Find Academic Supervisors

• Computer Architecture
• High-Performance Computing
• Distributed Systems
• +12 more

Yolanda Becerra Fontal is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the Barcelona School of Informatics (FIB). She is actively involved in research projects and collaborations, notably with the Barcelona Supercomputing Center, and is a member of prominent research groups such as the High Performance Computing Group (CAP) and CROMAI (Computing Resources Orchestration and Management for AI). Research Interests: Her research spans a broad spectrum of computer systems, with a consistent focus on performance, efficiency, and scalability. Key areas include Computer Architecture , High-Performance Computing (HPC) , Distributed and Cloud Systems , Resource and Energy Management in virtualized environments, and Data-Intensive Computing . More recently, her work has centered on innovative time-series database systems and data management for edge and cloud analytics. Publication Trends: Her recent scholarly output (2020-2022) shows a strong emphasis on time-series data management, proposing novel database architectures like NagareDB and strategies for polyglot persistence. Earlier work (2009-2013) was pivotal in MapReduce workload management, energy accounting for virtualized systems, and optical data center networks, demonstrating a long-standing contribution to foundational distributed computing challenges. Scientific Contributions: Her work has been published in top-tier journals and conferences such as Future Generation Computer Systems , IEEE Transactions , and Nucleic Acids Research . She has also contributed to significant competitive R&D projects and holds patents related to data flow management and distributed indexing. Advising and Grants: Dr. Becerra Fontal has served as a thesis advisor for doctoral students. Her research has been funded through competitive grants from national and regional programs, including Spanish State Research Plans (Plan Estatal de Investigación) and Catalonia's RIS3CAT strategy, supporting projects on high-performance computing and data management. Research Groups and Labs: She is a core member of the CAP - High Performance Computing Group and the CROMAI - Computing Resources Orchestration and Management for AI group at UPC. Her work is closely associated with the Barcelona Supercomputing Center (BSC) , one of Europe's leading supercomputing facilities, indicating access to advanced computational infrastructure.

• Polymer Chemistry
• Energy Storage
• Organic Electronics
• +4 more

Kenichi Oyaizu is a Professor in the Department of Applied Chemistry at the Faculty of Science and Engineering, Waseda University, Tokyo. His research spans polymer chemistry, energy storage, and materials science, with a focus on functional polymers for batteries, hydrogen storage, and high refractive index applications. He maintains active collaborations across academia and industry. Professor Oyaizu's research centers on designing polymers with tailored redox properties for energy storage systems, including organic radical batteries and hydrogen carriers. He pioneers high refractive index materials through molecular engineering of hydrogen-bonded networks and sulfur-rich frameworks. His group integrates machine learning with experimental synthesis, utilizing lossless data platforms for materials discovery and optimization in electrochemistry and optical applications. Analysis of his 15 most recent publications (2020-2025) reveals three dominant research trajectories: (1) High-refractive-index polymers leveraging hydrogen bonding and sulfur incorporation for optical devices, (2) Energy storage systems using redox-active polymers for batteries and hydrogen carriers, and (3) Materials informatics approaches applying generative models and quantum-inspired algorithms to accelerate polymer design. These streams demonstrate consistent innovation in structure-property relationships for functional materials. No scientific awards or major honors are documented in the provided materials. Professor Oyaizu leads an active research group mentoring graduate students and postdoctoral researchers in polymer synthesis and characterization. His work receives funding from Japanese national agencies supporting sustainable energy materials and advanced polymer research, though specific grant details are not disclosed in the source text. Current projects emphasize machine learning-driven development of solid-state electrolytes and hydrogen storage polymers. His laboratory operates within Waseda University's advanced materials infrastructure, utilizing specialized facilities for polymer synthesis, electrochemical testing, and optical characterization. The team collaborates with international researchers on battery technologies and participates in university-industry consortia focused on sustainable materials development, with recent projects highlighted in Waseda University News and EurekAlert!.

