@article{liu_clifton_laber_drake_fang_2023, title={Deep Spatial Q-Learning for Infectious Disease Control}, ISSN={["1537-2693"]}, DOI={10.1007/s13253-023-00551-4}, journal={JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS}, author={Liu, Zhishuai and Clifton, Jesse and Laber, Eric B. and Drake, John and Fang, Ethan X.}, year={2023}, month={Jul} }
 @article{clifton_laber_2020, title={Q-Learning: Theory and Applications}, volume={7}, ISSN={["2326-831X"]}, DOI={10.1146/annurev-statistics-031219-041220}, abstractNote={Q-learning, originally an incremental algorithm for estimating an optimal decision strategy in an infinite-horizon decision problem, now refers to a general class of reinforcement learning methods widely used in statistics and artificial intelligence. In the context of personalized medicine, finite-horizon Q-learning is the workhorse for estimating optimal treatment strategies, known as treatment regimes. Infinite-horizon Q-learning is also increasingly relevant in the growing field of mobile health. In computer science, Q-learning methods have achieved remarkable performance in domains such as game-playing and robotics. In this article, we ( a) review the history of Q-learning in computer science and statistics, ( b) formalize finite-horizon Q-learning within the potential outcomes framework and discuss the inferential difficulties for which it is infamous, and ( c) review variants of infinite-horizon Q-learning and the exploration-exploitation problem, which arises in decision problems with a long time horizon. We close by discussing issues arising with the use of Q-learning in practice, including arguments for combining Q-learning with direct-search methods; sample size considerations for sequential, multiple assignment randomized trials; and possibilities for combining Q-learning with model-based methods.}, journal={ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 7, 2020}, author={Clifton, Jesse and Laber, Eric}, year={2020}, pages={279–301} }
 @article{allwood_fierer_dunn_breen_reich_laber_clifton_grantham_faith_2020, title={Use of standardized bioinformatics for the analysis of fungal DNA signatures applied to sample provenance}, volume={310}, ISSN={["1872-6283"]}, DOI={10.1016/j.forsciint.2020.110250}, abstractNote={The use of environmental trace material to aid criminal investigations is an ongoing field of research within forensic science. The application of environmental material thus far has focused upon a variety of different objectives relevant to forensic biology, including sample provenance (also referred to as sample attribution). The capability to predict the provenance or origin of an environmental DNA sample would be an advantageous addition to the suite of investigative tools currently available. A metabarcoding approach is often used to predict sample provenance, through the extraction and comparison of the DNA signatures found within different environmental materials, such as the bacteria within soil or fungi within dust. Such approaches are combined with bioinformatics workflows and statistical modelling, often as part of large-scale study, with less emphasis on the investigation of the adaptation of these methods to a smaller scale method for forensic use. The present work was 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. This adaptation was to facilitate a standardized method for consistent, reproducible sample treatment, including bioinformatics processing and final application of resulting data to the available prediction model. To investigate this small-scale method, 76 DNA samples were treated as anonymous test samples and analyzed using the standardized process to demonstrate and evaluate processing and customized sequence data analysis. This testing included samples originating from countries previously used to train the model, samples artificially mixed to represent multiple or mixed countries, as well as outgroup samples. Positive controls were also developed to monitor laboratory processing and bioinformatics analysis. Through this evaluation we were able to demonstrate that the samples could be processed and analyzed in a consistent manner, facilitated by a relatively user-friendly bioinformatic pipeline for sequence data analysis. Such investigation into standardized analyses and application of metabarcoding data is of key importance for the future use of applied microbiology in forensic science.}, journal={FORENSIC SCIENCE INTERNATIONAL}, author={Allwood, Julia S. and Fierer, Noah and Dunn, Robert R. and Breen, Matthew and Reich, Brian J. and Laber, Eric B. and Clifton, Jesse and Grantham, Neal S. and Faith, Seth A.}, year={2020}, month={May} }