About Me
Having been raised by a mechanical engineer and an architect, the value of problem solving and unconventional critical thinking has been instilled in me for as long as I can remember. My love for technology comes from my belief in its positive benefits to our society, which can only be achieved through a combination of creative thinking and continuous advancements in tech.
As I continue to explore the prospects of Artificial Intelligence, I seek to efficiently implement AI where it's needed most. I'm ready to make an impact. |
I first became interested in Data Science, Machine Learning, and Analytics when I realized the influence these fields have on my personal passion: sports. I grew fascinated with the tools and criteria General Managers of billion dollar franchises use to add players to their teams. While most sports-lovers are well versed in their favorite player's statistics, I soon realized how much more there is to know. Measurements such as advanced statistics and efficiency metrics were, at the time, abandoned by many professionals in the industry. With access to top-tier talent and technology, this puzzled me. I began to research more about how Big Data and analytics are being used in the world today, developing a deep fascination with the potential the industry showed, not only in relation to the future of sports, but also in areas which can have a positive impact on advancing our society as we progress in this technological age. While exploring internships, courses, and personal hobbies at both UCLA and USC , it became clear to me that the best way to utilize my problem solving skills is through a career in the world of analytics.
EDUCATION
Undergraduate: University of California, Los Angeles (UCLA) - B.S. Statistics (2020) Graduate: University of Southern California (USC) - M.S. Applied Data Science: Emphasis in Machine Learning and Artificial Intelligence (2023) |
Coursework
- R for Data Science
- Python and Other Technologies for Data Science
- Regression Analysis
- Experimental Design and Testing (A/B Testing)
- Machine Learning for Data Science
- Probability for Data Science
- Mathematical Statistics
- Foundations of Database Management
- Foundations of Artificial Intelligence
- Foundations and Applications of Data Mining