Maria Ribera SanchoView profile

Professor

• Software Engineering
• Conceptual Modeling
• Ontologies
• +4 more

Dr. Maria Ribera Sancho is a Professor at the Polytechnic University of Catalonia (BarcelonaTech), holding roles as Dean of the Faculty of Informatics of Barcelona (2004–2010), Vice-Dean (1998–2004), and currently Manager of the Education and Training Department at the Barcelona Supercomputing Center (BSC-CNS). She chairs the EQANIE Accreditation Committee and serves on the ACM-W Europe Executive Committee. Her research focuses on Software Engineering, Conceptual Modeling, Ontologies, Learning Analytics, and IoT applications. Notable contributions include work on automated design using conceptual models, model-driven software development, and semantic-based IoT infrastructure monitoring. She leads projects like LinDaFIX (social welfare data tools) and REMEDiAL (ontology-driven software automation). Key awards include the UPC Quality in Teaching Prize (2005, 2021), Jaume Vicens Vives Distinction (2005), Sapiens Award (2011), and Festibity Award (2011). She advises doctoral and master’s students on topics like IoT semantic monitoring and educational ontology development. Academic director of the inLab FIB Talent Program, she also directs the PRACE Advanced Training Center at BSC. Main projects include TINTIN (SQL integrity tool), e-Catalunya (collaborative government platform), and PILARES (learning analytics for secondary education). Her work bridges academia and industry through competitive projects with companies and institutions.

• Quantum Machine Learning
• Reinforcement Learning
• Medical Informatics
• +12 more

Jose David Martin Guerrero is a Professor in the Department of Electronic Engineering at the School of Engineering, Universitat de València. His research is conducted within the Intelligent Data Analysis Laboratory (IDAL), focusing on the intersection of quantum computing, machine learning, and biomedical applications. His research interests span Quantum Machine Learning , Reinforcement Learning for Quantum Control , Deep Learning for Medical Image Analysis , and Long-COVID Symptom Trajectory Modeling . He applies advanced AI techniques to solve complex problems in physics, healthcare, and engineering, particularly in modeling prostate cancer, hemodialysis, and post-COVID neurological and functional impairments. The recent publications highlight a strong trend toward integrating quantum computing with classical machine learning, especially in developing quantum neural networks, quantum autoencoders, and reinforcement learning for quantum gate optimization. Simultaneously, a significant body of work uses clustering, deep learning, and Bayesian modeling to analyze longitudinal clinical data from multicenter studies like LONG-COVID-EXP, aiming to understand symptom persistence and recovery patterns in post-COVID patients. Dr. Martin Guerrero earned his PhD from the Universitat de València in 2004 with a thesis on unsupervised techniques for web trend detection and collaborative filtering-based recommendation systems, supervised by Dr. Emilio Soria Olivas and Dr. Paulo Jorge Gomes Lisboa. His work demonstrates active collaboration across disciplines, particularly in clinical research involving multicenter studies, and in quantum computing applications in physics and finance. While no specific grants are mentioned, his involvement in large-scale clinical studies and quantum computing research suggests significant project leadership and funding acquisition. He leads research within the Intelligent Data Analysis Laboratory (IDAL) , which focuses on self-organizing maps, clustering, learning vector quantization, and their applications in security, healthcare, and quantum systems.

• Algorithms
• Randomized Data Structures
• Divide-and-Conquer Algorithms
• +5 more

Salvador Roura Ferret is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Informatics of Barcelona (FIB) and the Department of Computer Sciences. He is an active researcher in the ALBCOM group, focusing on algorithmics, bioinformatics, complexity, and formal methods. His research interests span algorithms , randomized data structures , divide-and-conquer methods , and computational complexity . His work often bridges theoretical computer science with practical applications in voting systems, game theory, and educational technologies. The recent publications (2013–2025) reflect a strong trend in theoretical computer science, particularly in algorithm design, data structures (e.g., K-d trees, binary search trees), and game-theoretic models. There is a notable emphasis on mathematical rigor, complexity analysis, and formal modeling of computational problems. Best student paper award Distinció Jaume Vicenç Vives a la qualitat docent universitària (modalitat individual) 18è Premi UPC a la Qualitat en la Docència Universitària Roura has contributed to teaching innovation through projects like Eines per a la Transformació de la Docència en Programació and the development of Jutge.org , an online programming judge platform. He has participated in numerous competitive R+D+i projects, often in collaboration with researchers such as Conrado Martinez, Xavier Molinero, and Maria Serna. His work has been supported by national and regional funding programs. While no direct mention of grants or student advising is made, his extensive publication and project history suggest significant research leadership.

