Miguel Alves Gomes

Miguel Alves Gomes is a Scientific Researcher and doctoral student at the Institute for Technologies and Management of Digital Transformation (TMDT) at the University of Wuppertal, where he has been actively contributing since December 2020. He holds an M.Sc. in Computer Science with a focus on Data Analytics and previously studied Information Technology with a focus on Information Science, both at the University of Wuppertal.

• Bachelor of Science in Information Technology (focus: Information Science), University of Wuppertal
• Master of Science in Computer Science (focus: Data Analytics), University of Wuppertal

His research lies at the intersection of machine learning and data mining, particularly in personalized recommender systems. He investigates the integration of supervised and unsupervised learning methods, with a growing emphasis on Transformer-based architectures and explainable AI. His work aims to enhance model interpretability and adaptability, especially in dynamic environments requiring continuous learning.

The 2024 publication highlights a clear trend toward advancing embedding techniques for lifelong learning, focusing on algorithmic strategies for dynamically extending model representations. This reflects a deep engagement with cutting-edge challenges in AI sustainability and model evolution.

No scientific awards were mentioned in the provided text.

Miguel Alves Gomes has served as a student assistant at TMDT during his studies and is currently a research associate and doctoral candidate. There is no mention of grant leadership or student advisement in the available information.

He is affiliated with the Institute for Technologies and Management of Digital Transformation (TMDT), which serves as his primary research unit at the University of Wuppertal.