World's Best Scientists 2026 revealed!
Paolo Frasconi

Paolo Frasconi

D-Index & Metrics

Computer Science

D-Index
49
Citations
21541
World Ranking
5746
National Ranking
131

Paolo Frasconi 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 Paolo Frasconi 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: 183 publications — 40th percentile

40% of scientists in this discipline score the same or lower.

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

Paolo Frasconi 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 Paolo Frasconi 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: 49 D-Index — 60th percentile

60% of scientists in this discipline score the same or lower.

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

Overview

Paolo Frasconi is affiliated with the University of Florence in Italy. Their research spans multiple disciplines, with a primary focus on Medicine and Computer Science. These fields intersect notably in studies related to Cardiology and Cardiovascular Medicine as well as Biophysics, Signal Processing, Radiology, Nuclear Medicine and Imaging, and Artificial Intelligence.

The scientist's work covers several main topics. These include:

  • Atrial Fibrillation Management and Outcomes
  • Cell Image Analysis Techniques
  • Machine Learning and Data Classification
  • Music and Audio Processing
  • Advanced Fluorescence Microscopy Techniques
  • Single-cell and Spatial Transcriptomics
  • Venous Thromboembolism Diagnosis and Management

Recent publications by Paolo Frasconi reflect a variety of research areas and collaborations. Selected papers include:

  • "Universal autofocus for quantitative volumetric microscopy of whole mouse brains," 2021, Nature Methods
  • "Machine learning approach for prediction of outcomes in anticoagulated patients with atrial fibrillation," 2024, International Journal of Cardiology
  • "Deep learning-based localization algorithms on fluorescence human brain 3D reconstruction: a comparative study using stereology as a reference," 2024, Scientific Reports
  • "Two-Dimensional Aortic Size Normalcy: A Novelty Detection Approach," 2021, Diagnostics
  • "Hyperparameter Optimization in Machine Learning," 2024, arXiv (Cornell University)

The venues where Paolo Frasconi frequently publishes include:

  • International Journal of Cardiology
  • arXiv (Cornell University)
  • Nature Methods
  • Scientific Reports
  • Foundations and Trends® in Machine Learning

Collaboration plays a notable role in their research activities. Frequent co-authors are:

  • Luca Bindini
  • Andrea Bernardini
  • Martina Berteotti
  • Betti Giusti
  • Rossella Marcucci

Best Publications

  • Learning long-term dependencies with gradient descent is difficult

    Y. Bengio;P. Simard;P. Frasconi

  • Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning

    M. Lippi;M. Bertini;P. Frasconi

  • Exploiting the past and the future in protein secondary structure prediction.

    Pierre Baldi;Søren Brunak;Paolo Frasconi;Giovanni Soda

  • A general framework for adaptive processing of data structures

    P. Frasconi;M. Gori;A. Sperduti

  • Modeling the Internet and the Web

    Pierre Baldi;Paolo Frasconi;Padhraic Smyth

  • An Input Output HMM Architecture

    Yoshua Bengio;Paolo Frasconi

  • Input-output HMMs for sequence processing

    Y. Bengio;P. Frasconi

  • Modeling the Internet and the Web: Probabilistic Method and Algorithms

    Pierre Baldi;Paolo Frasconi;Padhraic Smyth

  • Learning without local minima in radial basis function networks

    M. Bianchini;P. Frasconi;M. Gori

  • The problem of learning long-term dependencies in recurrent networks

    Y. Bengio;P. Frasconi;P. Simard

  • Bilevel Programming for Hyperparameter Optimization and Meta-Learning

    Luca Franceschi;Paolo Frasconi;Saverio Salzo;Riccardo Grazzi

  • Local feedback multilayered networks

    Paolo Frasconi;Marco Gori;Giovanni Soda

  • New results on error correcting output codes of kernel machines

    A. Passerini;M. Pontil;P. Frasconi

  • Disulfide connectivity prediction using recursive neural networks and evolutionary information

    Alessandro Vullo;Paolo Frasconi

  • Forward and Reverse Gradient-Based Hyperparameter Optimization

    Luca Franceschi;Michele Donini;Paolo Frasconi;Massimiliano Pontil

  • Hidden tree Markov models for document image classification

    M. Diligenti;P. Frasconi;M. Gori

  • Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines

    Yuan Yao;Gian Luca Marcialis;Massimiliano Pontil;Massimiliano Pontil;Paolo Frasconi

  • Whole-Brain Vasculature Reconstruction at the Single Capillary Level.

    Antonino Paolo Di Giovanna;Alessandro Tibo;Ludovico Silvestri;Ludovico Silvestri;Marie Caroline Müllenbroich;Marie Caroline Müllenbroich

  • Representation of finite state automata in recurrent radial basis function networks

    Paolo Frasconi;Marco Gori;Marco Maggini;Giovanni Soda

  • kFOIL: learning simple relational kernels

    Niels Landwehr;Andrea Passerini;Luc De Raedt;Paolo Frasconi

  • Unified integration of explicit knowledge and learning by example in recurrent networks

    P. Frasconi;M. Gori;M. Maggini;G. Soda

Frequent Co-Authors

Marco Gori
Marco Gori University of Siena
Massimiliano Pontil
Massimiliano Pontil Italian Institute of Technology
Luc De Raedt
Luc De Raedt KU Leuven
Pierre Baldi
Pierre Baldi University of California, Irvine
Padhraic Smyth
Padhraic Smyth University of California, Irvine
Yoshua Bengio
Yoshua Bengio University of Montreal
Alessandro Sperduti
Alessandro Sperduti University of Padua
Gianluca Pollastri
Gianluca Pollastri University College Dublin
Gian Luca Marcialis
Gian Luca Marcialis University of Cagliari
Patrick Sturt
Patrick Sturt University of Edinburgh

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