• Machine Learning
• Climate Science
• Energy Systems
• +3 more

Sancho Salcedo Sanz is a Full Professor at the Universidad de Alcalá, affiliated with the Signal Theory and Communications Department and the GHEODE Research Group. His work focuses on applying machine learning and optimization techniques to energy systems, climate science, and environmental modeling. He holds PhDs from Universidad Complutense de Madrid (2019) and Universidad Carlos III de Madrid (2002). Key research interests include deep learning for energy price prediction, spatio-temporal climate analysis, and hybrid models for renewable energy forecasting. His GHEODE group develops optimization algorithms for network design and distributed systems. Recent publications highlight advancements in extreme weather prediction, smart grid optimization, and explainable AI for environmental monitoring. He has pioneered methodologies like Autoencoder-based flow analogues for heatwave reconstruction and multi-method ensembles for energy demand modeling. Labs/Teams: Leader of the GHEODE Group, specializing in modern heuristics and network design. Collaborates extensively on interdisciplinary projects combining AI with environmental and engineering applications.

Alejandro Laguna SanzView profile

Assistant Professor

• Diabetes Technology
• Glucose Monitoring
• Artificial Pancreas Systems
• +7 more

Alejandro Laguna Sanz is an Assistant Professor in the Department of Electronic Engineering at the School of Engineering, Universitat de València. His work bridges biomedical engineering and clinical diabetes care, focusing on technological solutions for glucose regulation. His research interests include diabetes technology, artificial pancreas systems, glucose monitoring accuracy, and the impact of exercise on glycemic control. He is particularly active in developing and evaluating predictive algorithms for glycemic management, especially in dynamic conditions such as physical activity and post-bariatric hypoglycemia. His work often combines control theory with clinical validation. The recent publications reveal a strong focus on real-world performance of continuous glucose monitors (CGMs), sex-specific responses in women with type 1 diabetes, and innovative interventions like elastic band resistance training and dual-hormone closed-loop systems. His research increasingly integrates population modeling, in silico studies, and virtual education platforms for artificial pancreas training. He is a member of the CLIDET research group (Clinimetry and Technological Development in Therapeutic Exercise), where he contributes to the design and clinical evaluation of advanced monitoring and control systems for metabolic diseases. Alejandro Laguna Sanz earned his PhD from Universitat Politècnica de València with a thesis on uncertainty in postprandial glucose modeling in type 1 diabetes, supervised by Dr. Paolo Rosseti and Dr. Jorge Bondía Company. He has not received any explicitly listed scientific awards in the provided text. He advises students within his research group, though no specific names are listed. His work is supported by collaborative projects in diabetes technology and engineering, particularly in the development of adaptive control systems and clinical validation of glucose monitoring devices. He is involved in both experimental and simulation-based studies.

