Prof. Yan-Fu Li gave a talk on “Recent Research Progresses on Optimal System Reliability Design”

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Optimal system reliability design is an important research field in reliability engineering. Since the 1950s, extensive studies have been conducted on various aspects of this issue. This field remains highly active today due to the need to develop new generations of complex engineering systems, such as 5G telecom networks and high-performance computing clusters, which are expected to be highly reliable to meet the stringent, dynamic, and often real-time quality demands of system operators and end-users. Over the past five years, numerous new researches on optimal system reliability design have been published, addressing the theoretical challenges posed by the new engineering systems. This presentation will systematically review these works with the focus on theoretical advancements, including the models and methods for redundancy allocation problem, redundancy allocation under mixed uncertainty, joint reliability-redundancy allocation problem and joint redundancy allocation and maintenance optimization. Through analysis and discussions, we will outline future research directions.

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Prof. Stefan Feuerrigel gave a talk on “Learning policies for decision-making with causal machine learning: The case of development financing”

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The Sustainable Development Goals (SDGs) of the United Nations provide a blueprint of a better future by “leaving no one behind”, and, to achieve the SDGs by 2030, poor countries require immense volumes of development aid. In this work, we develop a causal machine learning framework for estimating heterogeneous treatment effects of aid disbursements that inform optimal aid allocation. We demonstrate the effectiveness of our method using data with official development aid earmarked to end HIV/AIDS in 105 countries, amounting to more than USD 5.2 billion. For this, we first show that our method successfully computes heterogeneous treatment-response curves using semi-synthetic data. Then, using real-world HIV data, we find that an optimal aid allocation suggested by our method could reduce the total number of new HIV infections compared to current allocation practice. Our findings indicate the effectiveness of causal machine learning to inform cost-efficient allocations of development aid that maximize progress towards the SDGs.

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Prof. Indranil Bose gave a talk on “Toward a Cognitive Understanding of Mobile Augmented Reality”

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Augmented Reality (AR) has become increasingly popular in different areas of application and is transforming the way mobile commerce is conducted through mobile apps. It imbibes the sensory experience of local presence during online browsing. We investigate the impact of AR-based presentation on sales rank of products using data from Amazon AR View. This is followed by our investigation of the impact of AR on perceived diagnosticity, perceived risk, perceived cognitive load, and emotion of users. Using a mixed-method approach, we find that the use of AR on a mobile app significantly improves sales rank, enhances perceived diagnosticity, and reduces perceived risk. These effects are greater for technology products. Additionally, we find that AR significantly increases perceived cognitive load for non-technology products. We capture various touch movements, such as pan, pinch, and rotate as well as touch pressure, in consumers’ AR interactions and find that they significantly impact the generated emotion. Our research contributes to the literature on mobile commerce and provides directions on when to use the AR interface for product presentation and how to assess consumers’ reactions to it.

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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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Prof. Chao Hu gave a talk on physics-informed machine learning for battery degradation diagnostics

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Battery diagnostics aims to monitor a lithium-ion battery’s state of health (SOH) by estimating its capacity and degradation parameters over the service life. The SOH estimation informs online maintenance/control decision making, all performed within a battery management system. This talk will first give an overview of battery degradation diagnostics and then discuss the long-term testing and methodology development efforts led by a team of researchers at Iowa State University and the University of Connecticut. An emphasis will be placed on physics-informed machine learning for degradation diagnostics. Methodologies will be demonstrated using an industry-relevant application on implantable-grade lithium-ion batteries.

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Prof. Pascal Van Hentenryck gave a talk on Fusing AI and Optimization for Engineering

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This talk reviews new methodological developments in fusing data science, machine learning, and optimization, as well as their applications in energy systems, mobility, supply chains, fair recommendations. It highlights the symbiotic relationships between deep learning, reinforcement learning, and optimization, through optimization proxies and end-to-end learning.

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