* Liaison & Collaboration: Act as a key liaison between technical teams and clinical stakeholders, ensuring seamless communication, understanding project requirements, and aligning technical solutions with clinical needs.* Data Pipeline Development: Design, build, and maintain efficient ETL and data pipelines using Python and SQL. Extract and transform diverse fluid biomarker, clinical, and genetic datasets from internal and external sources into research-ready formats, meeting the needs of researchers.* Data Repository Development: Develop an in-house metadata repository for the MAC Biospecimens & Genetics Program in collaboration with the MAC IT team and scientific researchers, enhancing data accessibility and usability.* Proteomic Data Processing: Build Python-based pipelines to transform large-scale proteomic data from SomaLogic SomaScan Assay Services. Present data quality control processes and SomaLogic-specific formats to researchers, and interact regularly with the SomaLogic tool suite to track study progress and access results.*Reporting & Analysis: Utilize Python and SQL to create reports for the MAC Sample Processing Lab, covering billing, metrics, and data validation. Present these reports at weekly meetings, ensuring accurate tracking and management of sample data.* Custom Data Solutions: Fulfill ad hoc data requests from the Biospecimens & Genetics Program, including building datasets for grant approval, unblinding biomarker results, and facilitating research cohort design, while managing regular deadlines and responsibilities.

- Built and maintained SQL stored procedures using Microsoft SQL Server Management Studio (SSMS) to optimize report generation for clients, including the U.S. Air Force, Army, Navy, and commercial organizations.- Developed and maintained SQL Server Reporting Services (SSRS) environmental data reports in Microsoft Visual Studio for client databases, including the Formerly Used Defense Sites Chemistry Database (FUDSChem).- Managed data-intensive FUDSChem environmental restoration projects by hosting monthly district meetings with U.S. Army Corps of Engineers (USACE) personnel across different time zones. Responsibilities included monitoring the quality of analytical data, tracking groundwater field sampling events from planning to completion, and coordinating with laboratories to ensure complete and compliant data for agency decision-makers within the U.S. federal government.- Provided technical troubleshooting and advice to database users from both technical and non-technical backgrounds. Assisted with validating electronic data deliverables and field sampling data.

- Wrote an adapted Random Sample Consensus (RANSAC) machine learning algorithm to fit milk curve models to highly contaminated temporal dairy cow milk lactation data, as well as provide diagnostic plots and optimization records. - Wrote a web application using R Shiny package that uses an adapted Random Sample Consensus (RANSAC) algorithm to project daily milk yield of a dairy cow given user input parameters, along with diagnostic plots corresponding to statistical fits. The application also performs a sensitivity analysis of the RANSAC algorithm by visualizing error statistics of one hundred statistical fits produced by RANSAC, each fit corresponding to a simulated dataset. - Wrote a supplementary algorithm that estimates optimal RANSAC parameters, such as the number of iterations to best approximate a milk curve, and the threshold residual value that distinguishes an inlier from an outlier. - Wrote a function to simulate temporal dairy cow lactation data. This was achieved by simulating variability about the milk curve, outliers due to illness and milk claw kickoffs, and the underlying shape of the milk curve. - The above tasks were written in R programming language in collaboration with UC Davis PhD student, Catie McVey.

- Professor Marilyn S. Townsend, Ph.D., R.D. and her team studied habits and biological factors of their subjects in order to create an obesity risk assessment tool for young children. Hispanic children from low-income households were the demographic of interest during my involvement. I was the only undergraduate member of the team. - Utilized Statistical Analysis System (SAS) to produce summary statistics and utilize methods such as Kruskal-Wallis and Analysis of Variance (ANOVA), in order to make large data sets interpretable to the research team. - Presented my statistical analyses at weekly meetings with the research team to examine associations between daily habits of children and their score on a proposed obesity risk assessment tool.

- Interfaced with the largest web based Environmental Data Management System (EDMS) currently on the internet. - Queried field data files concerning environmental restoration projects within the Department of Defense in SQL. - Uploaded and validated CSV files containing environmental data, such as lithology data, groundwater measurements, and well construction information, into Synectics’ EDMS. - Entered field data measurements into Excel using coded values compatible with their EDMS.

- Prepared drinks and meals to customer satisfaction, restocked merchandise, washed dishes, and cleaned cafe. - Interacted with customers, completed cash and card transactions, and verified the cash register contained the expected monetary amount before and after each shift.

- Aided student with homework and created algebra exercises to enhance his understanding of the material. - Used adaptive learning strategies to accommodate the needs of a young student coping with ADD.

- Entered research outputs into the UK Research Council's database, Researchfish, along with general office work. - Assisted at the 4th Annual Conference of CIS, as well as the EPSRC (Engineering and Physical Sciences Research Council) Manufacturing the Future Conference.