World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
34
Citations
14539
World Ranking
11872
National Ranking
296

Mila Nikolova 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 Mila Nikolova 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: 126 publications — 17th percentile

17% of scientists in this discipline score the same or lower.

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

Mila Nikolova 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 Mila Nikolova 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: 34 D-Index — 16th percentile

16% of scientists in this discipline score the same or lower.

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

Overview

Mila Nikolova was affiliated with the École Normale Supérieure Paris-Saclay in France. Their research spanned multiple fields, primarily focusing on Engineering and Earth and Planetary Sciences. Subfields included Computational Theory and Mathematics, Computational Mechanics, Geometry and Topology, Global and Planetary Change, and Aerospace Engineering.

The main topics covered in their work comprised Optimization and Variational Analysis, Sparse and Compressive Sensing Techniques, Fixed Point Theorems Analysis, Advanced Aircraft Design and Technologies, Rocket and Propulsion Systems Research, and Advanced Combustion Engine Technologies, as well as Remote-Sensing Image Classification.

The scientist authored several papers published in notable venues. Some of their recent publications included:

  • A Characterization of Proximity Operators (2020) in the Journal of Mathematical Imaging and Vision
  • Comparative study of the energy efficiency of diesel and hybrid powered light aircraft (2020) in IOP Conference Series Materials Science and Engineering
  • A two-stage method for spectral-spatial classification of hyperspectral images (2020) in the Journal of Mathematical Imaging and Vision
  • DETERMINATION OF DEPTH PARAMETERS OF SUBSURFACE VOIDS USING GRAVIMETRIC AND GLOBAL NAVIGATION SATELITTE SYSTEMS MEASUREMENTS (2024) in International Multidisciplinary Scientific GeoConference SGEM...
  • Application of artificial intelligence and GIS for integrated modeling of processes and factors related to the greening of urban environments (2025) in Acta Scientifica Naturalis

The scientist collaborated frequently with several coauthors, including:

  • Rémi Gribonval
  • Vladimir Serbezov
  • Raymond H. Chan
  • Kelvin K.W. Kan
  • Robert J. Plemmons

The publication venues where Mila Nikolova most often appeared included:

  • Journal of Mathematical Imaging and Vision
  • IOP Conference Series Materials Science and Engineering
  • International Multidisciplinary Scientific GeoConference SGEM...
  • Acta Scientifica Naturalis

Best Publications

  • An Algorithm for Total Variation Minimization and Applications

    Mila Nikolova

  • Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization

    R.H. Chan;Chung-Wa Ho;M. Nikolova

  • ALGORITHMS FOR FINDING GLOBAL MINIMIZERS OF IMAGE SEGMENTATION AND DENOISING MODELS

    Tony F. Chan;Selim Esedoglu;Mila Nikolova

  • A Variational Approach to Remove Outliers and Impulse Noise

    Mila Nikolova

  • Analysis of Half-Quadratic Minimization Methods for Signal and Image Recovery

    Mila Nikolova;Michael K. Ng

  • Minimizers of Cost-Functions Involving Nonsmooth Data-Fidelity Terms. Application to the Processing of Outliers

    Mila Nikolova

  • Local Strong Homogeneity of a Regularized Estimator

    Mila Nikolova

  • Fast Nonconvex Nonsmooth Minimization Methods for Image Restoration and Reconstruction

    Mila Nikolova;Michael K Ng;Chi-Pan Tam

  • An iterative procedure for removing random-valued impulse noise

    R.H. Chan;Chen Hu;M. Nikolova

  • Efficient Reconstruction of Piecewise Constant Images Using Nonsmooth Nonconvex Minimization

    Mila Nikolova;Michael K. Ng;Shuqin Zhang;Wai-Ki Ching

  • Two-phase approach for deblurring images corrupted by impulse plus gaussian noise

    Jian-Feng Cai;Raymond H. Chan;Mila Nikolova

  • Efficient Minimization Methods of Mixed l 2- l 1 and l 1- l 1 Norms for Image Restoration

    Haoying Fu;Michael K. Ng;Mila Nikolova;Jesse L. Barlow

  • Multiplicative Noise Removal Using L1 Fidelity on Frame Coefficients

    Sylvain Durand;Jalal Fadili;Mila Nikolova

  • Fast Two-Phase Image Deblurring Under Impulse Noise

    Jian-Feng Cai;Raymond H. Chan;Mila Nikolova

  • ANALYSIS OF THE RECOVERY OF EDGES IN IMAGES AND SIGNALS BY MINIMIZING NONCONVEX REGULARIZED LEAST-SQUARES ∗

    Mila Nikolova

  • The Equivalence of Half-Quadratic Minimization and the Gradient Linearization Iteration

    M. Nikolova;R.H. Chan

  • Landmark Matching via Large Deformation Diffeomorphisms on the Sphere

    Joan Glaunès;Marc Vaillant;Michael I. Miller

  • Markovian reconstruction using a GNC approach

    M. Nikolova

  • Weakly Constrained Minimization: Application to the Estimation of Images and Signals Involving Constant Regions

    Mila Nikolova

  • Fast Hue and Range Preserving Histogram: Specification: Theory and New Algorithms for Color Image Enhancement.

    Mila Nikolova;Gabriele Steidl

  • Penalized likelihood regression for generalized linear models with non-quadratic penalties

    Anestis Antoniadis;Irène Gijbels;Mila Nikolova

Frequent Co-Authors

Raymond H. Chan
Raymond H. Chan Lingnan University
Michael K. Ng
Michael K. Ng Hong Kong Baptist University
Gabriele Steidl
Gabriele Steidl Technical University of Berlin
Robert J. Plemmons
Robert J. Plemmons Wake Forest University
Pierre Duhamel
Pierre Duhamel CentraleSupélec
Rémi Gribonval
Rémi Gribonval École Normale Supérieure de Lyon
Wai-Ki Ching
Wai-Ki Ching University of Hong Kong
Rachid Deriche
Rachid Deriche French Institute for Research in Computer Science and Automation - INRIA
Jalal M. Fadili
Jalal M. Fadili École Nationale Supérieure d'Ingénieurs de Caen
Tieyong Zeng
Tieyong Zeng Chinese University of Hong Kong

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