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Bailey's primary research interest lies in the application of feature and metric learning on problems where data is scarce. This leads to considerations such as how to leverage data from other tasks (data aggregation and transfer learning), embed existing knowledge into the model structure itself (model design), and synthesize new data for cross-domain adaption. His projects have covered areas of shoeprint forensics, precipitation prediction and forecasting from satellite imagery, and 3D dense pixel tracking from RGB videos.
Recently, Bailey's focus has shifted to applications in mental health. He is interested in questions such as: Can we build a system that recognizes a person's non-verbal cues (facial and body language) to explain their mental state? If so, how might AI systems be used to help individuals calibrate self-awareness of their mental and physiological states? When tying daily or major life events to these mental and physiological states over time, can we map out behavioral patterns that help individuals acknowledge and confront their underlying causes? Bailey investigates these research questions primarily using visual data but is exploring other modalities such as biometrics for multi-modal fusion methods.
Bailey completed a Ph.D. in Computer Science at the University of California, Irvine in 2018. He has 10+ years of combined experience in professional and academic research environments. Bailey likes to take complex concepts, ideas, and data and make them understandable using simple language and visualizations.
Learn more at: https://baileykong.com
Contact: bailey[dot]kong/at-sign/ronininstitute(dot)org
