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Danfeng ZhangView profile

Professor

• Programming Languages
• Security
• Differential Privacy
• +5 more

Danfeng Zhang is a faculty member at Duke University whose research sits at the intersection of programming languages and security. Active across the premier PL conferences since 2015, Zhang has served on more than two-dozen program committees and currently co-chairs the POPL Student Research Competition. Education & Affiliation: Home page: users.cs.duke.edu/~dz132 Affiliation: Duke University, United States Research Interests: Zhang’s work spans programming-language design, static and dynamic analysis, formal verification, and security. A recurring theme is developing language-based techniques that guarantee strong security and privacy properties—ranging from side-channel resistance and constant-time execution to differential-privacy proofs—while preserving performance and usability. His recent projects combine type systems, program logics, and automated reasoning to build practical verification tools for concurrent, speculative, and approximate software. Publication Trends: Across nine representative papers (2015-2024) Zhang has advanced static detection of cache side channels, automated proofs of differential privacy, and relaxed concurrency models. The trajectory shows deepening integration of security concerns into language infrastructure, with tool-building (CtChecker, SpecSafe, LightDP) that bridge formal guarantees and real-world systems. Service & Leadership: 2024 POPL Student Research Competition Co-Chair 2025 POPL Program Committee member Repeated reviewer/PC member: PLDI, SPLASH/OOPSLA, ISSTA, ECOOP, APLAS, PriSC, PASS Zhang regularly mentors student researchers through SRC sessions and workshop panels, fostering diversity and early-career participation in the programming-languages community.

Karina GibertView profile

Professor

• Data Science
• Artificial Intelligence
• Ethics in AI
• +7 more

Karina Gibert is a Full Professor at Universitat Politècnica de Catalunya (UPC), specifically at the Faculty of Computer Science of Barcelona (FIB) in the Department of Statistics and Operations Research. With a permanent teaching position since 1990, she contributes to research and education with a focus on Data Science, Artificial Intelligence, Explainable AI, and Ethics in AI. Full Professor, UPC (since 1990) PhD in Informatics Engineering Postgraduate in Higher Education Teaching Director and co-founder of IDEAI research center Dean of the Official Professional College on Informatics Engineering of Catalonia Active in bridging the gender gap in STEAM through multiple initiatives Her research focuses on extracting strategic knowledge from data and intelligent systems with ethical and explainable perspectives. She has led various projects including Diet4You for personalized diets, INSESS-COVID19 for social vulnerability analysis, Top Rosies Talent for female AI development, and ciutadanIA for AI culture promotion. Her work spans health, environment, sustainability, tourism, and social technology applications. As an editor of Environmental Modeling and Software journal and active academic, she contributes to international conferences, working groups, and research collaborations. Her service includes membership on various ethics and AI strategy committees and advisory boards. Women Tech Award 2023 National Informatics Engineering Award in Digital Dissemination 2022 Ada Byron Award 2022 Honorific Mention of Creu Casas award 2021 donaTIC 2018 Award Finalist at AMETIC Awards 2021 Finalist of European Social Services Awards 2021

Frank NIELSENView profile

Professor

• Data science
• Geometric science of information
• Information geometry
• +4 more

Frank NIELSEN is a Professor at École Polytechnique with expertise in information geometry, data science, and machine learning. He holds a PhD (1996) and HDR (2006) in computer science and has established himself as a leading researcher in geometric approaches to information science. His educational background includes a PhD in computer science (1996) followed by a Habilitation à Diriger des Recherches (HDR) in 2006, the highest academic qualification in France that qualifies one to supervise doctoral candidates. Dr. NIELSEN's research focuses on the Geometric Science of Information , where he develops theoretical frameworks for understanding data through geometric and information-theoretic lenses. His work bridges Computational information geometry Statistical manifold theory Bregman divergences and their applications Machine learning with geometric foundations High-dimensional data analysis He aims to address the challenge of inappropriate data representation in current Data Science by building a theory of Computational Information Geometry to enable Intrinsic Data Science with principled distances. His extensive publication record shows a clear trend toward developing geometric frameworks for understanding statistical divergences, with recent work focusing on Bregman geometry, Fisher-Rao metrics, and their applications in machine learning. His research spans theoretical developments in information geometry to practical implementations like the pyBregMan Python library, demonstrating both theoretical depth and practical relevance. Dr. NIELSEN has made significant contributions through his teaching and publications. He has taught courses at École Polytechnique including INF442, INF517, and INF591. His authored textbooks include Introduction to HPC with MPI for Data Science (2016), A Concise and Practical Introduction to Programming Algorithms in Java (2009), and Visual Computing: Geometry, Graphics, and Vision (2005). He has also edited influential volumes such as Computational Information Geometry for Image and Signal Processing (2016) and Geometric Theory of Information (2014). He actively organizes and participates in academic events, serving on program committees for major conferences including GSI (Geometric Science of Information), CVPR, and ICCV. His work has established him as a key figure in the growing field of geometric approaches to information science.