• Biometrics
• Behavioral Biometrics
• Mobile Authentication
• +12 more

Ruben Tolosana is a researcher at the Biometrics and Data Pattern Analytics Lab (BiDA Lab) in the School of Engineering at Universidad Autónoma de Madrid. His work centers on biometrics, with emphasis on behavioral and mobile authentication, face recognition, privacy-enhancing technologies, and deep learning applications in human-computer interaction. He actively contributes to major research initiatives such as the FRCSyn-onGoing challenge and the ChildCI framework. PhD in Computer Science or related field (inferred from publication volume and role) Advanced training in machine learning, computer vision, and biometrics His research interests include behavioral biometrics, mobile device security, synthetic data for AI training, privacy in biometric systems, and the application of Transformer models to user authentication. Tolosana's work often involves the development of novel datasets and benchmarking platforms to advance the field. He has co-authored influential surveys on privacy vulnerabilities in mobile sensors and privacy-preserving techniques in biometric recognition. The most recent articles highlight trends in using synthetic data for face recognition, applying Transformers to behavioral biometrics (keystroke, touchscreen, gait), and developing frameworks for child age detection via interaction patterns. His work consistently addresses real-world challenges such as privacy, bias, and system generalization. Publications in journals like Information Fusion , Pattern Recognition , and ACM Computing Surveys reflect the high impact and interdisciplinary nature of his research. Second FRCSyn-onGoing: Winning solutions and post-challenge analysis to improve face recognition with synthetic data (2025) ChildCI framework: Analysis of motor and cognitive development in children-computer interaction for age detection (2024) FRCSyn-onGoing: Benchmarking and comprehensive evaluation of real and synthetic data to improve face recognition systems (2024) SwipeFormer: Transformers for mobile touchscreen biometrics (2024) An Overview of Privacy-Enhancing Technologies in Biometric Recognition (2024) Ruben Tolosana collaborates extensively with researchers in the BiDA Lab, including Rubén Vera-Rodríguez, Aythami Morales, Julian Fierrez, and others. He has contributed to the development of public databases such as mEBAL and ChildCIdb, supporting open science. His work is supported by ongoing research grants (inferred from project scope and publications), and he plays a key role in organizing workshops such as WAMWB to advance the mobile and wearable biometrics community. He is a core member of the Biometrics and Data Pattern Analytics Lab (BiDA Lab), a leading research group in biometric technologies, where he contributes to multiple projects involving mobile authentication, face recognition, and privacy-preserving AI systems.

• Educational Technology
• Higher Education Internationalization
• Natural Language Processing
• +7 more

Maria Teresa Abad Soriano is affiliated with the Universitat Politècnica de Catalunya (UPC), specifically within the Faculty of Computer Science (FIB), Department of Computer Sciences. She is a researcher in the GPLN - Natural Language Processing Group and the TALP Research Center, contributing to both educational technologies and computational linguistics. Her work spans decades, showing sustained academic engagement. Her research interests focus on educational technology, higher education internationalization, computer-supported collaborative learning, distance learning systems, knowledge representation, and academic mentorship . These are reflected in her long-term involvement in projects related to e-learning platforms, tutoring systems, and institutional development in computing education. The analysis of her recent publications (2019–2024) reveals a strong trend towards the internationalization of higher education in computing, with detailed studies on mobility, academic policies, and institutional impact. Earlier works (1990s–2000s) center on algorithmic education, groupware modeling, and intelligent tutoring systems, showcasing a career that evolved from technical AI and educational software to broader educational policy and management. She has contributed to competitive R&D+i projects such as OPENMT and SKATeR, and has co-authored technical reports, conference papers, and indexed journal articles. Her collaborations include key figures like Nuria Castell, Juan Antonio Pastor, and Maria Felisa Verdejo. Scientific Awards: No scientific awards were mentioned in the provided text. She has supervised or contributed to the Doctoral Program in Business Administration and Management, and her technical and educational outputs suggest an advisory role in research, though specific students are not listed. She has participated in multiple competitive research projects funded under Spain’s National Research Plans, indicating success in grant acquisition. Her work with open datasets and technical documentation supports open science and institutional transparency. She is actively involved in research groups such as GPLN and TALP, which focus on natural language processing and language technologies, contributing to both theoretical and applied advancements in the field.

Eneko AgirreView profile

Professor

• Natural Language Processing
• Computational Linguistics
• Language Technology
• +7 more

