Find Academic Supervisors

Marta CasanellasView profile

Professor

• Algebraic Geometry
• Computational Biology
• Phylogenetics
• +4 more

Marta Casanellas is a full professor in the Department of Mathematics at the Polytechnic University of Catalonia (UPC) and a researcher at the Centre de Recerca Matemàtica. She teaches at the Faculty of Mathematics and Statistics, ETSEIB, and FIB. She earned her PhD in mathematics from the University of Barcelona under RM Miró-Roig, focusing on algebraic geometry and liaison theory. After completing a postdoc at UC Berkeley with a Fulbright Scholarship, she shifted her research to applications of algebraic geometry in computational biology, particularly phylogenetics. Her educational background includes a PhD from the University of Barcelona (2002), followed by a postdoctoral fellowship at UC Berkeley (2002-2003) supported by a Fulbright Scholarship. She obtained a prestigious Ramón y Cajal contract at UPC in 2003, which marked her transition to interdisciplinary research at the intersection of mathematics and biology. Casanellas' research focuses on applying algebraic and geometric techniques to phylogenetics, with particular emphasis on evolutionary models, phylogenetic invariants, and computational methods for genomic data analysis. Her work bridges pure mathematics (particularly algebraic geometry) with biological applications, developing mathematical frameworks to reconstruct evolutionary histories and understand genomic relationships. She has published extensively in both mathematics journals like Advances in Mathematics and biology journals like Molecular Biology and Evolution. Her recent publications demonstrate a consistent focus on developing algebraic methods for phylogenetic analysis, with increasing attention to heterogeneous evolutionary processes across lineages, time-reversible models, and computational implementations of theoretical results. The trend shows progression from theoretical foundations in algebraic geometry toward increasingly sophisticated and applicable computational methods for biological data. Fulbright Scholarship for postdoctoral research at UC Berkeley Ramón y Cajal contract (2003) Casanellas has supervised PhD students including A. Kedzierska (co-supervised with R. Guigó of the CRG). She has served as principal investigator for three competitive Spanish government projects involving fifteen researchers each. She has held significant academic leadership roles including Deputy Director of Research of the Department of Mathematics at UPC (2015-2018), head of studies for the Degree in Data Science and Engineering at UPC (2018-2022), and currently coordinates UPC's PhD in Bioinformatics program and Bachelor's Degree in Bioinformatics. She leads the BIO-GEOMAP research group focused on applying mathematical techniques to biological problems.

Luis Merino CabañasView profile

Professor

• Robotics
• Systems Engineering
• Automation
• +5 more

Luis Merino Cabañas is a Professor at the Universidad Pablo de Olavide , affiliated with the Deporte e Informática department and leading the SRL Service Robotics Laboratory . His research focuses on robotics, systems engineering, and automation, with a specialization in human-robot interaction and path planning. Education : PhD in Systems Engineering from the Universidad de Sevilla (2007), where his thesis explored cooperative perception techniques for multiple unmanned aerial vehicles in forest fire detection. Research Trends : Recent work (2023–2025) emphasizes 3D path planning, sensor fusion (LiDAR, radar, inertial systems), neural distance fields for safe navigation, and socially aware robotics. His studies integrate AI, genetic programming, and multi-modal perception for applications in construction, healthcare, and GNSS-denied environments. Labs & Teams : He leads the SRL Service Robotics Laboratory , contributing to projects like the Skyeye team and BIM2ROS integration for construction robotics.

• Computational Neuroscience
• Spacetime Connectomics
• Neurotwin Models
• +5 more

Jakub Vohryzek is a postdoctoral researcher at the Computational Neuroscience (CNS) Group at University Pompeu Fabra in Barcelona, supervised by Prof. Gustavo Deco. His work focuses on spacetime connectomics and whole-brain modeling, particularly in neurodegenerative disorders and psychedelic neuroscience. He holds a DPhil from the University of Oxford, where he studied under Prof. Morten Kringelbach. Research Interests: Spacetime connectomics Psychedelic-induced brain state transitions Neurotwin models for personalized medicine Cognitive and clinical applications of whole-brain dynamics Current projects include developing neurotwin models under a European grant for neurodegenerative treatments, investigating brain state dynamics in mindfulness therapy, and modeling psychedelic effects on Alzheimer’s disease. His recent work emphasizes low-dimensional brain network interactions and functional hierarchy perturbations. His research has explored connectivity profiles, oscillatory restoration in dementia, and algorithmic agent approaches to neuropsychiatric disorders. He collaborates on open-science initiatives like Brainhack and advocates for inclusive conference design.

