Jesse Clifton

College of Sciences

Works (3)

Updated: August 5th, 2023 05:02

2023 article

Deep Spatial Q-Learning for Infectious Disease Control

Liu, Z., Clifton, J., Laber, E. B., Drake, J., & Fang, E. X. (2023, July 8). Journal of Agricultural Biological and Environmental Statistics.

By: Z. Liu*, J. Clifton n, E. Laber*, J. Drake * & E. Fang *

author keywords: Infectious diseases; Reinforcement learning; Graph neural networks
topics (OpenAlex): COVID-19 epidemiological studies; Viral Infections and Outbreaks Research; Influenza Virus Research Studies
TL;DR: A novel model-free algorithm is developed for the online management of an infectious disease spreading over a finite set of locations and an indefinite or infinite time horizon and introduces a graph neural network-based estimator in order to improve the performance of this class of algorithms. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
3. Good Health and Well-being (Web of Science; OpenAlex)
Source: Web Of Science
Added: July 31, 2023

2020 article

Q-Learning: Theory and Applications

Clifton, J., & Laber, E. (2020, March 9). Annual Review of Statistics and Its Application.

By: J. Clifton n & E. Laber n

author keywords: reinforcement learning; dynamic treatment regimes; model-free; causal inference; policy search
topics (OpenAlex): Advanced Causal Inference Techniques; Statistical Methods in Clinical Trials; Advanced Bandit Algorithms Research
TL;DR: The history of Q-learning in computer science and statistics is reviewed, formalized within the potential outcomes framework, and variants of infinite-horizon Q- learning and the exploration-exploitation problem are reviewed, which arises in decision problems with a long time horizon. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions (OpenAlex)
Source: Web Of Science
Added: March 23, 2020

2020 article

Use of standardized bioinformatics for the analysis of fungal DNA signatures applied to sample provenance

Allwood, J. S., Fierer, N., Dunn, R. R., Breen, M., Reich, B. J., Laber, E. B., … Faith, S. A. (2020, March 12). Forensic Science International, Vol. 310.

By: J. Allwood n, N. Fierer *, R. Dunn n , M. Breen n , B. Reich n , E. Laber n, J. Clifton n, N. Grantham n, S. Faith n

Contributors: J. Allwood n, N. Fierer *, R. Dunn n , M. Breen n , B. Reich n , E. Laber n, J. Clifton n, N. Grantham n, S. Faith n

author keywords: Forensic microbiology; Bioinformatics; Metabarcoding; Sample provenance
MeSH headings : DNA Barcoding, Taxonomic; DNA, Fungal / chemistry; Demography; Forensic Sciences; Fungi; Humans; Reference Values; Soil
topics (OpenAlex): Environmental DNA in Biodiversity Studies; Microbial Community Ecology and Physiology; Protist diversity and phylogeny
TL;DR: Investigating a small-scale approach as an adaptation of a larger metabarcoding study to develop a model for global sample provenance using fungal DNA signatures collected from dust swabs to facilitate a standardized method for consistent, reproducible sample treatment. (via Semantic Scholar)
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UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries, ORCID
Added: May 26, 2020

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