World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
37
Citations
7416
World Ranking
8282
National Ranking
2286

Jeff Moehlis 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 Jeff Moehlis 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: 144 publications — 24th percentile

24% of scientists in this discipline score the same or lower.

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

Jeff Moehlis 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 Jeff Moehlis 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: 37 D-Index — 16th percentile

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

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

Research.com Recognitions

  • 2005 - Fellow of Alfred P. Sloan Foundation

Overview

Jeff Moehlis is affiliated with the University of California, Santa Barbara in the United States. Their research spans multiple areas within neuroscience, focusing primarily on neural dynamics, brain function, and neural engineering.

The main fields of study associated with their work include Neuroscience, with subfields in Cognitive Neuroscience, Cellular and Molecular Neuroscience, Neurology, Statistical and Nonlinear Physics, and Artificial Intelligence.

Their research topics encompass:

  • Neural dynamics and brain function
  • Neuroscience and Neural Engineering
  • Neurological disorders and treatments
  • Insect and Arachnid Ecology and Behavior
  • Neurobiology and Insect Physiology Research
  • Model Reduction and Neural Networks
  • Neural Networks and Applications

Notable recent papers by Jeff Moehlis include:

  • Decoding collective communications using information theory tools (2020), published in Journal of The Royal Society Interface
  • Depolarization block in olfactory sensory neurons expands the dimensionality of odor encoding (2022), published in Science Advances
  • Recent advances in the analysis and control of large populations of neural oscillators (2022), published in Annual Reviews in Control
  • Symbolic regression via neural networks (2023), published in Chaos An Interdisciplinary Journal of Nonlinear Science
  • Supervised learning algorithms for controlling underactuated dynamical systems (2020), published in Physica D Nonlinear Phenomena

Frequent collaborators include:

  • Bharat Monga
  • Faranak Rajabi
  • Frédéric Gibou
  • Philip H. Wong
  • Matthieu Louis

Jeff Moehlis has published frequently in venues such as Biological Cybernetics, arXiv (Cornell University), Journal of The Royal Society Interface, Science Advances, and Annual Reviews in Control.

In 2005, Jeff Moehlis was recognized as a Fellow of the Alfred P. Sloan Foundation.

Best Publications

  • The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks.

    Rafal Bogacz;Eric Brown;Jeff Moehlis;Philip Holmes

  • On the Phase Reduction and Response Dynamics of Neural Oscillator Populations

    Eric Brown;Jeff Moehlis;Philip Holmes

  • Low-Dimensional Modelling of Turbulence Using the Proper Orthogonal Decomposition: A Tutorial

    Troy R. Smith;Jeff Moehlis;Philip Holmes

  • Generalized parametric resonance in electrostatically actuated microelectromechanical oscillators

    Jeffrey F. Rhoads;Steven W. Shaw;Kimberly L. Turner;Jeff Moehlis

  • Isostables, isochrons, and Koopman spectrum for the action-angle representation of stable fixed point dynamics

    Alexandre Mauroy;Igor Mezić;Jeff Moehlis

  • A low-dimensional model for turbulent shear flows

    Jeff Moehlis;Holger Faisst;Bruno Eckhardt

  • Linear and Nonlinear Tuning of Parametrically Excited MEMS Oscillators

    B.E. DeMartini;J.F. Rhoads;K.L. Turner;S.W. Shaw

  • Optimal Inputs for Phase Models of Spiking Neurons

    Jeff Moehlis;Eric Shea-Brown;Herschel Rabitz

  • Chaos for a Microelectromechanical Oscillator Governed by the Nonlinear Mathieu Equation

    B.E. DeMartini;H.E. Butterfield;J. Moehlis;K.L. Turner

  • Globally Coupled Oscillator Networks

    Eric Brown;Philip Holmes;Jeff Moehlis

  • A broadband vibrational energy harvester

    Louis Van Blarigan;Per Danzl;Jeff Moehlis

  • Phasic Burst Stimulation: A Closed-Loop Approach to Tuning Deep Brain Stimulation Parameters for Parkinson's Disease.

    Abbey B. Holt;Dan Wilson;Max Shinn;Jeff Moehlis

  • Phase reduction and phase-based optimal control for biological systems: a tutorial.

    Bharat Monga;Dan Wilson;Timothy Matchen;Jeff Moehlis

  • Coarse-grained analysis of stochasticity-induced switching between collective motion states

    Allison Kolpas;Jeff Moehlis;Ioannis G. Kevrekidis

  • Models for turbulent plane Couette flow using the proper orthogonal decomposition

    J. Moehlis;T. R. Smith;P. Holmes;H. Faisst

  • Optimal Chaotic Desynchronization for Neural Populations

    Dan Wilson;Jeff Moehlis

  • Controlling biological networks by time-delayed signals

    Gábor Orosz;Jeff Moehlis;Richard M. Murray

  • Event-based minimum-time control of oscillatory neuron models: Phase randomization, maximal spike rate increase, and desynchronization

    Per Danzl;João Hespanha;Jeff Moehlis

  • Minimum energy desynchronizing control for coupled neurons

    Ali Nabi;Mohammad Mirzadeh;Frederic Gibou;Jeff Moehlis

  • The Influence of Spike Rate and Stimulus Duration on Noradrenergic Neurons

    Eric Brown;Jeff Moehlis;Philip Holmes;Ed Clayton

  • Synchronization of Identical Oscillators Coupled Through a Symmetric Network With Dynamics: A Constructive Approach With Applications to Parallel Operation of Inverters

    Leonardo A. B. Torres;Joao P. Hespanha;Jeff Moehlis

  • Equivariant dynamical systems

    Jeff Moehlis;Edgar Knobloch

Frequent Co-Authors

Edgar Knobloch
Edgar Knobloch University of California, Berkeley
Philip Holmes
Philip Holmes Princeton University
Theoden I. Netoff
Theoden I. Netoff University of Minnesota
Kimberly L. Turner
Kimberly L. Turner University of California, Santa Barbara
Bruno Eckhardt
Bruno Eckhardt Philipp University of Marburg
Steven W. Shaw
Steven W. Shaw Florida Institute of Technology
Rafal Bogacz
Rafal Bogacz University of Oxford
Jonathan D. Cohen
Jonathan D. Cohen Princeton University
Joao P. Hespanha
Joao P. Hespanha University of California, Santa Barbara
Francesco Bullo
Francesco Bullo University of California, Santa Barbara

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