جستجوی استاد راهنمای دانشگاهی

Ana Muñoz Sánchez is an Associate Professor in the Department of Mechanical Engineering at University Carlos III of Madrid, where she conducts research in composite materials, additive manufacturing, and manufacturing processes. Her work bridges theoretical modeling with practical applications, particularly in drilling processes, ballistic protection, and engineering education. Her research focuses on several key areas within mechanical engineering and materials science: Composite materials machining, particularly carbon fiber reinforced polymers (CFRP) Tool wear analysis and optimization of drilling parameters Numerical modeling of manufacturing processes Additive manufacturing applications in medical and educational contexts Ballistic protection systems Engineering education tools and methodologies Dr. Muñoz Sánchez's publication record demonstrates consistent contributions to both fundamental manufacturing research and practical educational applications. Her recent work shows a strong emphasis on improving manufacturing processes for composite materials while simultaneously developing innovative educational tools, including specialized resources for visually impaired students. Her research combines computational methods with experimental validation to address complex engineering challenges. She has secured funding from multiple Spanish research agencies for projects including "DIGITDRILL - Digitalization of industrial drilling process" and "DISEÑO AVANZADO, FABRICACIÓN Y ENSAYO DE PROTECCIONES BALÍSTICAS DE CABEZA (PROTECT_BAL)". Her work has practical applications in aerospace, medical simulation, and inclusive education.

• Mechanical Engineering
• Composite Materials
• Additive Manufacturing
• +۴ مورد دیگر

Simo Alami is a Ph.D. candidate and Tutor at Ecole Polytechnique, affiliated with the LIX computer science laboratory under the supervision of Professor Jesse Read. He teaches tutorials for the Advanced Machine Learning and Autonomous Agents course (Computer Science, Ingénieur 3A/Master1) since 2020, focusing on practical implementations of machine learning algorithms and autonomous systems. His academic background includes undergraduate and graduate studies in Mathematics and Computer Science at Université Pierre et Marie Curie and Ecole CentraleSupélec. Ph.D. Candidate, Ecole Polytechnique (2020-Present) Master's Studies, Mathematics and Computer Science, Université Pierre et Marie Curie and Ecole CentraleSupélec Alami's research centers on developing algorithms that learn from experience through self-learned metrics, with core expertise in Deep Learning, Inverse Reinforcement Learning, and Metric Learning. His work targets robotic applications where agents must perform novel tasks by understanding logical concepts, while also extending to energy management systems through non-invasive load monitoring. He explores how meta-learning frameworks can enable transferable reward structures across domains. His publication record reveals a strong trajectory in distributional reinforcement learning and inverse reinforcement learning using generative models, with significant contributions to metric learning for clustering and energy disaggregation. The consistent focus on transferable methodologies across robotics and smart grid applications demonstrates his interdisciplinary approach to solving real-world problems through advanced machine learning techniques. Scientific recognition includes: Nvidia Grant awarded in 2022 for research on Meta-Inverse Reinforcement Learning 3rd Prize at Hi Paris Hackathon (2021) for a reinforcement learning-based smart grid energy management solution While not supervising graduate students as a Ph.D. candidate, Alami has secured competitive external funding including the Nvidia research grant. His industry collaboration with Accenta during a visiting researcher position resulted in ECML 2022 publications on non-invasive load monitoring, highlighting his ability to bridge academic research with practical engineering applications. As an active member of the LIX laboratory at Ecole Polytechnique, Alami participates in cutting-edge computer science research while maintaining external collaborations. His visiting researcher position at Accenta demonstrates engagement with industry partners, and his organization of the IRT SystemX Electromobilité et territoires webinar in 2021 reflects leadership in knowledge dissemination within the energy and mobility research community.

