World's Best Scientists 2026 revealed!
Manik Varma

Manik Varma

D-Index & Metrics

Computer Science

D-Index
30
Citations
11743
World Ranking
13814
National Ranking
162

Manik Varma publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Manik Varma sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 72 publications — 2nd percentile

2% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Manik Varma D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Manik Varma sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 30 D-Index — 3rd percentile

3% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Research.com Recognitions

  • Fellow of the Indian National Academy of Engineering (INAE)
  • Fellow of the Indian National Academy of Engineering (INAE)
  • Fellow of the Indian National Academy of Engineering (INAE)
  • Fellow of the Indian National Academy of Engineering (INAE)

Overview

Manik Varma is affiliated with Microsoft (India) and has contributed extensively to the field of computer science, with a primary focus on artificial intelligence and computer vision. Their research spans multiple interconnected areas including machine learning, natural language processing, and smart agriculture.

Their main fields of study include:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Molecular Biology
  • Plant Science
  • Materials Chemistry

The key research topics explored in their work include:

  • Machine Learning in Bioinformatics
  • Smart Agriculture and AI
  • Text and Document Classification Technologies
  • Machine Learning and Data Classification
  • Topic Modeling
  • Natural Language Processing Techniques
  • Domain Adaptation and Few-Shot Learning

Manik Varma's publication record features research articles primarily published in venues such as arXiv (Cornell University), the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, ACM Transactions on Sensor Networks, IEEE Sensors Journal, and the Journal of Crop and Weed.

Recent publications include:

  • "RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference" (2020), arXiv (Cornell University)
  • "Multi-modal Extreme Classification" (2022), 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • "One Size Does Not Fit All" (2021), ACM Transactions on Sensor Networks
  • "DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents" (2021), arXiv (Cornell University)
  • "Exploring the suitability of machine learning algorithms for crop yield forecasting using weather variables" (2022), Journal of Crop and Weed

The scientist has frequently collaborated with several coauthors including:

  • Sumeet Agarwal
  • Kunal Dahiya
  • Purushottam Kar
  • Deepak Saini
  • Kushal Dave

Manik Varma has been recognized as a Fellow of the Indian National Academy of Engineering (INAE), indicating a level of peer acknowledgment within the engineering community in India.

Best Publications

  • A Statistical Approach to Texture Classification from Single Images

    Manik Varma;Andrew Zisserman

  • Multiple kernels for object detection

    Andrea Vedaldi;Varun Gulshan;Manik Varma;Andrew Zisserman

  • A Statistical Approach to Texture Classification from Single Images

    Unknown

  • A Statistical Approach to Material Classification Using Image Patch Exemplars

    M. Varma;A. Zisserman

  • Learning The Discriminative Power-Invariance Trade-Off

    M. Varma;D. Ray

  • Texture classification: are filter banks necessary?

    M. Varma;A. Zisserman

  • CHARACTER RECOGNITION IN NATURAL IMAGES

    Teófilo Emídio de Campos;Bodla Rakesh Babu;Manik Varma

  • More generality in efficient multiple kernel learning

    Manik Varma;Bodla Rakesh Babu

  • Classifying Images of Materials: Achieving Viewpoint and Illumination Independence

    Manik Varma;Andrew Zisserman

  • Sparse local embeddings for extreme multi-label classification

    Kush Bhatia;Himanshu Jain;Purushottam Kar;Manik Varma

  • FastXML: a fast, accurate and stable tree-classifier for extreme multi-label learning

    Yashoteja Prabhu;Manik Varma

  • Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications

    Himanshu Jain;Yashoteja Prabhu;Manik Varma

  • Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages

    Rahul Agrawal;Archit Gupta;Yashoteja Prabhu;Manik Varma

  • Multiple Kernel Learning and the SMO Algorithm

    Zhaonan Sun;Nawanol Ampornpunt;Manik Varma;S.v.n. Vishwanathan

  • Parabel: Partitioned Label Trees for Extreme Classification with Application to Dynamic Search Advertising

    Yashoteja Prabhu;Anil Kag;Shrutendra Harsola;Rahul Agrawal

  • Resource-efficient machine learning in 2 KB RAM for the internet of things

    Ashish Kumar;Saurabh Goyal;Manik Varma

  • Locally Invariant Fractal Features for Statistical Texture Classification

    M. Varma;R. Garg

  • Large Scale Max-Margin Multi-Label Classification with Priors

    Bharath Hariharan;Lihi Zelnik-manor;Manik Varma;S.v.n. Vishwanathan

  • FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network

    Aditya Kusupati;Manish Singh;Kush Bhatia;Ashish Kumar

  • Learning to re-rank: query-dependent image re-ranking using click data

    Vidit Jain;Manik Varma

  • ProtoNN: compressed and accurate kNN for resource-scarce devices

    Chirag Gupta;Arun Sai Suggala;Ankit Goyal;Harsha Vardhan Simhadri

Frequent Co-Authors

Prateek Jain
Prateek Jain Google (United States)
Andrew Zisserman
Andrew Zisserman University of Oxford
Anish Arora
Anish Arora The Ohio State University
S. V. N. Vishwanathan
S. V. N. Vishwanathan Purdue University West Lafayette
C. V. Jawahar
C. V. Jawahar International Institute of Information Technology, Hyderabad
Kentaro Toyama
Kentaro Toyama University of Michigan–Ann Arbor
Jitendra Malik
Jitendra Malik University of California, Berkeley
A. G. Ramakrishnan
A. G. Ramakrishnan Indian Institute of Science
Andrea Vedaldi
Andrea Vedaldi University of Oxford
Thorsten Joachims
Thorsten Joachims Cornell University

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