• Optimization Algorithms
• Smart Grids
• Renewable Energy Systems
• +3 more

Silvia Jiménez Fernández is an Associate Professor in the Department of Signal Theory and Communications at Universidad Autónoma de Madrid. Her research focuses on optimization algorithms, smart grids, renewable energy systems, telemedicine, and machine learning applications. She holds a Ph.D. from Universidad Politécnica de Madrid (2009), supervised by Dr. Francisco del Pozo Guerrero and Dr. Paula de Toledo Heras. Her work integrates interdisciplinary approaches, such as combining evolutionary algorithms with engineering challenges in energy systems and healthcare. Key contributions include advancements in coral reefs optimization algorithms for energy management, machine learning for battery health estimation, and telemedicine systems for chronic disease monitoring. Recent research trends emphasize hybrid learning models in education, multi-objective optimization in renewable energy systems, and risk analysis in smart grids with electric vehicles. She is affiliated with the GHEODE Research Group (Modern Heuristics and Network Design).

• Signal Processing
• Machine Learning
• Acoustics
• +7 more

Luis Antonio Azpicueta Ruiz is an Associate Professor in the Department of Signal Theory and Communications at Carlos III University of Madrid. He leads research in the Signal Processing and Learning Group (GTSA) and Machine Learning for Data Science (ML4DS) group, focusing on interdisciplinary applications spanning acoustics, telecommunications, and machine learning. Research Interests: His work bridges signal processing theory with practical applications in environmental acoustics, adaptive filtering systems, and machine learning. Key research themes include: Advanced adaptive filtering architectures for nonlinear systems Distributed estimation in sensor networks Acoustic echo cancellation and room equalization Psychoacoustic evaluation methods Machine learning applications in noise monitoring and sound analysis Research Projects: Principal investigator for multiple funded projects including: Diagnóstico del ruido de chorro en aeronaves (AEI, 2022-2025) LearnINg FLow and Noise Dynamics via AI (COMUNIDAD DE MADRID, 2024-2026) BODYinTRANSIT - Sensory-driven Body Transformation (EUROPEAN COMMISSION, 2022-2026) Aprendizaje Automático para análisis Big Data (MINISTERIO DE ECONOMÍA, 2018-2021)

• Computer Science
• Machine Learning
• Evolutionary Algorithms
• +4 more

Raul Giraldez Rojo is a Professor at the University of Pablo de Olavide in Seville, Spain, working in the Department of Sports and Information Technology within the area of Computer Languages and Systems. He is affiliated with the DASE (Data Analytics Science & Engineering) research group and contributes to PhD programs in Engineering, Data Science, and Bioinformatics. His research interests focus on computer science with specialization in machine learning, evolutionary algorithms, and data analytics. Dr. Giraldez Rojo has made significant contributions to supervised learning techniques, clustering algorithms, and genetic programming applications. His work bridges theoretical computer science with practical applications in bioinformatics and data mining. His publication record demonstrates expertise in developing algorithms for data analysis, particularly in biclustering methods for expression data and supervised clustering techniques. His research shows a consistent focus on improving efficiency and effectiveness of computational methods for handling complex datasets. Dr. Giraldez Rojo collaborates extensively with researchers including Jesús Salvador Aguilar-Ruiz and José Cristobal Riquelme Santos, indicating strong research partnerships within the Spanish academic community specializing in computational intelligence.

• Data Mining
• Bioinformatics
• Machine Learning
• +3 more

Domingo Savio Rodríguez Baena is a Professor at Pablo de Olavide University, affiliated with the Department of Computer Languages and Systems. His research focuses on data mining, bioinformatics, and computational biology, with a particular emphasis on biclustering algorithms, gene co-expression networks, and high-performance computing applications. PhD in Engineering, Data Science, and Bioinformatics (2012) from Pablo de Olavide University His work spans interdisciplinary domains, including recommender systems , livestock behavior analysis , and biological data interpretation . Recent articles highlight his contributions to multi-GPU optimization , ensemble learning , and historical database construction . Key collaborations include the DATAi Intelligent Data Analysis and DASE Data Analytics Science & Engineering research groups. He has developed tools like the CyEnGNet–App for gene network visualization and BIGO for gene enrichment analysis. Contact: dsrodbae@upo.es

