Bio: Vladimir was a PhD student at the NYU Center for Data Science where he was a part of the Math and Data Group. His graduate work was advised by Robert Kohn and Carlos Fernandez-Granda, and was supported in part by the Moore-Sloan Data Science Environment at New York University. He completed an MS in Mathematics at the Courant Institute under the advisement of Afonso Bandeira, as well as a JD and LLM at the NYU School of Law. His research area is mathematical and statistical foundations of data science and machine learning. I focus on their intersection with differential equations, control theory and optimization, and on applications to online learning, medical and molecular imaging, computer vision, robotics, finance and regulatory policy.

Link to Vladimir Kobzar’s Website