Find Academic Supervisors

• 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.

• Computer Architecture
• Programming Models
• Parallel Computing
• +5 more

Marc Gonzalez Tallada is a researcher at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the Barcelona School of Informatics (FIB). He is a member of the MGT Research Group, focusing on Programming Models and high-performance computing systems. His research interests lie in the domain of computer architecture and programming models, with a strong emphasis on parallel and distributed computing, performance optimization, and system-level software. His work contributes to the design and implementation of efficient programming models that exploit modern parallel architectures. The recent publications (2010–2024) reflect a consistent focus on programming models, particularly in the context of high-performance computing. Key themes include task-based programming, runtime systems, compiler optimization, memory management, and performance analysis. The research spans computer architecture, system software, and compiler design, demonstrating a multidisciplinary approach to solving challenges in parallel computing. Scientific Awards and Recognitions: Premi o reconeixement Advising and Grants: While specific student names are not listed, his involvement in competitive R&D+i projects indicates active participation in funded research. These projects suggest leadership or collaboration in grant-supported initiatives related to programming models and computer systems. His role in innovation and research projects highlights contributions to both academic and applied domains. Labs and Research Teams: He is a core member of the MGT (Programming Models) research group at UPC, which focuses on advancing the state-of-the-art in programming models for parallel and distributed systems.

Tiark RompfView profile

Assistant Professor

• Compiler Design
• Programming Languages
• Runtime Code Generation
• +8 more

Tiark Rompf is an Assistant Professor at Purdue University , with research spanning programming languages, compilers, and systems. His work bridges domains including architecture, databases, machine learning, and AI through projects like Reachability Types and Rhyme. Co-director of the Purdue Center for Programming Principles and Software Systems (PurPL) Scientific Advisor at SambaNova Systems Previously a member of the Scala team at EPFL His research focuses on: Runtime code generation and advanced compiler technology Expressive data-centric query languages (Rhyme, Datalog) Reachability type systems for memory safety and effect handling Metaprogramming and logical relations for formal verification Recent publications highlight contributions to Datalog compilation (Flan), nested data structures (Rhyme), and polymorphic reachability types. He leads projects exploring: Compiler optimizations for emerging architectures (GPU, TPU, FPGA) ML-driven compiler improvements Secure multi-party computation via metaprogramming Scientific awards include: NSF CAREER Award (2016) Google Faculty Research Awards (2017, 2018) DOE Early Career Research Award (2017) ACM SIGPLAN PL Software Award (2019) GPCE Test of Time Award (2020) Students and alumni from his group have joined institutions like Databricks, DeepMind, Galois, and Meta. He teaches advanced compiler courses including: CS 590 - Advanced Topics in Compilers CS 352 - Compilers CS 502 - Graduate Compilers

• Nuclear Fusion
• Plasma Physics
• Magnetohydrodynamics (MHD)
• +3 more

Jose Miguel Reynolds Barredo is an Associate Professor and Director of the Doctorate in Plasmas and Nuclear Fusion at Carlos III University of Madrid. His research focuses on plasma physics, magnetohydrodynamics (MHD), and energy systems resilience. He leads studies on stellarator reactor design, plasma confinement optimization, and the integration of renewable energy into power grids. His work spans advanced MHD equilibrium solvers (e.g., SIESTA, FLIPEC) and fusion device optimization for ITER and Wendelstein 7-X. He also investigates climate impacts on renewable energy efficiency and power grid stability under high renewable penetration. Notable contributions include HVDC grid segmentation strategies and non-axisymmetric plasma transport modeling. Key Areas: Fusion reactor design, MHD stability, power grid resilience, climate-energy interactions Tools: SIESTA, FLIPEC, GENE, OPA cascading blackout model Projects: Doctorate in Plasmas and Nuclear Fusion, W7-X bootstrap current studies, climate-energy system interdependencies Research emphasizes computational plasma physics and interdisciplinary energy solutions, blending theoretical, numerical, and applied engineering approaches.

