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Andrea Cavallaro is a prominent researcher and professor focusing on machine learning and audio-visual sensing to enhance the interaction capabilities of autonomous systems. His research aims to create next-generation models for machine perception that effectively utilize sensory data for safe operations in varied environments. Andrea has significantly contributed to the academic community with over 350 published scientific papers, including a monograph on video tracking and several edited volumes that delve into intelligent multimedia and content analysis. He has received numerous accolades including prestigious fellowships and awards for his contributions to engineering and teaching. Andrea's expertise extends into various application areas, including digital education and clinical decision support, and he has served in multiple leadership positions within prominent technical committees of the IEEE. He is also involved in several funded research projects that explore the alignment of machine learning models with societal values, showcasing practical implications in domains such as education and mental health. Throughout his career, Andrea has demonstrated a commitment to fostering safer online environments through educational initiatives and course projects that explore ethical considerations in deep learning. His significant experience in guiding PhD students underscores his dedication to mentorship and academic development.
Standard requirements for Engineering and Basic Science Master's programs. Architecture requires an additional portfolio.