World's Best Scientists 2026 revealed!
Balaji Lakshminarayanan

Balaji Lakshminarayanan

D-Index & Metrics

Computer Science

D-Index
36
Citations
15361
World Ranking
10964
National Ranking
4558

Balaji Lakshminarayanan 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 Balaji Lakshminarayanan 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: 250 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: 560 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: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 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: 88 publications — 5th percentile

5% of scientists in this discipline score the same or lower.

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

Balaji Lakshminarayanan 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 Balaji Lakshminarayanan sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 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: 36 D-Index — 23rd percentile

23% of scientists in this discipline score the same or lower.

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

Overview

Balaji Lakshminarayanan is affiliated with Google in the United States and works primarily in the field of Computer Science. Their research spans various specialized subfields, including Artificial Intelligence, Computer Vision and Pattern Recognition, Global and Planetary Change, Water Science and Technology, and Control and Systems Engineering.

The scientist's scholarly contributions focus on multiple main topics:

  • Adversarial Robustness in Machine Learning
  • Anomaly Detection Techniques and Applications
  • Domain Adaptation and Few-Shot Learning
  • Advanced Neural Network Applications
  • Machine Learning and Data Classification
  • Topic Modeling
  • Flood Risk Assessment and Management

Balaji Lakshminarayanan has authored numerous papers, with a notable concentration of publications in arXiv (Cornell University). Other venues include Environmental Science and Pollution Research, Machine Learning, Journal of Water and Climate Change, and Medical Image Analysis.

Recent publications include:

  • "Gemini: A Family of Highly Capable Multimodal Models" (2023) published in arXiv (Cornell University)
  • "Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness" (2020) published in arXiv (Cornell University)
  • "Exploring the Limits of Out-of-Distribution Detection" (2021) published in arXiv (Cornell University)
  • "Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift" (2020) published in arXiv (Cornell University)

The scientist frequently collaborates with other researchers, with notable co-authors such as Dustin Tran, Jie Ren, Jasper Snoek, Jeremiah Zhe Liu, and Shreyas Padhy. Their co-authorship counts indicate strong collaborative relationships, particularly with Dustin Tran and Jie Ren.

Best Publications

  • Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

    Balaji Lakshminarayanan;Alexander Pritzel;Charles Blundell

  • Clinically applicable deep learning for diagnosis and referral in retinal disease

    Jeffrey De Fauw;Joseph R. Ledsam;Bernardino Romera-Paredes;Stanislav Nikolov

  • Normalizing flows for probabilistic modeling and inference

    George Papamakarios;Eric T. Nalisnick;Danilo Jimenez Rezende;Shakir Mohamed

  • Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

    Unknown

  • Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift

    Yaniv Ovadia;Emily Fertig;Jie Ren;Zachary Nado

  • AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

    Dan Hendrycks;Norman Mu;Ekin Dogus Cubuk;Barret Zoph

  • Do Deep Generative Models Know What They Don't Know?

    Eric T. Nalisnick;Akihiro Matsukawa;Yee Whye Teh;Dilan Görür

  • Deep Ensembles: A Loss Landscape Perspective

    Stanislav Fort;Huiyi Hu;Balaji Lakshminarayanan

  • Likelihood Ratios for Out-of-Distribution Detection

    Jie Ren;Peter J. Liu;Emily Amanda Fertig;Jasper Roland Snoek

  • Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach

    Forrest Briggs;Balaji Lakshminarayanan;Lawrence Neal;Xiaoli Z. Fern

  • Variational Approaches for Auto-Encoding Generative Adversarial Networks.

    Mihaela Rosca;Balaji Lakshminarayanan;David Warde-Farley;Shakir Mohamed

  • The Cramer Distance as a Solution to Biased Wasserstein Gradients

    Marc G. Bellemare;Ivo Danihelka;Will Dabney;Shakir Mohamed

  • Learning in Implicit Generative Models

    Shakir Mohamed;Balaji Lakshminarayanan

  • Mondrian Forests: Efficient Online Random Forests

    Balaji Lakshminarayanan;Daniel M Roy;Yee Whye Teh

  • Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step

    William Fedus;Mihaela Rosca;Balaji Lakshminarayanan;Andrew M. Dai

  • Adapting Auxiliary Losses Using Gradient Similarity

    Yunshu Du;Wojciech M. Czarnecki;Siddhant M. Jayakumar;Razvan Pascanu

  • Plex: Towards Reliability using Pretrained Large Model Extensions

    Unknown

  • Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness

    Jeremiah Zhe Liu;Zi Lin;Shreyas Padhy;Dustin Tran

  • Exploring the Limits of Out-of-Distribution Detection

    Stanislav Fort;Jie Ren;Balaji Lakshminarayanan

  • Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

    Zachary Nado;Shreyas Padhy;D. Sculley;Alexander D'Amour

  • Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

    Eric Nalisnick;Akihiro Matsukawa;Yee Whye Teh;Balaji Lakshminarayanan

  • Does your dermatology classifier know what it doesn't know? Detecting the long-tail of unseen conditions.

    Abhijit Guha Roy;Jie Ren;Shekoofeh Azizi;Aaron Loh

  • Bayesian Deep Ensembles via the Neural Tangent Kernel

    Bobby He;Balaji Lakshminarayanan;Yee Whye Teh

Frequent Co-Authors

Yee Whye Teh
Yee Whye Teh University of Oxford
Jasper Snoek
Jasper Snoek Google (United States)
Dustin Tran
Dustin Tran Google (United States)
D. Sculley
D. Sculley Google (United States)
Charles Blundell
Charles Blundell DeepMind (United Kingdom)
Arthur Gretton
Arthur Gretton University College London
Nicolas Heess
Nicolas Heess DeepMind (United Kingdom)
Razvan Pascanu
Razvan Pascanu DeepMind (United Kingdom)
András György
András György New York University Abu Dhabi
Ian Goodfellow
Ian Goodfellow Google (United States)

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