Javier de Lope Homepage
Associate Professor in the Department of Artificial Intelligence at the Universidad Politécnica de Madrid. He currently focuses his research on speech emotion recognition. Previously, he has led and contributed to R&D projects covering multi-robot systems and reconfigurable robotics, computational neuroethology, unmanned aerial vehicles, humanoid robots, vision-based autonomous driving, and industrial robotics. Find a brief description of some research projects, a list of publications, and a list of recently supervised master’s theses.
Autonomous Robots (European MSc in Artificial Intelligence). This course delves into the fundamental principles of mobile robot autonomous navigation. Students will explore state-of-the-art hybrid artificial intelligence methods for robot control. A practical mini-project requires students to develop maps of unknown environments for subsequent path planning.
Robotics (European MSc in Informatics Engineering). This project-based course focuses on applying hybrid artificial intelligence methods in robotics. Students begin by exploring key areas of robotics, encompassing both industrial and autonomous domains. Students subsequently conduct a state-of-the-art review and define a project, which they execute over the semester with regular weekly progress discussions. The final deliverables include a conference paper and a formal presentation of their work.
Robotics (Bachelor Degree in Data Science and Artificial Inteligence). This introductory course explores robotics from the perspective of data science and artificial intelligence. Students will learn the fundamentals of industrial and autonomous robotics, detailing commonly used sensors and actuators. The course also reviews and compares various mobile robot control methods, and covers imaging and computer vision techniques with direct applications in robotics.
F. Portal, J. de Lope and M. Graña. A performance benchmarking review of transformers for Speaker-Independent Speech Emotion Recognition. International Journal of Neural Systems, 2025. DOI
J. de Lope and M. Graña. An ongoing review of speech emotion recognition. Neurocomputing, 528:1-11, 2023. DOI
J. de Lope and M. Graña. A hybrid time-distributed deep neural architecture for speech emotion recognition. International Journal of Neural Systems, 32(6):2250024, 2022. DOI
J. de Lope and M. Graña. Deep transfer learning-based gaze tracking for behavioral activity recognition. Neurocomputing, 500:518-527, 2022. DOI
J.A. Nicolás, J. de Lope, and M. Graña. Data augmentation techniques for speech emotion recognition and deep learning. In J.M. Ferrández, J.R. Álvarez Sánchez, F. de la Paz, and H. Adeli, editors, Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence, LNCS 13259, pages 319-326. Springer Nature, Cham, 2022. DOI
J. de Lope, E. Hernández, V. Vargas and M. Graña. Speech emotion recognition by conventional machine learning and deep learning. In H. Sanjurjo, I. Pastor, P. García, H. Quintián, and E. Corchado, editors, Hybrid Artificial Intelligent Systems, LNAI 12886, pages 319-330. Springer Nature, Cham, 2021. DOI