• Deep Learning
• Reinforcement Learning
• Inverse Reinforcement Learning
• +۴ مورد دیگر

Riccardo Marin is a Postdoctoral Researcher at the Technical University of Munich (TUM) within the Computer Vision Group. His research focuses on Spectral Shape Analysis, Geometric Deep Learning, and 3D Human Modeling. He holds affiliations with ELLIS Society and is an alumni of the Alexander von Humboldt Foundation and Marie Skłodowska-Curie Actions. Marin's work bridges computational geometry and deep learning, addressing challenges in 3D human registration (NICP), avatar creation (Human-3Diffusion), and neural surface modeling (NSF). He has contributed to open-source tools for spectral shape analysis and maintains active GitHub repositories showcasing his research implementations. His research interests span 3D reconstruction, neural fields, and implicit representations, with applications in virtual humans and human-object interaction analysis. Marin's recent work emphasizes scalable registration pipelines and diffusion models for 3D generation. Awards: ELLIS Member, Alexander von Humboldt Alumni, Marie Skłodowska-Curie Fellow Key Contributions: NICP, Human-3Diffusion, NSF framework Education: PhD background (implied from postdoc status), involvement in a Spectral Shape Analysis course at University of Verona

• Spectral Shape Analysis
• 3D Computer Vision
• Geometric Deep Learning
• +۴ مورد دیگر

Professor Pedro Diez is a faculty member at the Universitat Politècnica de Catalunya (UPC), specializing in computational mechanics and reduced-order modeling. He holds a BSc and PhD in Civil Engineering from UPC (1989 and 1996, respectively). His research focuses on error estimation, extended finite element methods (X-FEM), and applications in automotive and geophysical systems. He co-organizes the International Conference in Adaptive Modeling and Simulation since 2003, with the next edition in 2023. Key research areas include: Reduced Order Models (POD/PGD) Goal-oriented adaptivity in numerical simulations Geophysical inversion and thermal modeling Automotive structural dynamics and NVH analysis Sustainable materials for civil engineering His work bridges computational methods with real-world applications, such as earth dam monitoring, hydraulic fracturing modeling, and biomechanical simulations. Recent publications emphasize real-time risk analysis, parametric solutions for nonlinear systems, and inverse problem techniques in geophysics. His contributions to numerical methods have advanced high-fidelity, low-cost simulations across engineering disciplines. Publications span over 30 years, with a focus on: Efficient parametric solutions for transient coupled systems Nonintrusive reduced basis methods Thermo-hydro-mechanical modeling of geological media Advances in error estimation and model order reduction underpin much of his work, enabling applications in structural health monitoring, crashworthiness analysis, and geophysical exploration.

• Reduced Order Models
• Error Estimation and Adaptivity
• Extended Finite Element Methods (X-FEM)
• +۴ مورد دیگر

Dr. David Delgado Gómez is a Full Professor in the Department of Statistics at Universidad Carlos III de Madrid , where he serves as Director of the Doctoral Program in Statistics for Data Science . He leads the Statistical Data Science Group (SDS) and is affiliated with the UC3M-Santander Big Data Institute . Research Focus: His work bridges Bayesian inference , Monte Carlo methods , and mental health diagnostics , particularly in ADHD assessment and suicide risk prediction using machine learning and virtual reality . He develops computerized adaptive tests for behavioral analysis and contributes to gamified learning environments for statistical education. Recent Publications highlight his methodological innovations in importance sampling (pyLAIS package), thermodynamic integration , and ADHD diagnostic tools using motion tracking and game-based data. His research spans computational statistics , psychiatric epidemiology , and public health applications . Projects include: GIITN (2023-2025) - Interdisciplinary neurodevelopmental disorders research Paideia (2022-2025) - Digital tools for neurodevelopmental disorder diagnosis Desarrollo de un sistema de diagnóstico y tratamiento del TDAH (2021-2024) - Data science applications Higia (2019-2022) - Pattern recognition for ADHD diagnosis Contact: david.delgado@uc3m.es | ddelgado@est-econ.uc3m.es