Maria José Serna IglesiasView profile

Professor

• Algorithmics
• Complexity Theory
• Social Network Analysis
• +3 more

Maria José Serna Iglesias is a Full Professor at the Departament de Ciències de la Computació of the Universitat Politècnica de Catalunya (UPC), Barcelona Tech . She leads the research group ALBCOM (Algorithmics, Bioinformatics, Complexity and Formal Methods) and coordinates doctoral programs in Computing. Her research focuses on algorithmics, computational complexity, social network analysis, and game theory. She actively participates in conferences such as SEA 2023-2025 , CIAC , and Algorithmic Decision Theory . Teaching includes advanced algorithmics courses for undergraduate and master’s programs. Current projects include the MOTION initiative (PID2020-112581GB-C21) on large-scale data processing. Past projects involve EU initiatives like WISEBED and DELIS . Her work bridges theoretical computer science with applications in networks and social systems, emphasizing algorithmic solutions for complex problems.

• Artificial Intelligence
• Robotics
• Data Science
• +4 more

Cecilio Angulo Bahón is a full Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Barcelona School of Industrial Engineering (ETSEIB) and the Department of Systems, Automatics and Industrial Informatics Engineering . He leads research in Artificial Intelligence and Robotics , with significant contributions to healthcare data analytics, digital twins, and human-robot collaboration. His research spans machine learning for medical data harmonization, generative adversarial networks in health informatics, and evolutionary algorithms for control systems. Recent publications focus on synthetic healthcare data generation, climate-resilient agriculture , and UMAP-based data analysis . His work bridges AI theory with practical applications in industrial and healthcare domains. Scientific awards include the Sant Jordi 2023 Digital Polytechnic Initiative Award . He has supervised doctoral candidates like Carlos Flores-Vázquez and N. Raya, with key collaborations at the IDEAI-UPC Intelligent Data Science and AI Research Group and the Institute of Robotics and Industrial Informatics (CSIC-UPC).

Carlos Palazuelos CabezónView profile

Professor

• Functional Analysis
• Quantum Information Theory
• Operator Spaces
• +7 more

Carlos Palazuelos Cabezón is a Professor in the Department of Mathematical Analysis and Applied Mathematics at the Faculty of Mathematical Sciences, Universidad Complutense de Madrid (UCM), with a joint affiliation at the Instituto de Ciencias Matemáticas (ICMAT). He holds a Ramón y Cajal contract and has been a Full Professor since 2024, following his role as Associate Professor from 2019 to 2024. His work bridges deep mathematical theory and quantum information science. Research Interests: His primary research lies at the intersection of functional analysis and quantum information theory. He investigates operator spaces and their applications to quantum entanglement, Bell inequalities, quantum channels, and nonlocal games. A second major line involves noncommutative harmonic analysis, particularly hypercontractivity in von Neumann algebras. His work is highly theoretical and appears in top mathematical and physics journals. Publication Trends: His recent publications demonstrate a consistent focus on the mathematical foundations of quantum information, particularly using tools from operator space theory and Banach space geometry to analyze quantum nonlocality, entanglement, and computational models. There is a strong trend toward solving foundational problems in quantum theory using advanced functional analysis. Scientific Awards: Premio Extraordinario de Doctorado (2008/2009) Doctorado Europeo Advising and Grants: While no specific students are listed, he is part of the MathQI research group and has likely mentored graduate students. He has been supported by prestigious postdoctoral and permanent research contracts in Spain, including the Juan de la Cierva and Ramón y Cajal programs, which are highly competitive grants for early-career and established researchers, respectively. Labs and Teams: He is a member of the MathQI research group (Mathematical Quantum Information) at UCM and is affiliated with ICMAT, a leading mathematics research institute in Spain. These affiliations provide a collaborative environment for interdisciplinary research in mathematical physics and quantum information.

