World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
65
Citations
13947
World Ranking
1548
National Ranking
506

Barnabás Póczos publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Barnabás Póczos sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 251 publications — 64th percentile

64% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Barnabás Póczos D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Barnabás Póczos sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 65 D-Index — 85th percentile

85% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Barnabás Póczos is affiliated with Carnegie Mellon University in the United States. Their research is primarily situated within the field of Computer Science, with a significant focus on Artificial Intelligence. Other subfields of study include Molecular Biology, Computer Vision and Pattern Recognition, Computational Mechanics, and Materials Chemistry.

Their work covers a range of topics including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Sparse and Compressive Sensing Techniques
  • Domain Adaptation and Few-Shot Learning
  • Machine Learning in Materials Science
  • Cell Image Analysis Techniques
  • Multimodal Machine Learning Applications

Frequent coauthors collaborating with Barnabás Póczos include:

  • Newell R. Washburn
  • Emmanouil Antonios Platanios
  • Siamak Ravanbakhsh
  • Chunliang Li
  • Euxhen Hasanaj

Publication venues where Barnabás Póczos has frequently contributed are:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Zenodo (CERN European Organization for Nuclear Research)
  • Cell Reports Physical Science

Recent papers authored or coauthored by Barnabás Póczos include:

  • Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine Learning, 2020, Cell Reports Physical Science
  • Hierarchical Machine Learning for High-Fidelity 3D Printed Biopolymers, 2020, ACS Biomaterials Science & Engineering
  • Deep generative models for galaxy image simulations, 2021, Monthly Notices of the Royal Astronomical Society
  • Diffusion Models in De Novo Drug Design, 2024, Journal of Chemical Information and Modeling
  • End-to-end jet classification of quarks and gluons with the CMS Open Data, 2020, Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment

Best Publications

  • Deep Sets

    Manzil Zaheer;Satwik Kottur;Siamak Ravanbakhsh;Barnabas Poczos

  • Gradient Descent Provably Optimizes Over-parameterized Neural Networks

    Simon S. Du;Xiyu Zhai;Barnabas Poczos;Aarti Singh

  • MMD GAN: Towards Deeper Understanding of Moment Matching Network

    Chun-Liang Li;Wei-Cheng Chang;Yu Cheng;Yiming Yang

  • Stochastic variance reduction for nonconvex optimization

    Sashank J. Reddi;Ahmed Hefny;Suvrit Sra;Barnabás Póczós

  • Neural Architecture Search with Bayesian Optimisation and Optimal Transport

    Kirthevasan Kandasamy;Willie Neiswanger;Jeff Schneider;Barnabas Poczos

  • Characterizing and Avoiding Negative Transfer

    Zirui Wang;Zihang Dai;Barnabas Poczos;Jaime Carbonell

  • Found in Translation: Learning Robust Joint Representations by Cyclic Translations between Modalities

    Hai Pham;Paul Pu Liang;Thomas Manzini;Louis-Philippe Morency

  • One Network to Solve Them All — Solving Linear Inverse Problems Using Deep Projection Models

    J. H. Rick Chang;Chun-Liang Li;Barnabas Poczos;B. V. K. Vijaya Kumar

  • Competence-based Curriculum Learning for Neural Machine Translation

    Emmanouil Antonios Platanios;Otilia Stretcu;Graham Neubig;Barnabás Póczos

  • High Dimensional Bayesian Optimisation and Bandits via Additive Models

    Kirthevasan Kandasamy;Jeff Schneider;Barnabas Poczos

  • CMU DeepLens: deep learning for automatic image-based galaxy-galaxy strong lens finding

    Francois Lanusse;Quanbin Ma;Nan Li;Nan Li;Thomas E. Collett

  • Learning to predict the cosmological structure formation.

    Siyu He;Yin Li;Yu Feng;Yu Feng;Shirley Ho

  • Gradient Descent Can Take Exponential Time to Escape Saddle Points

    Simon S. Du;Chi Jin;Jason D. Lee;Michael I. Jordan

  • Predicting enhancer-promoter interaction from genomic sequence with deep neural networks

    Shashank Singh;Yang Yang;Barnabás Póczos;Jian Ma

  • Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization

    Sashank J. Reddi;Suvrit Sra;Barnabas Poczos;Alexander J. Smola

  • Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints.

    Fang-Cheng Yeh;Jean M. Vettel;Aarti Singh;Barnabás Póczos

  • On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions

    Aaditya Ramdas;Sashank J. Reddi;Barnabás Póczos;Aarti Singh

  • On variance reduction in stochastic gradient descent and its asynchronous variants

    Sashank J. Reddi;Ahmed Hefny;Suvrit Sra;Barnabás Pöczos

  • Gradient Descent Learns One-hidden-layer CNN: Don’t be Afraid of Spurious Local Minima

    Simon S. Du;Jason D. Lee;Yuandong Tian;Barnabas Poczos

  • Estimation of Rényi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs

    Dávid Pál;Barnabás Póczos;Csaba Szepesvári

  • Parallelised Bayesian Optimisation via Thompson Sampling

    Kirthevasan Kandasamy;Akshay Krishnamurthy;Jeff Schneider;Barnabás Póczos

  • Multi-fidelity Bayesian optimisation with continuous approximations

    Kirthevasan Kandasamy;Gautam Dasarathy;Jeff Schneider;Barnabás Póczos

  • Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels

    Simon S. Du;Kangcheng Hou;Russ R. Salakhutdinov;Barnabas Poczos

Frequent Co-Authors

Jeff Schneider
Jeff Schneider Carnegie Mellon University
Aarti Singh
Aarti Singh Carnegie Mellon University
Sashank J. Reddi
Sashank J. Reddi Google (United States)
Arthur Gretton
Arthur Gretton University College London
Eric P. Xing
Eric P. Xing Mohamed bin Zayed University of Artificial Intelligence
Alexander J. Smola
Alexander J. Smola Amazon (United States)
Larry Wasserman
Larry Wasserman Carnegie Mellon University
Simon S. Du
Simon S. Du University of Washington
Ruslan Salakhutdinov
Ruslan Salakhutdinov Carnegie Mellon University

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