Computer Science
Nakul Verma studies machine learning and high-dimensional statistics. He focuses on understanding and exploiting the intrinsic structure in data to design effective learning algorithms. His work has produced the first provably correct approximate distance-preserving embeddings for manifolds from finite samples, and has provided improved sample complexity results in various learning paradigms, such as metric learning and multiple-instance learning.
- Teaching Faculty, Columbia University, 2017–
- Research Specialist, Janelia Research Campus, HHMI, 2013–2017
- Research Scientist, Amazon, 2012–2013
- Research Intern, Yahoo Labs, 2011
- Research Intern, Qualcomm, 2008
- Institute of Electrical and Electronics Engineers (IEEE)
- International Machine Learning Society (IMLS)
- ICML Reviewer Award, 2015
- Janelia Teaching Fellowship, 2015
- Best Paper, Wireless Health, 2012
- Samory Kpotufe, Nakul Verma, “Time-accuracy tradeoffs in Kernel prediction: controlling prediction quality”, Journal of Machine Learning Research (JMLR), 2017.
- Nakul Verma, Kristin Branson, “Sample complexity of learning Mahalanobis distance metrics”, Neural Information Processing Systems (NIPS), 2015.
- Nakul Verma, “Distance preserving embeddings for general n-dimensional manifolds”, Journal of Machine Learning Research (JMLR), 2013.
- Nakul Verma, Dhruv Mahajan, Sundararajan Sellamanickam, Vinod Nair, “Learning hierarchical similarity metrics”, Computer Vision and Pattern Recognition (CVPR), 2012.
- Nakul Verma, “A note on random projections for preserving paths on a manifold”, UC San Diego Tech. Report CS2011-0971, 2011.
- Boris Babenko, Nakul Verma, Poitr Dollar, Serge Belongie, “Multiple instance learning with manifold bags”, International Conference in Machine Learning (ICML), 2011.
- Nakul Verma, Samory Kpotufe, Sanjoy Dasgupta, “Which spatial trees are adaptive to intrinsic dimension?”, Uncertainty in Artificial Intelligence (UAI), 2009.
- Yoav Freund, Sanjoy Dasgupta, Mayank Kabra, Nakul Verma, “Learning the structure of manifolds using random projections”, Neural Information Processing Systems (NIPS), 2007.
- Sanjoy Dasgupta, Daniel Hsu, Nakul Verma, “A concentration theorem for projections”, Uncertainty in Artificial Intelligence (UAI), 2006.