Abstract: Knowledge discovery on large-scale complex data is challenging. Not only do we need to devise efficient methods to extract insights, we must also enable users to interpret, trust and incorporate their domain knowledge into the automated results. How do we combine data mining, machine learning, and interactive visualization to address this problem? In this talk, I will review related research projects in the context of exploring, summarizing, and modelling temporal event sequence data for various application domains. Through our investigation, we identify symbiotic relationships between automated algorithms and visualizations: data mining and machine learning techniques suggest salient patterns and predictions to visualize; visualizations, on the other hand, can support data analysis across multiple levels of granularity, uncover potential limitations in automated approaches, and inspire new algorithms and techniques. Reflecting upon past experiences, I will discuss challenges and opportunities in tightly coupling automated algorithms with interactive visual interfaces for effective knowledge discovery.
Bio: Dr. Zhicheng Liu is an assistant professor in the department of computer science at University of Maryland. His research focuses on scalable methods to represent and interact with complex data, as well as techniques and systems to support the design and authoring of expressive data visualizations. Before joining UMD, he worked at Adobe Research as a research scientist and Stanford University as a postdoc fellow. He obtained his PhD at Georgia Tech. His work has been recognized with a Test-of-Time award at IEEE VIS, and multiple Best Paper Awards and Honorable Mentions at ACM CHI and IEEE VIS.