I am a Ph.D. Candidate in Economics at the University of Texas at Austin, specializing in Industrial Organization and Causal Machine Learning. My research focuses on the intersection of economics, data science, and technology, with a particular interest in online advertising and platform markets.   In my dissertation, I leverage Causal Machine Learning techniques, including Causal Forest, to analyze the impact of data on match quality in online advertising. My findings suggest that mergers between platforms with complementary data could significantly improve match quality and user click rates.   I also gained valuable experience as a Summer Intern at the International Monetary Fund (IMF), where I contributed to the Regional Economic Outlook and an IMF working paper.   I am proficient in various programming languages and statistical software, including Python, SQL, Stata, and MATLAB. I am also skilled in econometrics, causal inference, AB testing, and machine learning.I am eager to apply my skills and knowledge to real-world challenges and contribute to the development of innovative and impactful solutions. I am particularly interested in opportunities that allow me to leverage my expertise in causal inference and machine learning to improve online platform effectiveness and user experience.Feel free to connect with me to discuss my research, career interests, or potential collaborations.