Kaizhi Tang

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The recent development of social media (e.g., Twitter, Facebook, blogs, etc.) provides an unprecedented opportunity to study human social cultural behaviors. These data sources provide rich structured data (e.g., XML, relational tables, and categorical data) as well as unstructured data (e.g., texts). A significant challenge is to summarize and navigate(More)
BACKGROUND Predictive modeling of the biological effects of nanomaterials is critical for industry and policymakers to assess the potential hazards resulting from the application of engineered nanomaterials. METHODS We generated an experimental dataset on the toxic effects experienced by embryonic zebrafish due to exposure to nanomaterials. Several(More)
Twitter is a microblogging website that has been useful as a source for human social behavioral analysis, such as political sentiment analysis, user influence, and spread of news. In this paper, we discuss a text cube approach to studying different kinds of human, social and cultural behavior (HSCB) embedded in the Twitter stream. Text cube is a new way to(More)
GM Enterprise Systems Laboratory (GMESL) has developed a stand-alone single user simulation program for evaluating and predicting Order-to-Delivery (OTD) systems and processes. In order for more people to be able to access this simulator, to share the simulation results, and to analyze simulation collaboratively, we have designed, developed and implemented(More)
An Integrated Coordination Problem involves solving multiple related subproblems that collectively satisfy the requirements of a user, including subproblems that depend on the user's participation to solve. Fundamental challenges in solving such a problem include defining mechanisms to solve the individual subproblems. formulating the information and(More)
Data mining meta-optimization aims to find an optimal data mining model which has the best performance (e.g., highest prediction accuracy) for a specific dataset. The optimization process usually involves evaluating a series of configurations of parameter values for many algorithms, which can be very time-consuming. We propose an agent-based framework to(More)
We develop an evolutionary method that combines reinforcement learning and fictitious playing to seek equilibrium solution for a multi-agent and multi-stage game in the context of supply chain procurement. The game is designed to model task delegation among a group of self-interested transportation companies which serve logistic shipment. The game involves(More)