My research primarily focuses on the development of novel deep learning architectures and algorithms. While working with various types of data excites me, my main interest lies in image processing. 

I am deeply engaged in interdisciplinary machine learning research. In my current position at KU Leuven, I have been working in the area of computational aesthetics - an interdisciplinary field of research dedicated to understanding image aesthetics. My work involves developing deep learning approaches to gain a deeper understanding of aesthetic preferences in images. Utilizing deep neural networks, I aim to uncover the insights into the roles of low-, mid-, and high level features in determining aesthetic preferences. Furthermore,  I am investigating how neural networks perceive the world, aiming to enhance their robustness.

Another aspect of my research involves the exploration of attention mechanisms in neural networks, inspired by the human visual attention system. I have been seeking new approaches to improve existing attention mechanisms and reduce their computational complexities.