Mohammad Dehghanmanshadi

Mohammad Dehghanmanshadi is a deep learning researcher specializing in developing algorithms with minimal human supervision by leveraging self-supervised and semi-supervised learning techniques, with a focus on multi-modality learning. His research addresses challenges such as missing labels and integrating disparate modalities to enhance the robustness of algorithms in real-world applications. Additionally, he explores unsupervised domain adaptation techniques with applications in healthcare and autonomous vehicles, aiming to overcome cross-domain challenges and sensor discrepancies.