• Cooperative Robotic Systems
• Multi-Robot Systems
• Human-Robot Collaboration
• +7 more

Luis Merino is an Associate Professor at the School of Engineering, Universidad Pablo de Olavide (UPO), Seville, Spain. He founded and leads the Service Robotics Laboratory and contributed to establishing the Systems Engineering and Automation division at UPO. He served as Vice-Dean for five years and currently coordinates the Computer Science degree program. Education: Ph.D. in Robotics from the University of Seville (2007), supervised by Anibal Ollero. Research: Focuses on cooperative robotic systems, human-robot collaboration, localization/navigation techniques, and machine learning in social robotics. His work includes leading 2 H2020, 4 FP7, 3 National R&D, and 3 Andalusian regional projects. Notable projects: MBZIRC 2020 (PI), collaboration with Honda Research Institute Japan, and EU-funded initiatives. He advocates for open-source code/datasets and industry technology transfer. Scientific Awards: ABB Award to the Best Doctoral Dissertation on Robotics (2007) Best Paper Award at ROBOT2019 Professional Roles: Associate Editor for Image and Vision Computing and IEEE Robotics and Automation Letters . Serves on ICRA/IROS conference program committees. Grant reviewer for FONDECYT (Chile), SBIR (USA), and ERC.

• Applied Analytics
• Statistical Inference
• Time Series Analysis
• +7 more

Dr. Laleh Tafakori is a Senior Lecturer in the Department of Statistics and Analytics at RMIT University's School of Science. Her research focuses on the intersection of statistical modeling, machine learning, and their applications in healthcare, finance, and environmental science. She is affiliated with the university's City Campus and can be contacted at laleh.tafakori@rmit.edu.au . Her teaching interests include Applied Analytics , Statistical Inference , Time Series Analysis , Mathematical Statistics , Stochastic Processes , and Probability Theory . She actively supervises research projects in areas such as: Healthcare modeling (e.g., diabetes onset prediction, maternal risk assessment) Environmental data analysis (e.g., extreme precipitation estimation via satellite data) Financial risk analysis (e.g., Value-at-Risk forecasting, credit portfolio management) Machine learning applications in complex networks and smart grids Her recent research trends emphasize predictive modeling for public health challenges, leveraging advanced statistical techniques (copula models, functional volatility) and machine learning (CNNs, random forests). She collaborates with institutions worldwide, addressing issues like Saudi Arabia's healthcare indicators and European financial systemic risk. While no formal awards are listed, her work demonstrates impactful contributions to interdisciplinary data science. Dr. Tafakori has advised over 16 students on topics ranging from diabetes epidemiology to edge computing optimization. Her research outputs include 28+ peer-reviewed articles, with a focus on methodological innovation and real-world problem-solving. She maintains active engagement in collaborative projects without explicitly listed grants.

Diego Sevilla RuizView profile

Associate Professor

• Software Engineering
• Database Systems
• NoSQL Technologies
• +3 more

Diego Sevilla Ruiz is an Associate Professor at the University of Murcia's Faculty of Informatics, Department of Computer Engineering and Technology, where he leads initiatives in Software Engineering research. He completed his PhD in 2008 on distributed component models (CORBA-LC). His research focuses on database technologies, particularly schema evolution in NoSQL and relational databases, model-driven engineering approaches, and unified metamodeling. Recent work develops Skiql (a schema query language), Athena (schema definition language), and methodologies for referential integrity in graph databases. His publications advance database abstraction techniques, schema migration frameworks, and automated tools for database management and modernization.

Josep CasanovasView profile

Professor

• Modelling and Simulation
• Internet and Information Systems
• Urban Mobility
• +4 more

Josep Casanovas is a Full Professor at the Statistics and Operations Research Department of the Technical University of Catalonia (UPC), affiliated with the Barcelona School of Informatics. He previously served as head of inLab FIB (2012-2020) and as dean (1998-2004) and vice-rector (2006-2011) of UPC, leading strategic initiatives in university governance and ICT policies. His research focuses on Modelling and Simulation , Internet and Information Systems , and Urban Mobility . He has led projects for the European Union, including C-ROADS Spain, REMEDiAL, and ECHORD++, addressing intelligent transport, software automation, and robotic innovation. Recent publications highlight his work on agent-based simulation for urban health, deep learning applications in traffic and energy savings, and wildfire management tools . He co-directs LogiSim and coordinates the Severo Ochoa Research Excellence Program at the Barcelona Supercomputing Center (BSC-CNS).

