Javier Lamar León
Researcher (Laboratório BigData@UE (IIFA))
Fixed-term employment contract
VISTA LAB - Video, Image, Speech and Text Analysis Laboratory (Collaborating Member)
Her research focuses on Matching Learning, Computer Vision, Images Processing, Deep Learning, Video Surveillance, Gait Recognition, Natural language processing (NLP), Topological Data Analysis and Knowledge Graph (KG).
He has used the following Language, tools of programming and library:
Visual Studio C++, Python, Matlab,
Pytorch, Tensorflow, Keras, Caffe (C++ lib),
OpenCV ( Image Processing C++ lib),
Boost (C++ lib),
CGAL & GUDHI (TDA C++ lib),
ROS Robot Operating System, etc.
He has used the following Language, tools of programming and library:
Visual Studio C++, Python, Matlab,
Pytorch, Tensorflow, Keras, Caffe (C++ lib),
OpenCV ( Image Processing C++ lib),
Boost (C++ lib),
CGAL & GUDHI (TDA C++ lib),
ROS Robot Operating System, etc.
Javier Lamar León has a strong interest in Artificial Intelligence, with a focus on the intersection of Machine Learning, Natural Language Processing (NLP), Computer Vision, and Topological Data Analysis (TDA). His research includes the development and optimization of Large Language Models (LLMs), particularly through the integration of Knowledge Graphs to enhance question-answering systems in specialized domains. Additionally, he explores the application of TDA in Deep Learning, leveraging topological methods to improve neural network architectures. Recently, he has investigated efficient fine-tuning techniques for LLMs, such as LoRA (Low-Rank Adaptation), to tailor models for domain-specific applications, improving adaptability and performance.
Javier Lamar León also specializes in medical image processing, leveraging AI techniques to improve diagnostic precision and image interpretation. He is actively engaged in Simultaneous Localization and Mapping (SLAM) for robotic navigation, focusing on real-time environmental mapping and autonomous positioning. His expertise extends to anomaly detection, where he develops machine learning models to identify irregular patterns in complex datasets. Additionally, he has significant experience in gait recognition, utilizing computer vision and Topological Data Analysis (TDA) for biometric identification and movement analysis. Furthermore, as the administrator of the GPU Cluster Vision at the Big Data Laboratory, he manages high-performance computing resources to optimize deep learning and AI model training.
Member of the Algoritmi Research Center At the University of Évora, Member of the Cuban Association of Pattern Recognition, Member of the International Association in Pattern Recognition (IAPR), Member of the CIMA-GROUP, Combinatorial Image Analysis Research Group. Department of Applied Math, School of Computer Engineering University of Seville, Spain.
Academic Degree of Auxiliary Researcher
COMPETE2030-FEDER02238400
/
Portugal 2030
,Portugal 2030
acrónimo: FAR4
ongoing
ALT20-05-3559-FSE-000074
/
Portugal 2020
acrónimo: RH.Vita
finished in 30/06/2023
* For more publications, perform a search by author name on Digital Repository of Scientific Publications of UÉ.
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