Education

Research Description

Dr. Diaconeasa's research focus includes theories, applications, and simulation-based techniques in risk sciences such as traditional and dynamic probabilistic risk assessment, reliability analysis, resilient systems design, probabilistic physics of failure modeling, and Bayesian inference.

Grants

Simulation-based reliability methodology development for autonomous controls and adversarial human actions involved in fission battery designs

US Dept. of Energy (DOE)(12/10/20 - 9/30/21)

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Simulation-based reliability methodology development for autonomous controls and adversarial human actions involved in fission battery designs

Sponsor: US Dept. of Energy (DOE)

Start Date: 12/10/20

End Date: 9/30/21

Abstract

Fission batteries are unique from a technological standpoint given their proposed autonomous operation and tamper-proof features. Also, the actual operation of fission batteries will be novel in that local users may only have simple on/off control capabilities, while the manufacturer will need to be able to remotely monitor a fleet of units spread across different geographical regions. Furthermore, it must be self-detecting/protecting from human adversaries. The burden of local monitoring and control is therefore shifted to the autonomous technology and limited remote-monitoring capability of the manufacturer. Reliability analysis for cyber-physical systems (CPS) such as fission batteries is challenging since modern CPS incorporate distributed and networked heterogeneous software, hardware, and physical components that operate and interact in tandem. Human actions, such as those of the adversary, can also play an important role and need to be considered in the design process. All these ingredients yield highly structural and behavioral complexity for CPS models, making them computationally expensive to predict, model, and test. Consequently, highly sophisticated failure scenarios emerge, revealing new challenges for state-of-the-art quantitative reliability metrics and evaluation methods. To execute our proposal, the risk modeling needed for autonomous operations will first require newly developed dynamic PRA methods, due to the self-diagnosis, self-adjustment, and duration-prediction capabilities needed for autonomous operations. Second, reliability modeling will require the ability to integrate autonomous control, associated error-detection algorithms, and human actions for both cyber and tamper-proof designs. Finally, to perform the reliability/resilience evaluations, we will use Dual-Graph Error Propagation Models (DEPM) based on discrete-time Markov chain (DTMC) models.

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A Quantitative Approach to Assessing Drugs Supply Chain Disruptions Leading to Shortages

US Food & Drug Administration(9/28/20 - 9/27/21)

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A Quantitative Approach to Assessing Drugs Supply Chain Disruptions Leading to Shortages

Sponsor: US Food & Drug Administration

Start Date: 9/28/20

End Date: 9/27/21

Abstract

In this project we plan to develop a methodology to quantitatively characterize the drug shortages in terms of their frequency, persistence, and intensity by modeling their supply chain. Quantitative metrics, such as relative importance or criticality, will be developed to identify the most important contributors to shortages. With the estimation of the impact of shortages using available public information, we will be able to demonstrate the methodology by developing a risk of shortage profile in terms of frequency and consequences for a number of drug supply chains.

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