Nikhil Naik

I am a Lead Research Scientist at Salesforce Research. My research interests are in computer vision, machine learning, and their applications in sciences. I have worked on problems in scene understanding, representation learning, and AutoML; and on applying machine learning to problems in economics and biology.

Previously, I was a Prize Fellow at Harvard University. I obtained my PhD from MIT in 2017, where my advisor was Ramesh Raskar. My research has appeared in premier AI conferences along with interdisciplinary journals and has been featured by media outlets including The Atlantic, The Economist, MIT Technology Review, and New York Times. Email: naik[at] /  Google Scholar /  CV /  Twitter


  • (03/21) Presenting our work on ReceptorNet at NVIDIA GTC 2021.
  • (03/21) Paper on grounded self-supervised learning accepted at CVPR 2021!
  • (01/21) ReceptorNet featured in news articles and on Salesforce homepage.

  • Interns and Students Supervised/Co-supervised

    Isabela Albuquerque (U. Montreal), Ankan Bansal (U. Maryland), Abhimanyu Dubey (IIT Delhi→MIT), Bowen Baker (MIT→OpenAI, Winner of 2nd best CS masters thesis at MIT), Karan Dwivedi (Harvard), Otkrist Gupta (MIT→Startup), Jade Philipoom (MIT)

    Selected Publications and Preprints

    See Google Scholar for full list

    CASTing Your Model: Learning to Localize Improves Self-Supervised Representations

    Ramprasaath R. Selvaraju*, Karan Desai*, Justin Johnson, Nikhil Naik   CVPR 2021 (To appear)
    Paper /  Blog Intelligent crop sampling and Grad-CAM supervision improves localization and downstream performance of SSL models

    Improving Out-of-distribution Generalization via Multi-task Self-supervised Pretraining

    Isabela Albuquerque, Nikhil Naik, Junnan Li, Nitish Keskar, Richard Socher    arXiv preprint 2020
    Paper  A CNN trained with multiple complementary self-supervised learning tasks improves localization and OOD performance

    ProGen: Language Modeling for Protein Generation

    Ali Madani, Bryan McCann, Nikhil Naik, Nitish Keskar, Namrata Anand, Raphael Eguchi, Possu Huang, Richard Socher   
    arXiv preprint 2020   Paper /  Blog A language model successfully generate tailored protein sequences that appear structurally and functionally viable

    The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies

    Stephan Zheng, Alex Trott, Sunil Srinivasa, Nikhil Naik, Melvin Gruesbeck, David Parkes, Richard Socher   
    arXiv preprint 2020   Paper /  Blog /  Code Two-level reinforcement learning can be used to set optimal tax policies in simulated economies

    Deep Learning-enabled Breast Cancer Hormonal Receptor Status Determination from Base-level H&E Stains

    Nikhil Naik, Ali Madani*, Andre Esteva*, Nitish Keskar, Michael Press, Dan Ruderman, David Agus, Richard Socher  
    Nature Communications 2020   Paper /  Blog Deep learning can make hormone therapy decisions from H&E pathology images, without needing more complex IHC testing

    Maximum-Entropy Fine Grained Classification

    Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, Nikhil Naik   NeurIPS 2018
    Paper  /  Code Maximizing entropy of the output probability distribution for training CNNs helps tackle intra-class similarity in FGVC

    Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life

    Edward Glaeser, Scott Kominers, Michael Luca, Nikhil Naik   Economic Enquiry 2018
    Paper (Winner of the 2018 Best EI Article Award) /   Press:  The Atlantic   Chicago Policy Review   HBS Working Knowledge   Computer vision can predict important socioeconomic characteristics from street view images

    Pairwise Confusion for Fine-grained Visual Classification

    Abhimanyu Dubey, Otkrist Gupta, Pei Guo, Ryan Farrell, Ramesh Raskar, Nikhil Naik   ECCV 2018
    Paper  /  Code Reducing overfitting in neural net training by intentionally introducing confusion in activations improves FGVC performance

