Samory Kpotufe

Assistant Professor, Department of Statistics, Columbia University
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Research Interests

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I work in machine learning, with an emphasis on nonparametric methods and high-dimensional statistics. Generally, I’m interested in understanding the inherent difficulty of high-dimensional problems, under practical constraints from real-world application domains. The nonparametric setting is attractive in that it captures scenarios where we have little domain knowledge, which is important as data sciences reach into a diverse range of applications.

My main practical aim is to design adaptive procedures, i.e., practical procedures that can self-tune to unknown structure in data (e.g., manifold, sparsity, clusters), while at the same time meeting the various constraints (e.g., time, space, labeling cost) of modern applications.

For more, here is a recent research statement.

Professional Activities

  • Editorial Board Member: Journal of Machine Learning Research (2014 to present).

  • Area Chair: ICML (2019), NIPS (2015, 2016, 2018), COLT (2015, 2016, 2017), ALT (2018), AISTATS (2017, 2018, 2019).

  • Reviewing: Journal of Machine Learning Research, IEEE Transactions on Information Theory, IEEE Transactions on Pattern Analysis and Machine Intelligence, Annals of Statistics, ESAIM Probability and Statistics, Neural Information Processing Systems (NIPS), ACM-SIAM Symposium On Discrete Algorithms (SODA), International Conference on Machine Learning (ICML), …

Short bio

I graduated (Sept 2010) from Computer Science at the University of California, San Diego, advised by Sanjoy Dasgupta. I then was a researcher at the Max Planck Institute for Intelligent Systems. At the MPI I worked in the department of Bernhard Schoelkopf, in the learning theory group of Ulrike von Luxburg. Following this, I spent a couple years as an Assistant Research Professor at the Toyota Technological Institute at Chicago. I then spent some fun 4.5 years at ORFE, Princeton University as Assistant Professor.