Dr. Xiaoge Zhang delivered a talk on “Causality-Informed Neural Network (CINN)” at Hong Kong Institute for Advanced Study (HKIAS)

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Despite the impressive performance of deep learning in solving long-standing problems (e.g., machine translation, image classification), it has been oftentimes criticized for learning spurious correlations, vulnerable robustness, and poor generalizability. These deficiencies significantly hinder the adoptions of deep learning in high-stakes decision settings, such as healthcare. Although the state-of-the-art literature has made some advances in addressing some issues, the existing attempts to improve robustness, interpretability, and generalizability are unfortunately ad-hoc and unprincipled. In this talk, we develop a generic framework to inject causal knowledge in either structural or qualitative (or quantitative) form (or both) into neural network, where the orientation of each causal relationship is strictly preserved. The proposed causality-informed neural network (CINN) provides a one-stop solution with a great potential to fundamentally solve the existing issues in neural network. More importantly, the incorporation of causal knowledge greatly unlocks the power of neural network in scenarios more than prediction, such as causal effect estimation, interventional query. Several examples are used to demonstrate the appealing features of CINN.

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Prof. Sankaran Mahadevan gave a talk on probabilistic Digital Twins for System Monitoring and Decision-Making

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The digital twin paradigm integrates information obtained from sensor data, system physics models, as well as the operational and inspection/maintenance/repair history of a physical system or process of interest. As more and more data become available, the resulting updated model becomes increasingly accurate in predicting the future behavior of the system or process, and can potentially be used to support several objectives, such as safety, quality, mission planning, operational maneuvers, process control and risk management. This seminar will present recent advances in using Bayesian computational methods that advance the digital twin technology to support all these objectives, based on several types of computation: current state diagnosis, model updating, future state prognosis, and decision-making. All these computations are affected by uncertainty regarding system properties, operational parameters, usage, and environment, as well as uncertainties in data and prediction models. Thus, uncertainty quantification becomes an important need in system diagnosis and prognosis, considering both aleatory and epistemic uncertainty sources. The Bayesian methodology is able to address this need in a comprehensive manner and aggregate the uncertainty from multiple sources. A wide range of use cases such as additive manufacturing, aviation system safety, and power grid operations will be presented.

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