Designed and implemented a C# ad-serving engine for precise ad personalization and extensive experimentation, developing a distributed, scalable, fault-tolerant system for high throughput with automated retry mechanisms.

Maintained critical systems, impacting organizational metrics, and led integration efforts across teams with endpoint development and documentation to streamline operations.

Research on domain generalization techniques to improve the performance of models on out-of-distribution (OOD) datasets such as WILDS.

Investigation of sampling algorithms for effective training on datasets with non-trivial OOD domains.

Development and implementation of group fairness models for MIT-IBM AI fairness user survey.

Led the development of Cloud Init test automation infrastructure to eliminate bottleneck manual tests

for crucial cloud OS initialization.

Integrated modular testing infrastructure into existing testing code base with no interference.