Department of Statistics
Harvard Faculty of Arts and Sciences
Ivana Malenica works at the intersection of causal inference and machine learning, with an emphasis on development of statistical methodology for efficient and robust estimation in non/semi-parametric settings. Most of her work involves causal inference with complex dependence, as well as development of novel machine learning algorithms for data-driven decision-making. She is particularly interested in applications spanning personalized medicine and precision public health.
Ivana completed her PhD in Biostatistics from University of California, Berkeley under the supervision of Dr. Mark van der Laan. Prior to her PhD, she obtained a MA in Biostatistics and BS in Mathematics. During her graduate studies, she served as a Berkeley Institute for Data Science Fellow and the Biomedical Big Data Fellow, and enjoyed numerous collaborations with Bill and Melinda Gates Foundation, Gilead Sciences, Kaiser Permanente, Parexel and TGen. Ivana is also a founding core developer of the tlverse software ecosystem for targeted learning, and the corresponding open source handbook.