Our expertise is in the areas of machine learning, data science, and signal processing, with a focus on both theoretical (computational and statistical) foundations and on practical algorithmic solutions, evaluated in a wide range of real-world applications.
Current research topics include:
Dynamic data subspace learning based on Lp-norm projections.
Robust learning with limited, faulty, and adversarially corrupted data.
Tensor methods for multi-way data analysis and processing.
Tensor factorization methods for modeling efficient and explainable neural networks.
Learning from multimodal data and deep learning fusion (recent topic).
Continual learning with increasing parameter tensor ranks (recent topic).
Among multiple other areas, our fundamental research has found applications in remote sensing, computer vision, communication systems, and healthcare technology.