• Industrial Engineering
• Maintenance Optimization
• Industry 4.0/Digital Transformation
• +6 more

Aitor Goti Elordi is a Professor in the Department of Mechanics, Design and Industrial Management at the Faculty of Engineering, University of Deusto. His extensive research portfolio spans industrial engineering, maintenance optimization, Industry 4.0 implementation, and sustainable manufacturing practices. He leads multiple EU-funded and industry-collaborative research projects focused on digital transformation in manufacturing sectors. His research interests center around the intersection of industrial engineering and digital transformation, with particular focus on maintenance optimization using evolutionary algorithms, predictive maintenance systems, and the development of future skills requirements for evolving industrial sectors. His work bridges theoretical research with practical industrial applications, particularly in the Basque manufacturing ecosystem. Analysis of his recent publications reveals a strong trend toward interdisciplinary research addressing Industry 4.0 challenges across multiple sectors. His work consistently focuses on practical applications of data science and AI in industrial contexts, with growing emphasis on sustainability and circular economy principles. A distinctive pattern in his research is the development of competency frameworks to identify future skills requirements across various industrial sectors including steelmaking, renewable energy, and supply chain logistics. Professor Goti Elordi leads multiple significant research projects including SUSTASKILLS (2023-2025) focused on industrial symbiosis skills, REshaping Supply CHAins for Positive social impact (2022-2025), and Real-time acoustic sensorS and artificial Intelligence appLications (2023-2026). His work has secured funding from the European Commission, Basque Government, and major industrial partners including SIDENOR, NEMAK, and ETXE-TAR. He actively supervises student projects, particularly in the area of machinery redesign and additive manufacturing applications. His teaching and research integrate practical industrial experience with academic rigor, emphasizing the development of both technical and transversal skills needed for future industrial challenges.

Ramon Ferrer CanchoView profile

Associate Professor

• Quantitative Linguistics
• Information Theory
• Network Theory
• +3 more

Ramon Ferrer Cancho is an Associate Professor at the Polytechnic University of Catalonia's Department of Computer Science, affiliated with the Barcelona School of Informatics and the LQMC research group (Quantitative, Mathematical, and Computational Linguistics). His work focuses on quantitative linguistics, information theory, and network theory, with applications in language structure, evolution, and animal communication. Education: Doctor in Computer Science, Advanced Studies Diploma in Applied Physics and Simulation. Research Interests: Dependency syntax, linguistic laws (Zipf's law, Menzerath-Altmann law), computational models of language, and cross-disciplinary studies in biology and cognitive science. He leads projects on language optimization, complexity, and data analysis. Recent work includes studies on syntactic dependency distances, ape gesture patterns, and bottlenose dolphin communication parallels. His research bridges computer science, linguistics, and biology, emphasizing universal principles in communication systems. Notable contributions include theoretical frameworks for dependency distance minimization, optimization models of language structure, and empirical analyses of primate vocal sequences.

• Intelligent Control Systems
• Fault and Anomaly Detection
• Virtual and Robust Sensors
• +5 more

Dr. José Luis Calvo Rolle serves as a Professor in the Department of Industrial Engineering at the School of Engineering, Universidade da Coruña (UDC), specializing in Systems Engineering and Automation. His research focuses on intelligent control systems, fault detection, and virtual instrumentation within the Cybernetic Science and Technology Research Group. Teaches across multiple programs including Master's in Industrial Computing and Robotics, Textile Technology, and Occupational Risk Prevention Coordinates thesis supervision across Industrial Engineering and related disciplines His research spans intelligent control systems and optimization, with significant contributions in virtual sensors, fault detection, and AI-driven modeling for industrial applications. Current projects integrate machine learning with industrial processes for naval construction, wastewater treatment, and precision livestock farming, demonstrating cross-disciplinary impact from energy systems to agricultural technology. Recent publications reveal strong trends in applying deep learning to industrial metaverse frameworks, wastewater optimization, and livestock monitoring systems. His work bridges theoretical control engineering with practical implementations in energy management, naval manufacturing, and sustainable agriculture, frequently utilizing dimensionality reduction and one-class classification techniques. Dr. Calvo Rolle actively mentors students through thesis supervision across multiple engineering disciplines and coordinates research projects with diverse funding sources including the European Commission, Spanish National Research Agency, and industrial partners like Navantia and Telefónica. His laboratory work centers on the Cybernetic Science and Technology Research Group, developing testbeds for industrial automation, virtual instrumentation, and AI-driven monitoring systems. Current initiatives include digital twin implementations for naval manufacturing and smart energy management systems.

