Title:
Empowering Multiple Awareness Levels: Data-driven Decision-making and System Informatics for Computationally Aware Systems
Abstract:
In recent years, our endeavors to combat pandemics, cybersecurity, and wildfire, have highlighted the significance of computational awareness that empowers proactive decision-making and action-taking to mitigate potential risks and minimize the impact of adverse events. Computationally aware systems acquire, process, and distribute information related to what happened, what is happening, and what is going to happen in an environment to detect potential changes and anomalies. Key challenges across different awareness levels hinder wider and more intelligent application of computationally aware systems, including: (i) data variability, uncertainty, and inconsistent granularities; (ii) limited system resources for sensing, processing, and decision-making, and (iii) the complicated interactions and ever-changing dynamics in the environment of interest.In this presentation, I will focus on three directions of works on the sensory-level, representation-level, and cross-level awareness: (i) a weakly supervised learning method to tackle data granularity challenges; (ii) an on-demand learning method to address label-induced constraints for classification problems under resource constraints; (iii) pathwise sampling methods for online data-driven routing and detection. The theoretical, numerical, and experimental investigations demonstrate the potential for significant improvements in the accuracy, timeliness, and autonomy of the detection of abnormal events, thereby mitigating potential catastrophic consequences and adverse outcomes.
Biograph:
Dr. Xiaochen Xian is currently an assistant professor in the Department of Industrial and Systems Engineering at the University of Florida. She received her B.S. degree in Mathematics and Applied Mathematics from Zhejiang University, China in 2014, and the M.S. degree in Statistics, and the Ph.D. degree in Industrial and Systems Engineering from the University of Wisconsin-Madison in 2017 and 2019.Dr. Xian’s research focuses on computationally aware systems with a special interest in novel methodologies in data-driven decision-making and machine learning under constraint to enable theoretically sound and viable analytical tools. Her research has been supported by federal and local agencies including NSF, NIH, the Florida Center for Cybersecurity, and the Florida Space Grant Consortium. She is the recipient of multiple awards, including NIH NIBIB Trailblazer Award, Cottmeyer Family Faculty Fellowships, finalist of INFORMS QSR Best Referred Paper, INFORMS DMDA Workshop Best Paper, and IISE QCRE Best Track Paper, second runner-up of Best Paper Award in IEEE TASE, feature articles in IISE magazine, AIE, and YoungStats. Dr. Xian is an associate editor of IEEE Transactions on Automation Science and Engineering and IEEE International Conference on Automation Science and Engineering.