Research Interest: study human behavior on the Web via an interdisciplinary approach of Computer and Psychological Sciences, for addressing the fundamental problem of information overload on a human mind and augment human capabilities at workplace with human-AI collaboration. Specifically, I model individual behavior (e.g., intent, comprehension, and engagement) in offline and online social environments using semantic computing and applied machine learning methods, in order to help reduce the information overload in supporting individual and group decision making. This research is employed in the design of systems for social good in future smart communities for a variety of domains, including natural crises (e.g., hurricanes), societal crises (e.g., migration, gender violence), and human crises (e.g., terrorism, cyber attacks).
My technical interest is to fuse top-down and bottom-up data mining approaches for developing explainable models with prior knowledge (domain semantics and cognitive/social theories), which helps in both explaining and predicting individual or group behavior from unstructured social, open Web, and IoT data at scale.
Check more details and relevant projects of Humanitarian, Semantics & Informatics Lab (Human_Info_Lab) research group.
We are grateful to U.S. National Science Foundation (NSF), Office of Navel Research (ONR), and Research Council of Norway for supporting our research. Check grants at the Human_Info_Lab page.