I am Julian Morimoto, an incoming Ph.D. student in Statistics at UC Berkeley. I previously served as a Research Fellow supported by the Harvard University Frederick Sheldon Traveling Fellowship and the Fulbright Program. My work primarily focuses on developing new statistical methodologies for doing legal research. One of my recent contributions in this space is a paper titled "The Effect of Sample Size and Missingness on Inference with Missing Data," published in Communications in Statistics - Theory and Methods. My research has been presented as part of Ateneo de Manila University's Mathematics Research Seminar Series, a Roundtable Discussion in Manila, Philippines with the U.K. and U.S. embassies as well as Human Rights Watch, The Asia Foundation, and Freedom House, as well as on the AutoML podcast. Before my current role, I worked as an Associate for the law firm Kirkland & Ellis LLP, advising on fund formation, anti-money laundering, and notification/reporting matters.

I aim to focus on the intersection of theoretical and methodological research in statistics, and how machine learning and artificial intelligence can revolutionize the study and practice of law.

I hold a J.D. from Harvard University, and a B.A. in Mathematics from Case Western Reserve University. While at Harvard, I conducted several independent research projects exploring the intersection of Statistics and Law, supervised by Professors Gerald Neuman, Gary King, Kosuke Imai, and Crystal Yang. I was also a recipient of the Human Rights Program and Chayes Fellowships for international legal work and research.

Outside of work, I like to do ballet and was a proud member of the Harvard Ballet Company. As a first-generation college student from a working-class background, I am also passionate about improving accessibility to higher education and helping students from low-income backgrounds and other underrepresented minorities reach their full potential.