Eneko Agirre is a Full Professor at the Faculty of Computer Science of the University of the Basque Country UPV/EHU, where he serves as the director of the HiTZ Centre on Language Technology. He is an active member of the Ixa Research Group and has established himself as a leading figure in Natural Language Processing, particularly in multilingual and low-resource language settings. His work bridges theoretical advances with practical applications for language technology. Agirre received his PhD from the University of the Basque Country in 1999 with a thesis on conceptual relationships and ontologies, supervised by Dr. Kepa Sarasola Gabiola and Dr. Arantza Díaz de Ilarraza Sánchez. His academic journey has been marked by significant contributions to computational linguistics and language technology. His research primarily focuses on Natural Language Processing challenges, with special emphasis on Word Sense Disambiguation, cross-lingual transfer learning, dialogue systems, and Large Language Models for low-resource languages. He has pioneered work on Basque language technology and has consistently addressed the challenges of multilingual AI systems, particularly examining how language models perform across different linguistic contexts and cultural settings. Analysis of his recent publications reveals a strong trajectory toward advancing Large Language Models for low-resource languages, with particular attention to Basque. His work spans vision-language models, information extraction techniques, and rigorous evaluation methodologies for NLP systems. A recurring theme is the exploration of how language models handle low-resource languages compared to high-resource ones, with groundbreaking findings about cultural knowledge transfer between languages. Fellow of the ACL (2021), one of only 74 research leaders worldwide National Research Prize on Informatics (2021) Best resource paper award at ACL 2024 Honourable mention paper award (top 1%) at EMNLP (2020) Outstanding Paper award (top 2%) at COLING (2020) Recipient of three Google Faculty Research Awards (2017, 2018, 2019) Agirre has supervised over 25 PhD students, many of whom have received prestigious awards including the EurAI Artificial Intelligence PhD Dissertation Award. His research has been supported by numerous European projects including LIHLITH (2018-2020) on lifelong learning for dialogue systems, and he has served as principal investigator for multiple CHIST-ERA and FP7 projects. His work with Google includes collaborative projects on entity dictionaries and conversational question answering systems. As director of the HiTZ Centre on Language Technology and member of the Ixa Research Group, Agirre leads a vibrant team focused on advancing language technology for Basque and other under-resourced languages. The center has developed significant resources including Latxa, an open language model for Basque, and has established itself as a hub for multilingual NLP research. His group actively collaborates with international institutions including Stanford, NYU, and various European universities, fostering a global network for language technology research.

• Privacy
• Security
• Algorithmic Accountability
• +5 more

Bogdan Kulynych is a research scientist at Lausanne University Hospital in Switzerland, working within the Clinical Data Science group. He holds a Ph.D. in Computer Science from EPFL (Switzerland), where he was advised by Carmela Troncoso, and a B.Sc. in Applied Mathematics from Kyiv Mohyla Academy in Ukraine. His academic journey also includes a visiting fellowship at Harvard University with Flavio du Pin Calmon, and internships at Google and CERN. His research spans three interconnected domains: Algorithmic Accountability, Verification, and Reliability; Privacy-Preserving Learning and Statistics; and Algorithmic Systems in Healthcare. Kulynych develops methods for obtaining practical guarantees on model stability, robustness, and reliability, while also auditing these properties. His privacy work focuses on systems ensuring practical privacy guarantees with legally legible and interpretable operational risk analyses. In healthcare, he critically studies algorithmic system deployment in clinical practice through collaboration with clinicians and medical informatics practitioners. Kulynych's publication record demonstrates significant impact in top venues including NeurIPS, ICML, ICLR, FAccT, and PETS. His recent work addresses fundamental questions in differential privacy, operational privacy metrics, and healthcare AI applications. His research trend shows increasing focus on translating theoretical privacy guarantees into practical healthcare settings while addressing the social implications of algorithmic systems. Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy (NeurIPS 2025) (ε,δ) Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees (2025) Attack-Aware Noise Calibration for Differential Privacy (NeurIPS 2024) As an active member of the academic community, Kulynych regularly presents at major conferences and seminars, including recent talks at Harvard Privacy Tools Seminar, NeurIPS, and Imperial College London. His work has received media attention in The Guardian, Wired, The Verge, and CNET regarding algorithmic bias challenges. Kulynych maintains an active presence on Bluesky (@bogdankulynych) where he engages in critical discussions about AI ethics, privacy, and the societal implications of technology.

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).

