As a DoD SMART Fellowship recipient and graduate of Georgetown University's Master of Science in Data Science and Analytics program, I applied my data-driven mindset to defense-related research. Through hands-on experience and the program's support, I honed my skills in machine learning and statistical modeling. I actively contributed to impactful projects at the intersection of data science and defense, using my expertise to make a meaningful difference.

Selected from 30,000 applicants, my DS4A (Data Science for All) Fellowship provided me with hands-on experience in data science and analytics, including coursework, project-based learning, and mentorship from industry professionals. The capstone research project was a highlight of the program, where my team and I identified areas of food insecurity in the United States during pandemic times using machine learning algorithms and statistical models. I gained expertise in data visualization, statistical modeling, and machine learning, and worked on other exciting projects such as sentiment analysis of social media data and predictive maintenance of machinery. Being a DS4A Fellow was transformative, equipping me with skills and knowledge to pursue a career in data science. I highly recommend this program to anyone looking to kickstart their career in data science.

As a Summer Research Assistant, I conducted Department of Energy research on the energy-saving potential of reduced accuracy computation on mobile systems. I developed power management models, analyzed power vs. performance, and discovered the computational tradeoff between energy and performance. This work substantially impacts the advancement of energy-efficient mobile technology, ultimately reducing the energy consumption of mobile devices and contributing to a more sustainable future.

As a Research Assistance, I conducted Department of Energy research on computational experiments to compare energy consumption models of Exa-scale systems. I developed power management models that saved energy on exa-scale architectures, leading to efficient resource allocation. My theoretical analysis of the power-saving algorithm led to its implementation, resulting in a 10% energy savings. Through my research, I significantly impacted energy efficiency in high-performance computing.