverty and improving care for migrant populations. His publication record shows consistent output in high-impact journals, particularly in nursing (Journal of Advanced Nursing, BMC Nursing) and public health (Frontiers in Public Health). The research spans qualitative, quantitative, and mixed methods approaches, with a clear trajectory toward community-engaged scholarship that bridges research and practice. His work often involves interdisciplinary collaboration with colleagues across health sciences. Dr. Hernández Padilla has supervised multiple theses on topics including female genital mutilation, nursing communication competence, and end-of-life communication. His active research projects through 2024-2025 suggest ongoing opportunities for student involvement in meaningful research that addresses pressing healthcare challenges in Spain and beyond. His scholarly contributions extend to research methodology, particularly qualitative approaches, with book chapters on qualitative research tools and designs. This methodological expertise supports his substantive research on vulnerable populations and informs his approach to nursing education.

John Wickersonمشاهده پروفایل

مدرس ارشد

• concurrency semantics
• FPGAs
• GPUs
• +۵ مورد دیگر

John Wickerson is a Senior Lecturer in the Department of Electrical and Electronic Engineering at Imperial College London. His research spans formal methods, concurrency, and hardware/software synthesis. Academic Rank: Senior Lecturer Affiliation: Imperial College London His research interests include concurrency semantics, weak memory models, transactional memory, GPU and FPGA programming, and high-level synthesis for hardware accelerators. These areas intersect formal verification, programming language design, and hardware-software interface optimization. The publications of John Wickerson reflect trends in formalizing memory models, improving hardware synthesis reliability, and testing concurrency frameworks. His work addresses challenges in quantum compiler validation, GPU workgroup progress, and weak memory persistency across Intel, ARM, and C++ architectures. He actively contributes to academic communities as a Publicity Co-Chair and Session Chair in conferences like POPL and as a Committee Member in SPLASH and PLDI. His GitHub repository activity and X (Twitter) presence further demonstrate his engagement in technical dissemination.

Alexandra Silvaمشاهده پروفایل

استاد

• Programming Languages
• Formal Verification
• Kleene Algebra with Tests
• +۶ مورد دیگر

Alexandra Silva is a Professor of Computer Science in the Department of Computer Science at Cornell University's College of Engineering. She joined Cornell as faculty in 2021 after previously serving as a Royal Society Wolfson Fellow and Professor of Algebra, Semantics, and Computation at University College London. Her research spans programming languages, formal verification, and theoretical computer science, with particular focus on Kleene Algebra with Tests (KAT), probabilistic programming, and automata theory. She has held numerous leadership roles in major programming languages conferences including POPL, PLDI, and ICFP. Dr. Silva completed her PhD at Centrum Wiskunde & Informatica (CWI) in Amsterdam under the supervision of Jan Rutten and Marcello Bonsangue, with her thesis entitled "Kleene coalgebra" defended in December 2010. Prior to her PhD, she was an undergraduate student at University of Minho in Portugal, where she completed a 5-year Mathematics and Computer Science degree in May 2006. Her research focuses on the modular development of specification languages and algorithms for models of computations, often from the unifying perspective offered by coalgebra. She has made significant contributions to Kleene Algebra with Tests, probabilistic programming semantics, network verification, and automata learning. Her work bridges theoretical foundations with practical verification tools, particularly in the domain of Software-Defined Networking where her NetKAT framework has gained significant attention. Analysis of her recent publications reveals a strong trend toward unifying frameworks for program verification, particularly through her development of Outcome Logic which provides foundations for both correctness and incorrectness reasoning. Her work increasingly integrates probabilistic and concurrent aspects of programming languages, with applications to network verification and security. The NetKAT ecosystem remains a central theme, with extensions to infinite state verification, symbolic execution, and learning-based approaches. Distinguished paper award for Guarded Kleene algebra with tests: verification of uninterpreted programs in nearly linear time (POPL 2020) Dr. Silva advises a large research group with numerous PhD students, postdocs, and undergraduate researchers. Her group has produced significant work in programming languages theory, verification, and applications to networking. She has secured substantial research funding through various grants that support her work on formal methods for network verification and probabilistic programming. Her mentoring approach emphasizes both theoretical depth and practical impact, with many of her students moving to prestigious academic and industry positions. Her research group, spanning both Cornell University and University College London, focuses on developing theoretical foundations for programming languages with practical applications in network verification, probabilistic systems, and program analysis. The group maintains active collaborations with researchers at CWI, University of Oxford, and other leading institutions in programming languages and formal methods.