Big Signal Processing for Multi-Aspect Data Mining

What does a social graph between people who call each other look like? How does it differ from one where people instantmessage or e-mail each other? Social interactions, along with many other real-word processes and phenomena, have different aspects, such as the means of communication. In the above example, the activity of people calling each other will likely differ from the activity of people instant-messaging each other. Nevertheless, each aspect of the interaction is a signature of the same underlying social phenomenon: formation of social ties and communities. Taking into account all aspects of social interaction results in more accurate social models (e.g, communities). The main thesis of my work is that many real-world problems, such as the aforementioned, benefit from jointly modeling and analyzing the multi-aspect data associated with the underlying phenomenon we seek to uncover. In conclusion, I focus on scalable and interpretable algorithms for mining big multi-aspect data by bridging Signal Processing and Data Science for real-world applications.