Browsing by Author "Schupbach, Jordan"

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Combining Dynamic Bayesian Networks and Continuous Time Bayesian Networks for Diagnostic and Prognostic Modeling
(IEEE, 2022-08) Schupbach, Jordan; Pryor, Elliott; Webster, Kyle; Sheppard, John
The problem of performing general prognostics and health management, especially in electronic systems, continues to present significant challenges. The low availability of failure data, makes learning generalized models difficult, and constructing generalized models during the design phase often requires a level of understanding of the failure mechanism that elude the designers. In this paper, we present a new, generalized approach to PHM based on two commonly available probabilistic models, Bayesian Networks and Continuous-Time Bayesian Networks, and pose the PHM problem from the perspective of risk mit-igation rather than failure prediction. We describe the tools and process for employing these tools in the hopes of motivating new ideas for investigating how best to advance PHM in the aerospace industry.
Let’s Talk Online Video Pilot Results
(Montana State University, 2017-03) Flagg, Kenneth A.; Schupbach, Jordan; Lin, Lillian
Background: Montana has the highest suicide rate in the nation, with 26 deaths from suicide per 100,000. To address this threat, young adults were recruited to perform community-based theatre projects about the importance of seeking professional help for depression and thoughts of suicide. This study examined the effectiveness of two short documentaries that were based on the Let’s Talk theatre intervention in reducing stigma of help-seeking. Methods: 87 students at a college in Billings, Montana were randomly assigned to two interventions and one control group during the 2016-17 school year. Self-administered questionnaires were completed by students in all groups at baseline and approximately 2 weeks after program implementation. Results: 38 students completed both the baseline and follow-up questionnaires (a 44% follow-up rate). Lower rates of self-stigma of seeking help (SSOSH) were observed among students in the longer format intervention group. For respondents in that intervention group, we estimate the mean SSOSH score decrease to be 4.16 (SE = 1.67) more than the mean score decrease for individuals in the control group (P = 0.017). There was no evidence that the students' race/ethnicity, grade, and gender altered the impact of the intervention on any of the outcomes assessed in this analysis. Conclusion: This study provides preliminary analysis of the intervention, but further evaluations are needed with a larger and more racially and socio-economically diverse sample. Let’s Talk continues to be a unique, narrative-based suicide prevention program with demonstrated effects on self-reported stigma of help-seeking in a study utilizing a randomized experimental design.
Reconstructing embedded graphs from persistence diagrams
(2020-10) Belton, Robin Lynne; Fasy, Brittany T.; Mertz, Rostik; Micka, Samuel; Millman, David L.; Salinas, Daniel; Schenfisch, Anna; Schupbach, Jordan; Williams, Lucia
The persistence diagram (PD) is an increasingly popular topological descriptor. By encoding the size and prominence of topological features at varying scales, the PD provides important geometric and topological information about a space. Recent work has shown that well-chosen (finite) sets of PDs can differentiate between geometric simplicial complexes, providing a method for representing complex shapes using a finite set of descriptors. A related inverse problem is the following: given a set of PDs (or an oracle we can query for persistence diagrams), what is underlying geometric simplicial complex? In this paper, we present an algorithm for reconstructing embedded graphs in Rd (plane graphs in R2) with n vertices from n2 −n+d+1 directional (augmented) PDs. Additionally, we empirically validate the correctness and time-complexity of our algorithm in R2 on randomly generated plane graphs using our implementation, and explain the numerical limitations of implementing our algorithm.
Statistical Consulting and Research Services: Past, Present, and Future
(Montana State Univeristy, 2017-04) Flagg, Kenneth A.; Barbour, Christopher; Mack, Andrea; Schupbach, Jordan; Zhang, Huafeng
Statistical Consulting and Research Services (SCRS) is a group of statisticians at Montana State University (MSU) whose mission is to collaborate with domain experts across campus to improve the scientific research conducted at MSU and within the Montana University System. Since its inception, SCRS has grown at a tremendous rate and our statisticians continue to work with student and faculty researchers from a variety of scientific domains across the Montana University System. We present an overview of the history regarding how SCRS came to be, the services we perform, and the diversity of researchers that we collaborate with. We discuss the technical tools we incorporate in our workflow process and the steps we perform from the initial meeting to the final product. We will also highlight our vision moving into the future including what opportunities we see to continue improving the scientific research across the Montana University System, specifically highlighting the additional services we hope to provide here at MSU.