Contact: pk1822@nyu.edu

Bio: Polina was a PhD student at the NYU Center for Data Science and a DeepMind fellow supervised by Professor Andrew Wilson. Her research focused on probabilistic machine learning, Bayesian deep learning and uncertainty estimation. She was interested in building robust machine learning models and understanding when they can be trusted in making decisions, which is important for many sensitive applications. She obtained her Bachelor’s degree in Computer Science at the Higher School of Economics in Moscow. During her undergrad, she worked at Bayesian Methods Research group with professor Dmitry Vetrov, our research focused on variational inference and regularization for deep neural networks. Just before starting my PhD, she did a summer internship at EPFL in Machine Learning and Optimization Lab, where she worked on zero-order optimization for low precision neural networks supervised by professors Martin Jaggi and Dan Alistarh. She also had a chance to experience industry at Google as a software engineering intern in Munich and Seattle offices, where she worked on the backend, distributed systems and algorithm parallelization for internal tools. Before coming to NYU, she completed one year of a PhD program in Operations Research and Information Engineering at Cornell University where she started working with Professor Andrew Wilson on low-precision training of neural networks, Bayesian deep learning and normalizing flows; after her first year of a PhD, their research lab transferred to New York University.

Awards: 

• DeepMind Fellowship