• Bayesian Inference
• Monte Carlo Methods
• Machine Learning
• +۷ مورد دیگر

Alejandro Beacco Porres is a Researcher at the Universitat Politècnica de Catalunya (UPC) in the Department of Computer Science, affiliated with the Barcelona School of Informatics (FIB). He is a core member of the ViRVIG research group focusing on Virtual Reality, Real-Time Graphics, and Crowd Simulation. His work bridges computer graphics, human-computer interaction, and clinical applications of VR. Research interests include real-time crowd rendering techniques, embodied VR experiences, and applications in mental health. Notable contributions include QuickVR toolkit development, studies on virtual body ownership effects, and VR-based therapy for anxiety and psychosis. He has collaborated extensively with Prof. Mel Slater and international teams on projects funded by Horizon Europe and Spanish National Research Programs. Publications span high-impact journals like Scientific Reports, IEEE Transactions, and The Lancet Psychiatry. His work explores both technical advancements (e.g., avatar reconstruction from images) and socio-ethical implications of VR. Active in both academia and industry, he has led projects addressing cultural heritage visualization and medical training simulations. Education includes a PhD in Computer Science from UPC (2014), with dissertation on real-time crowd simulation. He maintains strong ties with the European Association for Computer Graphics (Eurographics) through conference organizing and peer review activities.

• Virtual Reality (VR)
• Computer Graphics
• Human-Computer Interaction
• +۳ مورد دیگر

Juan Carlos Gonzalez Victores is an Associate Professor at the Department of Systems Engineering and Automation, Universidad Carlos III de Madrid (UC3M), actively leading research in the Robotics Lab. His work spans multiple disciplines including Computer Science, Medicine, and Robotics and Industrial Informatics. Research interests focus on: Human-Robot Interaction with applications in neurorehabilitation and accessibility Autonomous Systems for smart cities and underground infrastructure inspection Deep Learning and Reinforcement Learning for robotic manipulation Medical Robotics with emphasis on surgical and diagnostic applications Recent publications highlight trends in: Garment-agnostic manipulation techniques Neurorehabilitation systems using synthetic data Human-like sketching via Deep Q-Learning Safety analysis of AI-human organizational interfaces Contact: juancarlos.gonzalez@uc3m.es | jcgvicto@ing.uc3m.es

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +۶ مورد دیگر

Lluïsa Jordi Nebot is a Lecturer in the Department of Mechanical Engineering at the Polytechnic University of Catalonia (UPC) , attached to the Barcelona School of Industrial Engineering (ETSEIB) . She teaches core subjects such as Theory of Machines and Mechanisms and Mechanical Vibrations across a variety of undergraduate and master’s programmes and has supervised over 80 student theses and projects since 2006. Education and Programmes. The curricula she contributes to include: Master’s Degree in Statistics and Operations Research (2006) Master’s Degree in Industrial Engineering (2014) Degree in Engineering Physics (2011) Degree in Chemical Engineering (2010) Erasmus Mundus Master’s in Mechanical Engineering (2006 & 2009) Master’s Degree in Teacher Training for Secondary and High-School Education (2009) Degree in Industrial Technologies and Economic Analysis (2018) Research Focus. Her research spans kinematics & dynamics of mechanisms , mechanical vibrations , biomechanical analysis using smartphones , and educational technology —particularly augmented reality and AI-enhanced learning in mechanical engineering. She actively integrates sustainable development goals into engineering curricula and participates in the CDEI_DM research group on industrial equipment design and machine dynamics. Recent Work Trends. Over the past three years her scholarly output has concentrated on: AR/VR tools for teaching mechanism theory ChatGPT and AI applications in engineering classrooms Low-cost biomechanical analysis with mobile devices Sustainable engineering education initiatives These themes are reflected in both peer-reviewed articles and innovative teaching materials she has developed. Scientific Awards & Recognition. No specific prizes, fellowships, or medals are explicitly listed in the provided text. Supervision & Grants. She has directed 40 master’s theses and 15 bachelor’s theses to date, working with multidisciplinary teams of students on projects ranging from climbing-robot mechanisms to AI-based secondary-school teaching tools. Funding sources are not detailed, but her affiliation with CDEI_DM implies participation in competitive research and educational innovation grants. Laboratories & Teams. She collaborates with the Centre de Disseny d’Equips Industrials – Dinàmica de Màquines (CDEI_DM) research group and maintains close ties with colleagues across ETSEIB, the Barcelona School of Informatics (FIB), and the Manresa School of Engineering (EPSEM).

• Theory of Machines and Mechanisms
• Mechanical Vibrations
• Kinematic and Dynamic Analysis of Mechanisms
• +۵ مورد دیگر