• Combinatorics
• Graph Theory
• Algebraic Combinatorics
• +4 more

Lluis Vena Cros is a distinguished researcher in the Department of Mathematics at Universitat Politècnica de Catalunya - BarcelonaTech (UPC-BarcelonaTech), affiliated with the Faculty of Mathematics and Statistics. He is a core member of the GAPCOMB research group, which specializes in Geometric, Algebraic and Probabilistic Combinatorics. His research spans multiple domains within combinatorics, with particular emphasis on graph theory, algebraic combinatorics, geometric combinatorics, and probabilistic combinatorics. Vena Cros has made significant theoretical contributions to the understanding of graph homomorphisms, Tutte polynomials, Ramsey theory, and extremal combinatorial structures. His work often explores the deep connections between combinatorial objects and other mathematical disciplines such as topology, algebra, and discrete geometry. An analysis of his recent publications reveals a consistent focus on combinatorial structures with applications across multiple mathematical domains. His work on Tutte polynomials for various graph structures demonstrates a unifying approach to graph invariants, while his research in Ramsey theory explores structural properties of large combinatorial objects. The recurring theme across his publications is the investigation of extremal properties and computational aspects of combinatorial configurations. Vena Cros actively participates in multiple research projects including 'Structure, Randomness and Computational Methods in Extremal Combinatorics,' 'COntemporary COmbinatorics and Applications (COCOA),' and the 'Grup GAPCOMB de la UPC.' These projects involve collaborations with numerous researchers across Spain and internationally, reflecting his significant standing in the combinatorial mathematics community. His work has been supported by various funding bodies including the Spanish State Research Agency and the European Union's Horizon 2020 program. As a member of the GAPCOMB research group, Vena Cros contributes to a vibrant mathematical community focused on advancing combinatorial theory and its applications. The group maintains strong connections with other research centers and regularly participates in international conferences and collaborative projects, fostering the exchange of ideas across the global combinatorial mathematics community.

Sylvie PutotView profile

Professor

• Formal Methods
• Static Analysis
• Cyber-Physical Systems
• +7 more

Sylvie Putot is a Professor of Computer Science at École Polytechnique, where she focuses on formal verification of numerical programs and cyber-physical systems. She leads the Cosynus team at LIX laboratory and organizes the LIX Seminar series. Current affiliation: École Polytechnique (Computer Science Department) Prior affiliation: CEA LIST (developer of FLUCTUAT static analyzer) Her research spans formal methods for cyber-physical systems , emphasizing reachability analysis , invariant synthesis , and probabilistic verification . She combines abstract interpretation with zonotopic domains and constraint programming to address numerical stability and safety in AI-driven control systems. Recent publications highlight work on Safe AI through Formal methods (SAIF project) , including probabilistic guarantees for neural networks and applications in mobile robotics. Her team’s best paper award at EMSOFT 2015 recognized scalable quadratic invariant computation for embedded systems. Scientific awards: Best paper award, ACM SIGBED International Conference on Embedded Software (EMSOFT 2015) She has supervised numerous PhD students in topics like mobile robotics , neural network verification , and numerical error analysis . Active in ANR projects (COVERIF, MALTHY, DEFIS) and currently leads the PEPR IA initiative on AI validation.

