Learn more about The Harvard Data Science Initiative Postdoctoral Fellowship.
Congratulations to the 2022 HDSI Postdoctoral Fellows! From left to right: Ivana Malenica, George Dasoulas, and Esther Rolf.
The Harvard Data Science Initiative (HDSI) welcomes and introduces Ivana Malenica, George Dasoulas, and Esther Rolf as its 2022 HDSI Postdoctoral Fellows. The HDSI Postdoctoral Fellows will work independently over their two-to-three-year fellowships with the guidance and partnership of Harvard University faculty. This year’s Fellows are exceptional early-career researchers with doctoral degrees in Biostatistics and Computer Science whose interests lie at the intersection of machine learning and several different fields.
Ivana Malenica’s current research involves causal inference with complex dependence and the development of machine learning algorithms for data-driven decision-making, with a particular emphasis on the elaboration of statistical methodology for efficient and robust estimation in non/semi-parametric settings. In addition, Malenica is passionate about exploring applications spanning personalized medicine and precision public health. Malenica studied for her Ph.D. in Biostatistics at the University of California, Berkeley, during which served as a Berkeley Institute for Data Science (BIDS) Fellow, the Biomedical Big Data Fellow, and had the opportunity to collaborate with organizations such as the Bill and Melinda Gates Foundation.
As an HDSI Postdoctoral Fellow, George Dasoulas is interested in leveraging the computational capabilities of learning models for accelerated biomedical research. His work on machine learning for structured data focuses on creating learning models, specifically graphs and non-Euclidean structures, that extract knowledge for diverse real-world applications ranging from communication networks to bioinformatics. Dasoulas received his Ph.D. in Computer Science from Laboratoire d'informatique at École Polytechnique in Paris, France, during which he worked as a doctoral researcher in the telecommunications industry and recognized the crucial impact of graph learning models in contributing to accurate predictions.
Esther Rolf’s research revolves around the relationship between machine learning, statistics, algorithms, and problem-solving for positive social impact. Rolf is studying how data acquisition influences the efficacy and applicability of machine learning systems and how to responsibly use machine learning in decision-making settings. During her HDSI Postdoctoral Fellowship, Rolf will continue to investigate the key characteristics of different data sources, types, and structures that affect efficacy of data-driven algorithms in order to innovate and connect insights in applied and theoretical statistical machine learning. Rolf joins the HDSI after earning her Ph.D. in Computer Science from UC Berkeley this year.