Nikhil Naik

5.0k total citations · 2 hit papers
37 papers, 2.3k citations indexed

About

Nikhil Naik is a scholar working on Computer Vision and Pattern Recognition, Instrumentation and Artificial Intelligence. According to data from OpenAlex, Nikhil Naik has authored 37 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 8 papers in Instrumentation and 8 papers in Artificial Intelligence. Recurrent topics in Nikhil Naik's work include Advanced Optical Sensing Technologies (8 papers), Random lasers and scattering media (7 papers) and Urban, Neighborhood, and Segregation Studies (5 papers). Nikhil Naik is often cited by papers focused on Advanced Optical Sensing Technologies (8 papers), Random lasers and scattering media (7 papers) and Urban, Neighborhood, and Segregation Studies (5 papers). Nikhil Naik collaborates with scholars based in United States, India and United Kingdom. Nikhil Naik's co-authors include Ramesh Raskar, César A. Hidalgo, Ali Madani, Richard Socher, Andre Esteva, Jade Philipoom, Ali Mottaghi, Jeff Dean, Katherine Chou and Yun Liu and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and American Economic Review.

In The Last Decade

Nikhil Naik

33 papers receiving 2.2k citations

Hit Papers

Deep learning-enabled medical compu... 2014 2026 2018 2022 2021 2014 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep learning-enabled medical computer vision
    2021 718 citations Andre Esteva, Katherine Chou et al. npj Digital Medicine profile →
  2. Streetscore -- Predicting the Perceived Safety of One Million Streetscapes
    2014 291 citations Nikhil Naik, Jade Philipoom et al. DSpace@MIT (Massachusetts Institute of Technology) profile →

Peers

Nikhil Naik
Shan Liu China
Guoping Qiu United Kingdom
Guoqiang Zhong China
Bo Yang China
Hui Yuan China
Xavier Puig Spain
Peng Wang China
Mei Chen China
Shan Liu China
Nikhil Naik
Citations per year, relative to Nikhil Naik Nikhil Naik (= 1×) peers Shan Liu

Countries citing papers authored by Nikhil Naik

Since Specialization
Citations

This map shows the geographic impact of Nikhil Naik's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nikhil Naik with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nikhil Naik more than expected).

Fields of papers citing papers by Nikhil Naik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nikhil Naik. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nikhil Naik. The network helps show where Nikhil Naik may publish in the future.

Co-authorship network of co-authors of Nikhil Naik

This figure shows the co-authorship network connecting the top 25 collaborators of Nikhil Naik. A scholar is included among the top collaborators of Nikhil Naik based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nikhil Naik. Nikhil Naik is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Naik, Nikhil, et al.. (2024). ARDC-UNet retinal vessel segmentation with adaptive residual deformable convolutional based U-Net. Multimedia Tools and Applications. 83(33). 78747–78768. 2 indexed citations
2.
Naik, Nikhil, Mayuri A. Mehta, & Vivek Joshi. (2024). Proximal Femur Segmentation from the Fluoroscopy Image Using DeeplabV3+ Model. 671–676. 1 indexed citations
3.
4.
Ermon, Stefano, et al.. (2023). End-to-End Diffusion Latent Optimization Improves Classifier Guidance. 7246–7256. 19 indexed citations
5.
Hwang, Jackelyn & Nikhil Naik. (2023). Systematic Social Observation at Scale: Using Crowdsourcing and Computer Vision to Measure Visible Neighborhood Conditions. Sociological Methodology. 53(2). 183–216. 13 indexed citations
6.
7.
Esteva, Andre, Katherine Chou, Serena Yeung, et al.. (2021). Deep learning-enabled medical computer vision. npj Digital Medicine. 4(1). 5–5. 718 indexed citations breakdown →
8.
Naik, Nikhil, Ali Madani, Andre Esteva, et al.. (2020). Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains. Nature Communications. 11(1). 5727–5727. 161 indexed citations
9.
Naik, Nikhil & Pierluigi Nuzzo. (2020). Robustness Contracts for Scalable Verification of Neural Network-Enabled Cyber-Physical Systems. 1–12. 5 indexed citations
10.
Glaeser, Edward L., et al.. (2018). Computer Vision and Real Estate: Do Looks Matter and Do Incentives Determine Looks. National Bureau of Economic Research. 1 indexed citations
11.
Naik, Nikhil, Scott Duke Kominers, Ramesh Raskar, Edward L. Glaeser, & César A. Hidalgo. (2017). Computer vision uncovers predictors of physical urban change. Proceedings of the National Academy of Sciences. 114(29). 7571–7576. 197 indexed citations
12.
Satat, Guy, Barmak Heshmat, Nikhil Naik, Albert Redo-Sanchez, & Ramesh Raskar. (2016). Advances in ultrafast optics and imaging applications. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9835. 98350Q–98350Q. 4 indexed citations
13.
Baker, Bowen, Otkrist Gupta, Nikhil Naik, & Ramesh Raskar. (2016). Designing Neural Network Architectures using Reinforcement Learning. arXiv (Cornell University). 210 indexed citations
14.
Nadai, Marco De, Radu L. Vieriu, Gloria Zen, et al.. (2016). Are Safer Looking Neighborhoods More Lively?. Institutional Research Information System (Università degli Studi di Trento). 1127–1135. 37 indexed citations
15.
Naik, Nikhil, Scott Duke Kominers, Ramesh Raskar, Edward L. Glaeser, & César A. Hidalgo. (2015). Do People Shape Cities, or Do Cities Shape People? The Co-Evolution of Physical, Social, and Economic Change in Five Major U.S. Cities. SSRN Electronic Journal. 3 indexed citations
16.
Naik, Nikhil, Achuta Kadambi, Christoph Rhemann, et al.. (2015). A light transport model for mitigating multipath interference in Time-of-flight sensors. 73–81. 60 indexed citations
17.
Naik, Nikhil, Jade Philipoom, Ramesh Raskar, & César A. Hidalgo. (2014). Streetscore -- Predicting the Perceived Safety of One Million Streetscapes. DSpace@MIT (Massachusetts Institute of Technology). 793–799. 291 indexed citations breakdown →
18.
Naik, Nikhil, Shuang Zhao, Andreas Velten, Ramesh Raskar, & Kavita Bala. (2011). Single view reflectance capture using multiplexed scattering and time-of-flight imaging. ACM Transactions on Graphics. 30(6). 1–10. 55 indexed citations
19.
Naik, Nikhil, Shuang Zhao, Andreas Velten, Ramesh Raskar, & Kavita Bala. (2011). Single view reflectance capture using multiplexed scattering and time-of-flight imaging.
20.
Naik, Nikhil, et al.. (2009). A fragment based scale adaptive tracker with partial occlusion handling. 32. 1–6. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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