• Biomedical Engineering
• Autonomous Systems
• Robotics
• +4 more

Rafael Barea Navarro is a Professor in the Department of Electronics Technology at the University of Alcalá. His research focuses on biomedical engineering, autonomous systems, robotics, artificial intelligence, and driver safety. He leads the Biomedical Engineering Research Group (GIB) and the Robótica de Servicios y Tecnologías para la Seguridad Vial (Robesafe) group. His work bridges AI applications in medical diagnostics (e.g., multiple sclerosis via OCT) and advanced autonomous vehicle technologies, including motion prediction, reinforcement learning, and real-time safety systems. Education: PhD in Electronics from the University of Alcalá (2001), with a thesis on EOG-based human-computer interfaces for mobility assistance. His research spans over 50 peer-reviewed articles since 2000, emphasizing practical applications in healthcare and robotics. Key research themes include: 1) AI-driven medical diagnostics; 2) autonomous vehicle control systems; 3) sensor fusion for navigation; and 4) driver attention monitoring. His work integrates robotics, computer vision, and biomedical signal processing. Publications emphasize interdisciplinary approaches, with recent work combining OCT and explainable AI for early disease diagnosis, and hybrid reinforcement learning frameworks for urban autonomous driving.

• Computer Science
• Artificial Intelligence
• Robotics

Gonzalo Esteban Costales is a researcher affiliated with the University of Leon from 2012 to 2025, specializing in Computer Science and Artificial Intelligence within the Robotics research group. His work bridges haptic simulation, robotics, and educational technology. Education: Ph.D. in Computer Science (2020), University of Leon, with a thesis on haptic simulators for expert knowledge transfer . Research Interests: Haptic simulation for surgical training and rehabilitation Robotics security in industrial environments Educational technology frameworks Version control analytics for student performance Open-source vulnerability detection tools Key Article Trends: Recent publications focus on haptic workspace optimization, threat modeling for robotic production plants, and security enhancements in continuous integration workflows. His work spans simulation design, cybersecurity, and educational applications of robotics.

• Computer Architecture
• Programming Models
• Parallel Computing
• +4 more

Daniel Jimenez Gonzalez is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the School of Informatics (FIB). He is an active researcher in programming models and high-performance computing, contributing extensively to the field through publications, projects, and academic supervision. Research Interests: His work focuses on Computer Architecture , Programming Models , and Parallel Computing . He investigates task-based programming, runtime systems, compiler optimizations, and performance modeling for multi-core and heterogeneous architectures. His research addresses challenges in scalability, energy efficiency, and resilience in modern computing environments. His recent publications show a consistent trend in task-based programming models , runtime scheduling , and performance optimization across diverse architectures, including NUMA and GPU-accelerated systems. The work spans from theoretical modeling to practical implementation, often targeting real-world HPC applications. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: While specific students are not listed, his involvement in doctoral theses and R&D projects suggests an active role in mentoring graduate students. He has participated in both competitive and non-competitive R&D+i projects, indicating grant acquisition and project leadership experience in areas related to programming models and computer architecture. Labs and Teams: He is a member of the UPC PM - Programming Models research group, which focuses on the design, analysis, and optimization of modern programming paradigms for high-performance systems.

• Formal Methods
• Satisfiability (SAT)
• Satisfiability Modulo Theories (SMT)
• +11 more

Enric Rodríguez Carbonell is a faculty member at the Department of Computer Sciences within the Faculty of Computer Science at Universitat Politècnica de Catalunya (UPC). He is a key member of the LOGPROG - Lògica i Programació research group, focusing on formal methods, automated reasoning, and combinatorial optimization. Research Interests: His work centers on satisfiability (SAT), satisfiability modulo theories (SMT), and their applications in program verification, constraint solving, and optimization. He investigates techniques such as conflict-driven learning, invariant generation, and Max-SMT for proving termination and safety. His research bridges theoretical foundations with practical applications in software analysis and industrial problem-solving. Publication Trends: His recent publications (2020–2024) show a sustained focus on enhancing SAT and SMT solvers, particularly in pseudo-Boolean reasoning, integer linear programming, and multi-conflict analysis. Earlier works established contributions in non-linear arithmetic, termination proofs, and efficient encodings for cardinality constraints, reflecting a long-term commitment to foundational and applied aspects of automated reasoning. Scientific Awards: Best student paper award at SAT 2024 Advising and Grants: Rodríguez Carbonell has contributed to multiple competitive R+D+i projects under Spain’s State Research Plans, indicating active grant involvement. He has co-authored doctoral theses and educational initiatives like Jutge.org, demonstrating engagement in academic supervision and pedagogical innovation. Labs and Teams: He is a core researcher in the LOGPROG group at UPC, which specializes in logic and programming, with strong collaborations across formal methods, verification, and constraint technologies.