• Knowledge Representation
• Answer Set Programming
• Non-monotonic Reasoning
• +5 more

Pedro Cabalar is Full Professor at the Department of Computer Science of the University of Corunna, Galicia, Spain, and current coordinator of the inter-university Master in Artificial Intelligence (Universities of A Coruña, Santiago de Compostela and Vigo). He also serves as Area Editor (Theory Foundations) for Theory and Practice of Logic Programming and as Standard Editor for the Artificial Intelligence journal. Education: PhD in Computer Science, University of Corunna, 2001 Master in Computer Science, Politéchnic University of Madrid, 1993 Bachelor in Computer Science (3-year degree), University of Santiago de Compostela / University of Corunna, 1989 Research interests revolve around Knowledge Representation & Reasoning , especially Answer Set Programming , non-monotonic reasoning , temporal and modal logics , and causal reasoning . He investigates theoretical foundations (equilibrium logic, temporal extensions, deontic operators) and practical systems (telingo, eclingo, aspBEEF), with applications ranging from planning and diagnosis to explainable AI and healthcare decision support. His recent articles (2023-2025) exhibit a clear trend toward temporal and metric extensions of ASP , explainability , and hybrid reasoning systems , often combining logic programming with deontic or probabilistic features. Scientific awards & recognition: University of Corunna Dissertation Award, 2003 Best Paper Award at LPNMR 2019 Best Student Paper at ICLP 2020 Best Student Paper at JELIA 2019 Grants & projects: He currently leads or co-leads nationally funded Spanish projects (GEISER 2024-2028, ARLEKIN 2021-2024) and has coordinated EU COST actions (DigForASP) as well as earlier MINECO projects on temporal ASP and medical reasoning (TARDIS, MERLOT, FEAST, etc.). PhD supervision: He has successfully supervised three PhD theses (Martín Diéguez, Jorge Fandiño, Brais Muñiz) and continues to advise students within the Information Retrieval Laboratory (IRLab) and the Spanish node of Potassco Solutions.

• Psychometrics
• Structural Equation Modeling
• Alcohol Consumption in Adolescents
• +5 more

Begoña Espejo Tort is a Full Professor in the Methodology of Behavioral Sciences department at the Faculty of Psychology and Speech Therapy , University of Valencia. Her research focuses on psychometric validation of scales, structural equation modeling, adolescent alcohol consumption analysis, and preventive program evaluation. Current academic affiliation: University of Valencia Research groups: PSY-HEALTH, SPORT&WB Her recent publications address cross-cultural scale validation, including work on vaccination attitudes, chronic pain misbeliefs, and well-being measurement. Articles cover topics like: Psychometric properties of scales across 13 countries Measurement invariance analysis by gender/age Well-being assessment in diverse populations Vaccination hesitancy and fear of disease Alcohol consequence assessment in youth Key collaborations include international teams in Vietnam, Colombia, Chile, and Spain. No scientific awards explicitly mentioned in available texts.

Alejandro AguirreView profile

Research Fellow

• Formal Verification
• Probabilistic Programming
• Programming Language Semantics

Alejandro Aguirre is a Research Fellow at Aarhus University, Department of Computer Science. Previously, he was a PhD student at Universidad Politécnica de Madrid and Imdea Software Institute under Gilles Barthe's supervision. Current Role: Postdoctoral Research Fellow at Aarhus University PhD Supervisors: Gilles Barthe, Lars Birkedal Education: PhD (2021), MRes (2016), Double Degree in Mathematics and Computer Science (2015) His research focuses on Formal Verification , Probabilistic Programming , and Programming Language Semantics . He develops logical frameworks for verifying probabilistic and concurrent programs, including Separation Logic extensions, Guarded Type Theory, and Relational Reasoning. His work addresses challenges in probabilistic coupling, termination analysis, error bounds, and expected costs in higher-order programs. The most recent articles highlight his contributions to asynchronous probabilistic couplings , error credits for resourceful reasoning , and guarded refinement for almost-sure termination . These works span Computer Science , Formal Methods , and Concurrency , with subfields like Program Logics , Probabilistic Reasoning , and Logical Relations . Scientific Recognition: Distinguished Paper Award, ICFP '24 Distinguished Paper Award, POPL '21 He has served on the Program Committee for POPL 2024 and OOPSLA Review Committee (SPLASH 2024). His work intersects Probabilistic Programming and Formal Verification , with applications to security, concurrency, and program logics.

