MING SHAO, assistant professor of computer and information science at the University of Massachusetts Dartmouth, recently received a $498,970 National Science Foundation CAREER award for his project “CAREER: Enabling Continual Multi-view Representation Learning: An Adversarial Perspective.” The CAREER Program is the foundation’s most prestigious award in support of early-career faculty who have the potential to serve as academic role models in research and education.
What does it mean to you to receive this award from the foundation? The CAREER Program is the National Science Foundation’s most prestigious award in support of early-career faculty who have the potential to serve as academic role models in research and education. This award is a significant milestone in my early career, which will help build a firm foundation for a lifetime of contributions to research, education and their integration.
What will the grant funding cover for the project? This five-year award for $498,970 will support my research in representation learning, student mentorship and educational activities at UMass Dartmouth, and promote research collaborations and civic engagement in the south coast of Massachusetts.
You say in the era of big data, representation learning techniques are confronted with new challenges. What are those challenges? Representation learning techniques attempt to extract and abstract key information (i.e., the features) from raw data to be used in analyses. As a critical step in machine-learning systems, representation learning is meant to be robust in its capacity. In the era of big data, representation learning techniques are confronted with new challenges. Massive data collected from different sensors (e.g., the multi-view camera system) or presented in different modalities (e.g., audiovisual text) have overloaded existing representation learning techniques. In addition, streaming data received from the internet and sensitive data accumulated over time, such as personal albums and electronic health records, require the established representation learning model to adapt and account for incoming data.
What will your project do to help address those challenges? This project will develop a robust continual representation learning model to address these challenges. It seeks to advance the fundamental understanding of continual multi-view robust representation learning by integrating machine intelligence and human knowledge in AI [artificial intelligence]-enabled security contexts. In real-world scenarios where data access is restricted (e.g., sensitive data) or the processing power of devices is limited (e.g., edge and mobile devices), stakeholders will benefit from the adaptive representation learning techniques to enable continual data analyses.
