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Since transitioning into the private sector in biotechnology, I have been involved in various innovative research projects. However, due to the sensitive nature of this work and existing nondisclosure agreements, the specific details of these projects cannot be publicly shared. This constraint underscores my commitment to maintaining the highest standards of confidentiality and integrity in research.
This project was a part of the GenoPhenoEnvo NSF HDR award. It involved developing a simplified proof of concept model in the R language, utilizing the bnlearn library to construct diacyclic graphs for the phenotypic, genomic, and environmental data from the TERRA-REF Project. The project expanded to experiment with the NO-TEARS DAG solving algorithm. However, it was ultimately unsuccessful in resolving the directed graph structure with this implementation. Despite the project not achieving its intended outcome, it provided significant insights into Bayesian statistics and highlighted how directed acyclic graph structure solving algorithms remain an active area of research.
As a trained microbial ecologist and microbiologist, I am deeply interested in the functioning of microbial communities. My doctoral research concentrated on the microbiomes of recirculating aquaculture systems and aquaponics. Previous work in this area is detailed here.
Developed for the University of Arizona Data Science Fellows Program, this course leverages the CyVerse Discovery Environment and docker to provide postdoctoral researchers in computer science and biology with an introduction to statistical analysis in R. Topics include basic regression, distribution modeling, Markov chains, and Bayesian Belief Networks.
For any recent publication activities, please visit my Google Scholar page.