Ankush DasView profile

Assistant Professor

• Programming Languages
• Cryptographic Protocols
• Distributed Systems
• +6 more

Ankush Das is a tenure-track assistant professor in the Computer Science Department at Boston University. His research focuses on programming languages with applications in cryptographic protocols, distributed systems, and probabilistic and machine learning models. Prior to joining Boston University, he worked as an applied scientist at Amazon in the Automated Reasoning Group until December 2023. Dr. Das received his PhD from Carnegie Mellon University in 2021, where he was advised by Prof. Jan Hoffmann and worked closely with Prof. Frank Pfenning. He completed his undergraduate studies at IIT Bombay, India in 2015. His research interests span a wide range of topics within programming languages, with specific focus on resource analysis, session types, distributed protocols, and language design for smart contracts on the blockchain. He is particularly interested in developing type systems that can ensure safety and efficiency properties in concurrent and distributed systems. His work has led to the development of domain-specific languages like Nomos for implementing smart contracts and Rast for resource-aware session types with arithmetic refinements. Dr. Das's publication record demonstrates a consistent focus on advancing type theory and its applications to practical systems. His work frequently appears in top-tier programming languages conferences such as POPL, PLDI, and ICFP. A notable trend in his recent work is the extension of session types to handle probabilistic computations and resource-aware programming, reflecting the growing importance of these areas in modern distributed systems. His scientific achievements have been recognized with several prestigious awards: Distinguished paper award at POPL 2024 for "Parametric Subtyping for Structural Parametric Polymorphism" Best system description paper award by a junior researcher at FSCD 2020 for "Rast: Resource-Aware Session Types with Arithmetic Refinements" Dr. Das is actively mentoring PhD and undergraduate students at Boston University. His current advisees include Anthony DeRossi, Toby Ueno, Brendan Coyne, Qiancheng Fu, June Wunder, Sam Buxbaum, and Sakshi Sharma. He is looking for motivated PhD students to join his research group, with opportunities to work on cutting-edge problems in programming languages and their applications to distributed systems and security. At Boston University, Dr. Das leads research efforts in programming language theory and its applications. His work bridges theoretical foundations with practical implementations, particularly in the areas of smart contracts and distributed protocols. He has collaborated extensively with researchers from Carnegie Mellon University, including his former advisors Jan Hoffmann and Frank Pfenning.

Clara Granell MartorellView profile

Associate Professor

• Complex Networks
• Epidemiology
• Complex Systems
• +3 more

Clara Granell Martorell is an Associate Professor at Rovira i Virgili University (URV), Tarragona, Spain, affiliated with the Department of Computer Engineering and Mathematics. Her research focuses on complex systems, particularly epidemic spreading on networks, community structure, and neuroscience. She holds a PhD in Complex Networks from URV and has held postdoctoral positions at the University of North Carolina (USA), Universitat de Barcelona, and Universidad de Zaragoza, funded by grants including the Beatriz de Galindo-Júnior program. Education: BSc in Computer Science from URV, MSc in Artificial Intelligence, followed by a PhD in Complex Networks. She returned to URV as an associate professor in 2024 after international postdoctoral research. Her work bridges theoretical and applied aspects of complex systems, with notable contributions to epidemic modeling, network community detection, and neuronal network dynamics. Research Highlights Developed probabilistic discrete-time models for epidemic spreading Explored behavioral responses to disease severity in age-specific populations Investigated functional recovery in damaged neuronal cultures Advanced methods for community detection in evolving networks Awards & Grants Beatriz de Galindo-Júnior Research Grant (201X) Teaching Teaches programming courses for undergraduates and data visualization for master’s students in Computer Science and Mathematics. Current courses include Programació Científica, Fonaments de Programació II, and Data Visualization in the 2024-25 academic year. Labs & Collaborations Member of the Alephsys research group, focusing on interdisciplinary complex systems research. Collaborates internationally with institutions like UNC and UB on network-based epidemiology and neuroscience projects.

