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Human trafficking is a global phenomenon referring to the illegal forced transportation of individuals between international or national borders to be exploited for labor or commercial sex. Our objective is to develop a systems dynamic model of human trafficker and victims interactions using social media and real victims/traffickers profiles (personas) with the help of big data analytics tools and methods. The developed systems dynamic models will help policy maker to devise both proactive and reactive interventions and policies against human trafficking activities through social media.
Collaborators: Greg Placencia and Shokoufeh Mirzaei (Industrial and Manufacturing Engineering, Cal Poly Pomona)
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Early diagnosis is crucial for proper treatment of Autism Spectrum Disorder (ASD). Although progress has been made to accurately diagnose ASD, it is far from ideal. It often requires various tests like behavioral assessments, observations from caretakers over a period of time to correctly determine the existence of Autism. Even with this tedious testing often times individuals are misdiagnosed. However, there remains promise in the development of accurate detection using various modalities of Biomedical Images, EEG, and Eye movement.
So, for our research, we plan to investigate the relationship between Eye movements (oculomotor plant), EEG, and ASD using Machine Learning. We aim to identify unique patterns in oculomotor plant or EEG or the combination in the ASD population. These patterns will enable us to develop intelligent systems for accurate early diagnosis.
Collaborators: www.chronaly.com
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Detailed knowledge about a user's interests is beneficial in web search, advertising, and personalized information delivery as well as in content targeting. The goal of personalized information delivery is to support users by identifying documents or the parts of a document that best match user’s interests during an open-ended information searching task. Such personalization can result in a more efficient use of the user’s time, e.g. that their time is spent on the most relevant documents. Understanding how the human eye movements and underlying non-visible anatomical structure and its characteristics (oculomotor plant) change over the course of user engagement with information resources will be valuable in the pursuit of developing more accurate user interest models.
This project will provide foundation for an extensive framework expanding the use of eye movements as a source for user interest modeling. This work constructs a model of human oculomotor plant features during user’s interactions with the goal of better interpreting user gaze data related to resource content. The project will explore the anatomical reasoning behind incorporating additional gaze features, the integration of the additional features into an existing interest modeling architecture, and a plan for assessing the impact of the addition of the features.