• Designed and implemented a new deployment pipeline leveraging AWS SSM and ECR integrated with GitHub Actions, reducing deployment time by 5x and 10x for domestic and international deployments respectively.

• Implemented a centralized logging infrastructure using AWS CloudWatch to monitor and support multiple edge machines, improving real-time diagnostics and system reliability.

• Developed an automated end-to-end integration testing pipeline triggered by pull requests, enhancing codebase integrity and reducing manual testing efforts.

• Deployed and configured IonSight at client locations, ensuring uninterrupted operations and delivering swift troubleshooting support.

• Orchestrated a successful transition of the robot controller motion codebase from “Minecraft” programming to proprietary UR Script. This transformation achieved full automation of proprietary operations, resulting in an impressive 30% reduction in setup time and a 10% reduction in operation time.

Successfully migrated five software projects from the Research Computing Department to Azure DevOps, implementing full CI/CD automation via infrastructure-as-code using YAML.

• Designed and developed new unit testings for existing modules and integration tests.

• Implemented functional module mocking for all Linux distros.

• Restructured the production codebase to increase readability and reduce time and memory complexity. - Assigned a backend task to develop an internal tool that would scale the performance of the company's experiments at least three-fold by using distributed computing design. Task halted due to internal reorganization.

Recipient of a full scholarship to the NHLBI-funded Boston University Summer Institute for Research Education in Biostatistics (Summer 2021). The program modules were in biostatistics, epidemiology, clinical trials, statistical genetics, infectious disease modeling, and training in SAS and R. A major component of the program was analysis, using SAS and R, of data collected from the Framingham Heart Study and Jackson Heart Study. The computing component included generating descriptive statistics, analysis of variance, chi-square tests, multiple linear and logistic regression analysis. At the end of the program, we delivered a group research project that examines the association between diabetes status and alcohol intake using multiple logistic regression and multiple linear regression with a collection of 7000+ datasets from the Jackson Heart Study.