Spearheaded the technology department and liaised between technical and non-technical stakeholders, translating business requirements into actionable IT strategies and driving alignment towards common goals. Co-developed and executed a strategic vision for integrating Customer Relationship Management technology into business operations, leveraging data insights to drive a 100% increase in location acquisition and a 50% increase in yearly average revenue by end of year 2023, and developed strategies on threat identification and mitigation. Led cross-functional teams in the design and implementation of secure scalable solutions, effectively reducing technician times per location by 50%, reducing total workloads by 25%, and location revisits by 75%. Engineered and deployed texting and reporting tools reducing user account creation times by 50%. Managed and trained technicians working in a Microsoft Windows operating system environment with specific training in network protocols, printer protocols, and software maintenance. Administrated email, CRM, and wiki systems which supported daily operations. Designed and implemented data pipelines using Azure Data Factory to handle terabytes of real-time accounting data generated by no-chance slot machines across multiple entertainment venues

In the SEA lab, I have explored the literature on small sample-size psychological studies with the aim of using similar techniques to analyze a novel data set consisting of infant sleep, maternal sleep, and maternal mood data. The goal is to implement times series models to find Granger causality between any of the aforementioned data sets.

Created business models in Microsoft Excel to discover and report tax remittance inconsistencies between client's business financials and their auditor's audit report. I work at SSTS during my visiting time in Las Vegas, NV.

The research consists of building models used to establish principled statistical confirmation for basic connectome features. The methods used for the analysis of rs-fMRI scans and the subject's corresponding symptomatic/genetic data are derived from the Principle Component Analysis (PCA) and Canonical Correlation Analysis (CCA) developed by Harold Hotelling in 1933 and 1936, respectively. Both methods are considered powerful elementary tools for analyzing relations between two sets of variates of high dimensional data. I am also running time-series analysis on imaging data to predict function from gene expression. The majority of the data used for the analyses comes from the Allen Human Brain Atlas generated and analyzed by the Allen Institute for Brain Science.

Rubinov Lab: Building and maintaining statistical brain-networks to analyze fMRI data of psychopathy patients to predict function from genetic data.