Jesper CockxView profile

Assistant Professor

• Type Theory
• Dependently Typed Programming
• Pattern Matching
• +6 more

Jesper Cockx is an Assistant Professor in the Programming Languages group at Delft University of Technology . His research focuses on the theory and implementation of dependently typed languages , particularly Agda , and he is a core contributor to Agda's development. His work bridges Type Theory , Pattern Matching , and practical Compiler Design through tools like agda2hs . Research Interests : Type Theory and Dependently Typed Programming Pattern Matching and Unification Agda and Compiler Implementation Erasure Annotations for Verified Compilation Generic Programming and Language Design Formal Verification of Functional Code Scientific Awards : NWO Veni Grant (2020) for a Trustworthy and Extensible Core Language for Agda Conference Contributions : He serves on program committees for CPP , WITS , POPL , and ICFP . Recent publications analyze erasure annotations , correct-by-construction compilers , and dependent (co)pattern matching in Agda. His work often intersects with Haskell and functional programming education in the Netherlands.

David Chaves-FragaView profile

Assistant Professor

• Knowledge Graphs
• Data Integration
• Semantic Web
• +4 more

David Chaves-Fraga is an Assistant Professor at Universidade de Santiago de Compostela (Spain), affiliated with CiTIUS (Center for Intelligent Technologies) and a research collaborator at KU Leuven's DTAI group. His expertise lies in Knowledge Graph Construction (KGC), focusing on declarative mapping rules, data integration, and semantic web technologies. He completed his PhD at Universidad Politécnica de Madrid in 2021, researching Knowledge Graph Construction from heterogeneous data sources. Education PhD in Artificial Intelligence, Universidad Politécnica de Madrid (2016–2021) Master in Artificial Intelligence, Universidad Politécnica de Madrid (2015–2016) Bachelor in Computer Science, Universidade de Santiago de Compostela (2011–2015) Research Interests Dr. Chaves-Fraga specializes in optimizing data integration systems using declarative rules (e.g., RML), scalable KG materialization, and benchmarking tools like KROWN. He emphasizes reproducibility and sustainability in KG creation, advocating for community-driven standards. His work bridges theory and practice, addressing challenges in real-world KG adoption. Contributions He co-chairs the W3C Knowledge Graph Construction Community Group, organizes workshops like KGC and Sem4Tra, and coordinates initiatives like Open Summer of Code. His tools (e.g., SDM-RDFizer, RMLdoc) are widely used in the semantic web community. Key themes include RDF-star generation, SHACL constraint extraction, and ontology-mapping interoperability.

• Plasma Propulsion
• Electric Propulsion
• Spacecraft Engineering
• +3 more

Eduardo Antonio Ahedo Galilea is a Full Professor in the Department of Aerospace Engineering at Universidad Carlos III de Madrid (UC3M), leading the Plasmas and Space Propulsion Team (EP2). His research focuses on plasma propulsion systems, particularly Hall thrusters, magnetic nozzles, and helicon plasma thrusters, with expertise in plasma dynamics, wave-plasma interaction, and spacecraft-plume effects. Current projects: MLPLUS-CM (2025-2028), PROPULSION ELECTROMAGNETICA POR EFECTO HALL (2023-2026) International collaborations: European Commission, Airbus Defense and Space, SENER His recent publications analyze non-stationary plasma expansions, electron cooling mechanisms, and magnetic nozzle physics across multiple thruster designs. He supervises theses on turbulent transport, wave-plasma interaction, and fluid-kinetic thruster modeling. Grants include funding from the European Commission Research Executive Agency, Agencia Estatal de Investigación (AEI), and regional programs like RIS3-CAM.