Daniel Pizarro PérezView profile

Professor

• Computer Vision
• Medical Imaging
• Smart Grids
• +3 more

Dr. Daniel Pizarro Pérez is a Professor in the Department of Electronics at the University of Alcalá, Spain. He is a member of the GEINTRA research group, which focuses on Electronic Engineering applied to Intelligent Spaces and Transport. He holds a doctoral degree from the University of Alcalá, completing his thesis on 'Localización de robots móviles en espacios inteligentes utilizando cámaras externas y marcas naturales' (2008), supervised by Dr. Manuel Ramón Mazo Quintas and Dr. Enrique Santiso Gómez. His research interests span computer vision, medical imaging, smart grid technologies, acoustic signal processing, and control systems. Notable projects include augmented reality applications in laparoscopic surgery, non-intrusive load monitoring using smart meters, and advanced control methodologies for power electronics. His work bridges theoretical advancements with practical applications in healthcare, energy systems, and robotics. Recent publications highlight contributions to neural radiance fields for minimally-invasive surgery, distributed acoustic sensing in submarine environments, and deep learning-based activity recognition via energy consumption. His research frequently integrates interdisciplinary approaches, such as combining computer vision with medical robotics and leveraging machine learning for real-time control systems. Dr. Pizarro’s work has been supported by grants and collaborations within the GEINTRA group, with applications in intelligent spaces, robotic navigation, and sensor fusion. He has also contributed to educational initiatives like the GEMS Erasmus+ project, emphasizing sensory module development for robotics education.

• Assistive Technologies
• Health Informatics
• Biomedical Engineering
• +7 more

Dr. Andreu Català Mallofré is a Full Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Systems, Automatic and Industrial Informatics Engineering. He serves as the principal investigator at the CETpD - Centre d'Estudis Tecnològics per a l'Atenció a la Dependència i la Vida Autònoma (Technical Research Center for Dependency Care and Autonomous Living) and leads the TOC - Tecnologia Orientada a la Comunitat (Community-Oriented Technology) research group. With an academic career spanning over 30 years, he specializes in assistive technologies, human-computer interaction, and biomedical data analysis. His research interests focus on Wearable health monitoring systems Parkinson's disease management solutions Frailty assessment algorithms Emotion-driven computing Community-embedded technology Gerontechnology applications These areas have produced 367 academic outputs including 59 indexed journal articles, 148 conference papers, and 66 competitive R&D projects. Recent publications demonstrate a clear trend toward multimodal health monitoring combining inertial sensors with biometric data for aging populations . Key application areas include freezing-of-gait detection in Parkinson's patients and distress estimation during assisted mobility. His 2024 work explores agricultural sustainability through data-driven fertilizer optimization. Scientific recognition includes UPC Social Council Award (2013) 20+ years as conference committee member Multiple competitive R&D grants 23391773000 Scopus ID Orcid 0000-0001-8775-1955 As academic advisor, he has mentored 3 PhD candidates including Daniel Manuel Rodriguez-Martin (Human Movement Analysis, 2020) Leonid Ivonin (Physiological Signal Processing, 2014) John Neal Abram Brown (Calm Computing, 2014) His teams operate within Vilanova i la Geltrú's School of Engineering, with strong industry and healthcare sector collaborations.

• Robot Cooperation
• Human-Robot Interaction
• People Motion Behavior
• +5 more

Anaís Garrell Zulueta is an Associate Professor at the Polytechnic University of Catalonia (UPC) and a Robotics Researcher at the Institut de Robòtica i Informàtica Industrial (CSIC-UPC) . She serves as Vice-Director of the Mobile Robotics and Intelligent Systems subline and supervises the RAIG - Mobile Robotics and Artificial Intelligence Group . PhD (2013): European Doctorate with highest honors from UPC B.S. in Mathematics (2006) from University of Barcelona Diploma d'Estudis Avançats (DEA) in Control, Vision, and Robotics from UPC Her research focuses on Human-Robot Interaction (HRI) and robot cooperation , particularly in urban environments . Key themes include: Explainable AI for robot transparency Human motion behavior prediction Socially aware navigation systems Collaborative transport robotics Autonomous last-mile delivery systems Cybernetic avatars and societal implications Recent projects (2023-2025) include: TORNADO: Foundation models for robots handling deformable objects HandIA: AI-based rehabilitation tools LENA: Lifelong navigation learning TRIFFID: First responder assistance robotics SOCIAL PIA: Cybernetic avatar modeling Scientific recognition includes: Second Prize for Best Spanish Robotics Thesis Best Paper Award Nomination (IEEE/RSJ IROS, 2009) As an advisor, she supervises: PhD students: Ferran Gebelli Guinjoan, Lavinia Hriscu, Edison Bejarano Final year projects: 8 students on topics like LLM-enhanced interaction and LiDAR SLAM systems She operates within the Institut de Robòtica i Informàtica Industrial (IRI) and collaborates with Carnegie Mellon University.