Crowdsourcing-based Hybrid Knowledge Discovery: Application to Biomedical Data
|Interdisciplinary Areas:||Internet of Things and Cyber Physical Systems, Data/Information/Computation, Human-Machine/Computer Interaction, Human Factors, Human-Centered Design
Human agents have remarkable capability to learn a large variety of concepts, often with few examples, whereas current data-mining algorithms require thousands of data points and struggle with problems such as ambiguity, validity, overfitting etc. However, well-trained human agents may be expensive. On the other hand, initially less competent human agents may have the desire to improve themselves on acquiring sufficient knowledge and drawing subsequent inferences. We will investigate the consensus problem of multi-agent hybrid systems with diverse knowledge discovery capacities under a crowdsourcing framework. Combining numerical and symbolic data mining methods remains challenging. Additionally, even though a crowdsourcing framework is easily achievable from an information technology infrastructure point of view, scaling up the networked control system for hybrid knowledge discovery remains challenging. For the above two challenges, we will explore the combination of numerical and symbolic data mining and the use of domain knowledge for improving the performances. We will also study the operations management aspect for the scaling-up that intends to balance efficiency of knowledge discovery and spending on crowdsourcing the necessary tasks. Moreover, we will use organizational psychology to study effective ways of improving workforce commitment. We will conduct proof-of-concept studies in healthcare and biology.
August 1, 2019
We are seeking a highly qualified individual with expertise in data science applied to healthcare, and in particular, to health-related big data integration. Areas of emphasis include deep learning, explainable artificial intelligence, causal inference, predictive analytics, transportability of causal and statistical relationships, as well as multi-agent control. Research experience in the area psychometrics is high appreciated.
Nan Kong, firstname.lastname@example.org, Weldon School of Biomedical Engineering, https://engineering.purdue.edu/BASO
Daisuke Kihara, email@example.com, Department of Biological Sciences; Department of Computer Science, http://kiharalab.org/
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