• RFID Technology
• Wireless Sensor Networks
• Internet of Things
• +5 more

Asier Perallos Ruiz is a Professor in the Faculty of Engineering at the University of Deusto, specializing in the Department of Computing, Electronics and Communication Technologies. His research focuses on RFID technology, wireless sensor networks, and computational intelligence applications with significant contributions to intelligent transport systems and antenna design. Dr. Perallos Ruiz's research interests span multiple domains with a focus on RFID technology , Wireless sensor networks , Internet of Things (IoT) , Computational intelligence , Evolutionary algorithms , and Intelligent transport systems . His work bridges theoretical advancements with practical applications, particularly in transportation systems, healthcare, and industrial automation. His research often involves interdisciplinary collaboration across engineering disciplines. His publication portfolio shows a consistent trend toward improving RFID systems, developing efficient anti-collision protocols, and applying computational intelligence to real-world problems. Recent work has focused on polarization-diversity rotation sensing, customizable RFID platforms, and the integration of RFID with IoT applications. His research demonstrates a progression from foundational RFID technology to more complex system integration and application-specific solutions. Dr. Perallos Ruiz has supervised several graduate students including Muralter Florian (2021), Arjona Aguilera Laura (2018), Cmiljanic Nikola (2018), Lopez Garcia Pedro (2016), and Moreno Emborujo Asier (2016). His research has been supported by various projects focusing on RFID technology, intelligent transportation systems, and wireless communication applications. He leads research teams focused on RFID systems development, wireless sensor networks, and computational intelligence applications. Current work appears to be advancing RFID sensing capabilities, energy-efficient protocols, and system integration for practical applications in transportation and industry.

• Bioinformatics
• Algorithms
• Computational Biology
• +5 more

Gabriel Alejandro Valiente Feruglio is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB) and the Department of Computer Science. He is a member of the ALBCOM research group, focusing on Algorithms, Bioinformatics, Complexity, and Formal Methods. His work integrates theoretical computer science with applications in computational biology, including phylogenetic analysis, graph algorithms, and metagenomics. Valiente’s research emphasizes the development of algorithms for biological networks, phylogenetic tree and network comparison, and efficient graph representation. He has contributed to tools like TANGO for taxonomic assignment in metagenomics and AligNet for protein-protein interaction network alignment. His publications span over 140 works in journals like BMC Bioinformatics, IEEE-ACM Transactions on Computational Biology, and Bioinformatics. He leads and collaborates in competitive research projects funded by institutions like the Catalan government, focusing on bioinformatics, computational biology, and algorithmic methods. His research also extends to LaTeX typesetting for scientific documents and the structural analysis of scientific collaborations in graph transformations.

• 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).

• 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.

Matthieu DefranceView profile

Associate Professor

• Gene Regulation
• Epigenetics
• Epigenomics
• +6 more

Matthieu Defrance is an Associate Professor in the Computer Science Department at Université Libre de Bruxelles (ULB), Belgium, a position he has held since October 2016. Prior to this, he worked as a PostDoc at ULB's Laboratory of Cancer Epigenetics from May 2010 to September 2016. Dr. Defrance's research bridges computer science with biological sciences, focusing on computational methods for analyzing complex biological data. His work spans epigenetics, genomics, and algorithm development for biological applications. His primary research interests include: Gene Regulation and Chromatin Biology Epigenetics and Epigenomics DNA Methylation analysis Computational Statistics for biological data Algorithm Development for genomic applications Next Generation Sequencing data analysis Dr. Defrance's publication record demonstrates a strong interdisciplinary approach, with recent work focusing on improving DNA methylation analysis techniques, developing novel bioinformatics tools like RedRibbon for gene expression signature comparison, and applying these methods to understand diseases like diabetes and cancer. His research shows consistent innovation in computational biology, with publications spanning from method development to biological applications across diverse fields including diabetes genetics, cancer epigenetics, and evolutionary adaptations in desert ants. With 61 publications and nearly 5,000 citations, Dr. Defrance has established himself as a significant contributor to computational biology and epigenetics research.