I am a postdoctoral researcher working at David Sontag's Clinical Machine Learning Lab, in NYU’s Courant Institute. My research is focused on creating new methods for finding causal relationships in large-scale high-dimensional observational studies. I recently gave a tutorial about causal inference for observational studies, at ICML 2016. One of the major motivations for my research is applications in healthcare and clinical medicine, and I am one of the organizers of the 2016 NIPS workshop on machine learning for healthcare.
I was a Ph.D. student at the School of Computer Science and Engineering at The Hebrew University of Jerusalem, under the guidance of Gal Chechik and Daphna Weinshall. My Ph.D. focused on using matrix manifold optimization tools to build better and faster machine learning algorithms. In the past I have collaborated with neuroscientists and computational biologists in developing machine learning algorithms for interpreting the complex data arising from cutting edge brain research. I have also worked on understanding the role of influence and innovation in contemporary music using computational tools.
From 2011 to 2014 I was a recipient of Google's European Fellowship in Machine Learning.