    Accelerating Neural Architecture Search using Performance Prediction

    Bowen Baker*, Otkrist Gupta*, Ramesh Raskar, Nikhil Naik   ICLR Workshops 2018
    Paper  /  Code Early-stopping based on final performance prediction of partially trained neural networks accelerates architecture search

    Computer Vision Uncovers Predictors of Physical Urban Change

    Nikhil Naik, Scott Kominers, Ramesh Raskar, Edward Glaeser, Cesar Hidalgo   PNAS 2017
    Paper  /  Website (Winner of 2018 Webby Award for the best use of machine learning on the Internet)
    Press:  Citylab   Fast Company   Forbes   Harvard Gazette   MIT News   New York Times   Quartz 
    Computer vision measures urban change from time-series street view images, enabling economic analysis of urban dynamics

    Green streets− Quantifying and Mapping Urban Trees with Street-level Imagery and Computer Vision

    Ian Seiferling, Nikhil Naik, Carlo Ratti, Raphäel Proulx    Landscape and Urban Planning 2017
    Paper  Computer vision can create detailed maps of urban vegetation using street view images

    Designing Neural Network Architectures using Reinforcement Learning

    Bowen Baker*, Otkrist Gupta*, Nikhil Naik, Ramesh Raskar   ICLR 2017
    Paper  /  Code  /  Press: MIT Technology Review  A reinforcement learning agent can automatically generate high-performing CNN architectures

    Deep Learning the City: Quantifying Urban Perception At A Global Scale

    Abhimanyu Dubey, Nikhil Naik, Devi Parikh, Ramesh Raskar, Cesar Hidalgo   ECCV 2016
    Paper  /  Code A neural network predicts perceptual attributes of the built environment from hundreds of cities from six continents

    Cities Are Physical Too: Using Computer Vision to Measure the Quality and Impact of Urban Appearance

    Nikhil Naik, Ramesh Raskar, Cesar Hidalgo   American Economic Review: Papers and Proceedings 2016
    Paper  Computer vision-driven prediction of urban appearance enables studies of its quality and impact on society

    A Light Transport Model for Mitigating Multipath Interference in Time-of-flight Sensors

    Nikhil Naik, Achuta Kadambi, Christoph Rhemann, Shahram Izadi, Ramesh Raskar, Sing Bing Kang   CVPR 2015
    Paper  /  Supplement Separating global and direct components of light transport can reduce multipath interference

    Estimating Wide-angle, Spatially Varying Reflectance using Time-resolved Inversion of Backscattered Light

    Nikhil Naik, Christopher Barsi, Andreas Velten, Ramesh Raskar   JOSA A 2014
    Paper (Selected by Editors to appear in a Special Issue of Virtual Journal of Biomedical Optics) A trillion-frames-per-second camera can measure the reflectance profile of objects imaged through a diffuser

    Streetscore – Predicting the Perceived Safety of One Million Streetscapes

    Nikhil Naik, Jade Philipoom, Ramesh Raskar, Cesar Hidalgo   CVPR Workshops 2014
    Paper /  Website  /  Data  /  Press:   Daily Mail   The Economist   Fast Company   Gizmodo   A computer vision algorithm, trained with an online participatory game, accurately predicts human perception of streetscapes

    Frequency Analysis of Transient Light Transport with Applications in Bare Sensor Imaging

    Di Wu, Gordon Wetzstein, Christopher Barsi, Matthew O’Toole, Nikhil Naik, Kyros Kutulakos, Ramesh Raskar    ECCV 2012
    Paper  Analyzing free space propagation in the frequency domain leads to a new, time-resolved bare sensor imaging system

    Single View Reflectance Capture using Multiplexed Scattering and Time-of-flight Imaging

    Nikhil Naik, Shuang Zhao, Andreas Velten, Ramesh Raskar, Kavita Bala    ACM SIGGRAPH ASIA 2011
    Paper  A trillion-frames-per-second camera can measure the reflectance profile of objects by analyzing indirectly scattered light