Nikhil Naik

I am a Prize Fellow at Harvard University. I obtained my PhD from MIT Media Lab. I develop machine learning tools that generate rich observational and outcome measurements from new sources of digital data to address previously unanswerable social science questions. In my doctoral work, I studied how socioeconomic behavior shapes—and is shaped by—the built environment with computer vision and deep learning algorithms that analyze geospatial imagery at street-level resolution and global scale. Recent examples of my work include Streetchange and Streetscore. My current and past industrial collaborations include Google, Microsoft Research, Mastercard, ExxonMobil, and Samsung.

Email: naik@mit.edu



Selected Papers

  • Computer Vision Uncovers Predictors of Physical Urban Change, Proceedings of the National Academy of Sciences (PNAS) 2017
  • Designing Neural Network Architectures using Reinforcement Learning, International Conference on Learning Representations (ICLR) 2017
  • Deep Learning the City: Quantifying Urban Perception At A Global Scale, European Conference on Computer Vision (ECCV) 2016
  • Streetscore – Predicting the Perceived Safety of One Million Streetscapes, IEEE Computer Vision & Pattern Recognition (CVPR) Workshops 2014
  • Updates

  • (08/17) Streetchange featured on the Harvard Homepage!
  • (07/17) Our paper on Streetchange appears in the Proceedings of the National Academy of Sciences.
  • (06/17) Invited talk on Streetchange at the Stanford Institute for Economic Policy Research.
  • (05/17) Presented our work on neural network meta-modeling at the New England Machine Learning Day organized by Microsoft Research.
  • (04/17) Presented our work on neural network meta-modeling at the International Conference on Learning Representations held in Toulon, France.
  • (03/17) Participated in the Predictive Cities exploratory seminar at the Radcliffe Institute.
  • (02/17) Invited talk on Streetchange at the Center for Mathematical Sciences and Applications at Harvard University.