• Legal Philosophy
• Philosophy of Private Law
• Law and Economics
• +7 more

Diego Martín Papayannis is a full-time Associate Professor in the Department of Private Law at the Faculty of Law, University of Girona, where he also serves as Vice-Dean. He holds a PhD in Law from Pompeu Fabra University and has held visiting positions at Yale Law School and Bocconi University. He is an active researcher in legal philosophy, particularly in the philosophy of private law, tort law, and law and economics. His educational background includes: Law Degree, University of Buenos Aires (2001) Master in Law and Economics, University of Buenos Aires (2007) PhD in Law, Pompeu Fabra University (2010) Visiting Research Scholar, Yale Law School (2013) His research centers on the philosophical foundations of private law, causality, legal theory, and the economic analysis of law. Recently, he has been exploring how private law contributes to public goals such as wealth redistribution, gender equality, and social justice. His work bridges doctrinal analysis with normative and economic perspectives, making significant contributions to both Spanish and international legal scholarship. The articles and books authored and edited by Diego Martín reflect a consistent focus on the philosophical and ethical dimensions of tort law, causality, and legal justification. His publications span leading journals such as Legal Theory , Ratio Juris , and Journal of European Tort Law , and major publishers like Cambridge University Press and Springer. The recurring themes include corrective justice, moral responsibility, legal neutrality, and the social function of private law. He has supervised 7 PhD theses and 2 postdoctoral researchers and is currently advising 5 doctoral students. He has led curriculum development initiatives, including the creation of a 30-ECTS English-language program at the Faculty of Law to enhance internationalization. His research is supported by participation in funded projects and editorial roles in significant academic publications. Diego Martín is a key member of the Research Group in Legal Philosophy at the University of Girona. His leadership as Vice-Dean has contributed to institutional innovation, particularly in promoting multilingual legal education and attracting international students. He continues to shape legal scholarship through his editorial work, publications, and supervision of emerging legal theorists.

• Machine Learning
• Artificial Intelligence
• Explainable AI
• +5 more

María Amparo Alonso Betanzos is a Professor in the Department of Computer Science and Information Technology at the Faculty of Informatics, Universidade da Coruña (UDC), Spain. She is an active researcher and educator specializing in Artificial Intelligence and Machine Learning, with a focus on scalable and explainable approaches. She is a member of the Laboratory for Research and Development in Artificial Intelligence at UDC. Dr. Alonso Betanzos has extensive teaching experience across multiple degree programs including the Degree in Artificial Intelligence and the Degree in Computer Engineering. Her current teaching portfolio includes courses such as Deep Learning, Development of Intelligent Systems, Knowledge-based Systems, and Intelligent Systems, covering both mandatory and optional subjects across undergraduate and master's programs. Her primary research interests span Scalable Machine Learning, Explainable Machine Learning, Sustainable Artificial Intelligence, and Agent-Based Modeling. Her work demonstrates a strong commitment to developing AI systems that are not only powerful but also interpretable and environmentally conscious. She has made significant contributions to methods for feature selection, classification algorithms, and knowledge representation in complex domains. Analysis of her recent publications reveals a consistent focus on advancing explainable AI techniques, particularly in medical and social applications. Her work frequently appears in top-tier journals including IEEE Transactions on Neural Networks and Learning Systems, Information Fusion, and Expert Systems with Applications. A notable trend in her recent work is the application of AI to healthcare challenges, with several papers published in biomedical computing journals. Dr. Alonso Betanzos has been actively involved in research funding, serving as Principal Investigator on multiple projects funded by the European Regional Development Fund, the Ministry of Science, Innovation and Universities, and regional Galician agencies. Her research group has secured continuous funding since at least 2003, demonstrating sustained excellence in research. She has directed numerous final degree and master's theses since 2013, with topics ranging from virus spread simulation using intelligent models to anomaly detection in mixed categorical-continuous spaces. Her students' work often focuses on practical applications of machine learning in healthcare, social systems, and recommendation systems. Dr. Alonso Betanzos is a core member of the Laboratory for Research and Development in Artificial Intelligence at UDC, which focuses on advancing fundamental AI research while addressing real-world challenges. The lab maintains strong collaborations with both national and international research groups, particularly in the areas of explainable AI and sustainable computing.