His research focuses on the use of machine learning, particularly Deep Learning methods for image and video analysis. In video analysis, he develops deep learning architectures for egocentric videos that integrate both action appearance and motion within a single model. Development and evaluation are carried out using publicly available data.

Additionally, he has collaborated with the Life Sciences and Veterinary Sciences departments at the university on several machine-learning projects. This interdisciplinary work involved developing and applying advanced machine learning techniques to solve complex problems in biological and veterinary sciences.