@article{cui_singh_staicu_reich_2021, title={Bayesian variable selection for high-dimensional rank data}, volume={5}, ISSN={["1099-095X"]}, DOI={10.1002/env.2682}, abstractNote={AbstractThe study of microbiomes has become a topic of intense interest in last several decades as the development of new sequencing technologies has made DNA data accessible across disciplines. In this paper, we analyze a global dataset to investigate environmental factors that affect topsoil microbiome. As yet, much associated work has focused on linking indicators of microbial health to specific outcomes in various fields, rather than understanding how external factors may influence the microbiome composition itself. This is partially due to limited statistical methods to model abundance counts. The counts are high‐dimensional, overdispersed, often zero‐inflated, and exhibit complex dependence structures. Additionally, the raw counts are often noisy and compositional, and thus are not directly comparable across samples. Often, practitioners transform the counts to presence–absence indicators, but this transformation discards much of the data. As an alternative, we propose transforming to taxa ranks and develop a Bayesian variable selection model that uses ranks to identify covariates that influence microbiome composition. We show by simulation that the proposed model outperforms competitors across various settings and particular improvement in recall for small magnitude and low prevalence covariates. When applied to the topsoil data, the proposed method identifies several factors that affect microbiome composition.}, journal={ENVIRONMETRICS}, author={Cui, Can and Singh, Susheela P. and Staicu, Ana-Maria and Reich, Brian J.}, year={2021}, month={May} }
 @article{jones_savage_naughton_singh_robertson_roe_marcellin-little_mathews_2018, title={Influence of Radiographic Positioning on Canine Sacroiliac and Lumbosacral Angle Measurements}, volume={31}, ISSN={["2567-6911"]}, DOI={10.3415/vcot-17-04-0052}, abstractNote={
            Objectives To evaluate the influence of radiographic malpositioning on canine sacroiliac and lumbosacral inclination angles.
            Methods Using canine cadavers, lateral pelvic radiographs were acquired with the radiographic beam in a neutral position and then rotated 5, 10 and 15° to mimic rotational malpositioning. The focal point of the beam was then focused over the abdomen and again over mid-diaphysis of the femur to mimic an abdominal or femoral radiographic study.
            Results Five degrees of rotational malpositioning did not influence measurements of sacroiliac or lumbosacral inclination, but malpositioning by more than 5° led to a significant decrease in both sacroiliac and lumbosacral angles. Moving the focal point to the femur significantly decreased the measured lumbosacral angle. Abdominally centred radiographs had no effect on lumbosacral and sacroiliac angle measurements.
            Clinical Significance When evaluating canine lumbosacral and sacroiliac angles radiographically, pelvic rotation of more than 5° should be avoided as should the use of lateral radiographs centred over the femur.}, number={1}, journal={VETERINARY AND COMPARATIVE ORTHOPAEDICS AND TRAUMATOLOGY}, author={Jones, Susan and Savage, Mason and Naughton, Brian and Singh, Susheela and Robertson, Ian and Roe, Simon C. and Marcellin-Little, Denis J. and Mathews, Kyle G.}, year={2018}, month={Jan}, pages={30–36} }
 @article{pacifici_reich_miller_gardner_stauffer_singh_mckerrow_collazo_2017, title={Integrating multiple data sources in species distribution modeling: a framework for data fusion}, volume={98}, ISSN={["1939-9170"]}, DOI={10.1002/ecy.1710}, abstractNote={AbstractThe last decade has seen a dramatic increase in the use of species distribution models (SDMs) to characterize patterns of species’ occurrence and abundance. Efforts to parameterize SDMs often create a tension between the quality and quantity of data available to fit models. Estimation methods that integrate both standardized and non‐standardized data types offer a potential solution to the tradeoff between data quality and quantity. Recently several authors have developed approaches for jointly modeling two sources of data (one of high quality and one of lesser quality). We extend their work by allowing for explicit spatial autocorrelation in occurrence and detection error using a Multivariate Conditional Autoregressive (MVCAR) model and develop three models that share information in a less direct manner resulting in more robust performance when the auxiliary data is of lesser quality. We describe these three new approaches (“Shared,” “Correlation,” “Covariates”) for combining data sources and show their use in a case study of the Brown‐headed Nuthatch in the Southeastern U.S. and through simulations. All three of the approaches which used the second data source improved out‐of‐sample predictions relative to a single data source (“Single”). When information in the second data source is of high quality, the Shared model performs the best, but the Correlation and Covariates model also perform well. When the information quality in the second data source is of lesser quality, the Correlation and Covariates model performed better suggesting they are robust alternatives when little is known about auxiliary data collected opportunistically or through citizen scientists. Methods that allow for both data types to be used will maximize the useful information available for estimating species distributions.}, number={3}, journal={ECOLOGY}, author={Pacifici, Krishna and Reich, Brian J. and Miller, David A. W. and Gardner, Beth and Stauffer, Glenn and Singh, Susheela and McKerrow, Alexa and Collazo, Jaime A.}, year={2017}, month={Mar}, pages={840–850} }
 @inproceedings{schuman_disney_singh_bruer_mitchell_klibisz_plank_2016, title={Parallel evolutionary optimization for neuromorphic network training}, booktitle={Proceedings of 2016 2nd Workshop on Machine Learning in HPC Environments (MLHPC)}, author={Schuman, C. D. and Disney, A. and Singh, S. P. and Bruer, G. and Mitchell, J. P. and Klibisz, A. and Plank, J. S.}, year={2016}, pages={36–46} }