Dustin Burns, Ph.D., GStat

Burns, Dustin. 2021. "Applications of Artificial Intelligence in Cybersecurity." In Software Engineering: Artificial Intelligence, Compliance, and Security, edited by Brian D’Andrade. New York: Nova Science Publishers.

Measurements of properties of the Higgs boson decaying into the four-lepton final state in pp collisions at √s = 13 TeV. By CMS Collaboration (Albert M Sirunyan et al.). arXiv:1706.09936 [hep-ex].

Search for associated production of dark matter with a Higgs boson decaying to bb or γγ at √s = 13 TeV. By CMS Collaboration (Albert M Sirunyan et al.). arXiv:1703.05236 [hep-ex].

Les Houches 2015: Physics at TeV colliders - new physics working group report. By G. Brooijmans et al.. arXiv:1605.02684 [hep-ph].

Searching for ultra high-energy cosmic rays with smartphones. By Daniel Whiteson, Michael Mulhearn, Chase Shimmin, Kyle Cranmer, Kyle Brodie, Dustin Burns. arXiv:1410.2895 [astro-ph.IM]. 10.1016/j.astropartphys.2016.02.002. Astropart.Phys. 79 (2016) 1-9.

For a complete listing of publications, see https://inspirehep.net/literature?sort=mostrecent&size=25&page=1&q=Dustin%20Burns

Teaching Assistant, Department of Physics, University of California, Davis, 2011-2013, 2017

Graduate Student Researcher, Department of Physics, University of California, Davis, 2013-2017

Graduate Student Researcher, CERN, 2013-2017

Undergraduate Researcher, Department of Physics, Georgia Institute of Technology, 2008-2011

Undergraduate Researcher, Department of Mathematics, Georgia Institute of Technology, 2009

Undergraduate Researcher, Department of Mathematics, University of Georgia, 2008

International Society for Pharmacoepidemiology (member)

InfraGard (vetted member)

American Statistical Association (member)

American Physical Society (member)

Exponent has ingested data from one of the largest government personnel databases and maintained a data warehouse to preserve historical records and update the status of active personnel for over 20 years. Data transfer is performed over a secure network connection from the client’s relational database servers to our in-house SAS database workstation. In this relational database structure, various queries are made to extract rich subsets of the data for specific analyses. Our team of data scientists and statisticians performs recurring monthly analyses in both SAS and Python to track trends over time which give key insight into the agency’s personnel records and field-deployed equipment. These analyses include tracking of hardware failure modes in the field over time and predictive analytics with machine learning techniques to reduce overall failure rates. Our contributions have resulted in millions of dollars in saved costs and a deeper understanding of the clients multifactor data assets through data-driven reporting and data visualizations.

Exponent’s team of database engineers and data scientists successfully aggregated medical data from 100+ hospitals for thousands of events where a medical device malfunctioned and further analysis was required for FDA compliance. This data included patient records, medical device logs and investigation reports, hospital training records, device testing reports, and included interfacing with the client’s database endpoints. Data files were exported from the client’s enterprise data management system and transmitted to Exponent via secure network file transfer. Each data type had a custom ETL pipeline including time-series analysis of log files and text analysis of interviews and investigation reports. After the data was collected and processed, a multivariate analysis was conducted to extract new insights into the failure investigations, including machine learning modeling, text mining and natural language processing, and application of advanced statistical methods. For example, a correlation analysis was performed to understand the relationship between recurring errors in device log files and the propensity for a device to cause harm to a patient. Additionally, a cluster analysis was performed using NLP techniques to discover patterns in the text of investigation reports that may be related to hidden similarities in device failures. The analysis resulted in a report which elucidated underlying patterns and correlations not initially apparent to investigators.