World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
54
Citations
16658
World Ranking
2236
National Ranking
877

Computer Science

D-Index
57
Citations
17742
World Ranking
3760
National Ranking
1797

Philip Schniter publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where Philip Schniter sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 183 publications — 24th percentile

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

The last bar groups every scientist with 1,065 publications or more.

Philip Schniter D-index placement in Electronics and Electrical Engineering in 2026

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

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 54 D-Index — 68th percentile

68% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

Research.com Recognitions

  • 2014 - IEEE Fellow For contributions to signal processing in communications

Overview

Philip Schniter is affiliated with The Ohio State University in the United States and has contributed extensively to research spanning engineering, medicine, and computer science. Their work intersects several specialized fields, including radiology, nuclear medicine and imaging, artificial intelligence, computer vision and pattern recognition, computational mechanics, and biomedical engineering.

The main topics of Schniter's research include advanced MRI techniques and applications, medical imaging techniques and applications, sparse and compressive sensing techniques, stochastic gradient optimization techniques, numerical methods in inverse problems, photoacoustic and ultrasonic imaging, and image and signal denoising methods.

They have published in a variety of highly specialized venues, with frequent publications appearing in:

  • arXiv (Cornell University)
  • Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
  • IEEE Signal Processing Magazine
  • IEEE Journal on Selected Areas in Information Theory
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Among notable recent papers authored or coauthored by Schniter are:

  • Plug-and-Play Methods for Magnetic Resonance Imaging: Using Denoisers for Image Recovery (2020, IEEE Signal Processing Magazine)
  • OCMR (v1.0)--Open-Access Multi-Coil k-Space Dataset for Cardiovascular Magnetic Resonance Imaging (2020, arXiv (Cornell University))
  • Sketching Data Sets for Large-Scale Learning: Keeping only what you need (2021, IEEE Signal Processing Magazine)
  • Inference With Deep Generative Priors in High Dimensions (2020, IEEE Journal on Selected Areas in Information Theory)
  • Denoising Generalized Expectation-Consistent Approximation for MR Image Recovery (2022, IEEE Journal on Selected Areas in Information Theory)

Schniter's frequent collaborators include Rizwan Ahmad, R. Badlishah Ahmad, Sizhuo Liu, Saurav K. Shastri, and Xuan Lei. These coauthors have contributed to a significant portion of the body of work in signal processing and medical imaging techniques associated with Schniter.

In 2014, Philip Schniter was recognized as an IEEE Fellow for contributions to signal processing in communications. This accolade reflects the standing within the engineering and signal processing community.

Best Publications

  • In-Band Full-Duplex Wireless: Challenges and Opportunities

    Ashutosh Sabharwal;Philip Schniter;Dongning Guo;Daniel W. Bliss

  • On the achievable diversity-multiplexing tradeoff in half-duplex cooperative channels

    K. Azarian;H. El Gamal;P. Schniter

  • Blind equalization using the constant modulus criterion: a review

    R. Johnson;P. Schniter;T.J. Endres;J.D. Behm

  • Vector Approximate Message Passing

    Sundeep Rangan;Philip Schniter;Alyson K. Fletcher

  • Low-complexity equalization of OFDM in doubly selective channels

    P. Schniter

  • Expectation-Maximization Gaussian-Mixture Approximate Message Passing

    Jeremy P. Vila;Philip Schniter

  • Full-Duplex Bidirectional MIMO: Achievable Rates Under Limited Dynamic Range

    B. P. Day;A. R. Margetts;D. W. Bliss;P. Schniter

  • AMP-Inspired Deep Networks for Sparse Linear Inverse Problems

    Mark Borgerding;Philip Schniter;Sundeep Rangan

  • Compressive phase retrieval via generalized approximate message passing

    Philip Schniter;Sundeep Rangan

  • Full-duplex MIMO relaying: Achievable rates under limited dynamic range

    Brian P. Day;Adam R. Margetts;Daniel W. Bliss;Philip Schniter

  • Channel Estimation in Broadband Millimeter Wave MIMO Systems With Few-Bit ADCs

    Jianhua Mo;Philip Schniter;Robert W. Heath

  • Full-duplex bidirectional MIMO: Achievable rates under limited dynamic range

    Unknown

  • Channel estimation in millimeter wave MIMO systems with one-bit quantization

    Jianhua Mo;Philip Schniter;Nuria Gonzalez Prelcic;Robert W. Heath

  • Compressive imaging using approximate message passing and a Markov-tree prior

    Subhojit Som;Lee C. Potter;Philip Schniter

  • Bilinear Generalized Approximate Message Passing—Part I: Derivation

    Jason T. Parker;Philip Schniter;Volkan Cevher

  • Fast bayesian matching pursuit

    P. Schniter;L.C. Potter;J. Ziniel

  • Regularization by Denoising: Clarifications and New Interpretations

    Edward T. Reehorst;Philip Schniter

  • On the Convergence of Approximate Message Passing With Arbitrary Matrices

    Sundeep Rangan;Philip Schniter;Alyson K. Fletcher;Subrata Sarkar

  • Channel estimation and precoder design for millimeter-wave communications: The sparse way

    Philip Schniter;Akbar Sayeed

  • Fast Bayesian Matching Pursuit: Model Uncertainty and Parameter Estimation for Sparse Linear Models

    Philip Schniter;Justin Ziniel

  • Plug-and-Play Methods for Magnetic Resonance Imaging: Using Denoisers for Image Recovery

    Rizwan Ahmad;Charles A. Bouman;Gregery T. Buzzard;Stanley Chan

  • On the convergence of approximate message passing with arbitrary matrices

    Sundeep Rangan;Philip Schniter;Alyson K. Fletcher

  • Hybrid Approximate Message Passing

    Sundeep Rangan;Alyson K. Fletcher;Vivek K. Goyal;Evan Byrne

  • On the Achievable Diversity-Multiplexing Tradeoffs in Half-Duplex Cooperative Channels

    Kambiz Azarian;Hesham El Gamal;Philip Schniter

Frequent Co-Authors

Sundeep Rangan
Sundeep Rangan New York University
Alyson K. Fletcher
Alyson K. Fletcher University of California, Los Angeles
Richard G. Baraniuk
Richard G. Baraniuk Rice University
Daniel W. Bliss
Daniel W. Bliss Arizona State University
Volkan Cevher
Volkan Cevher École Polytechnique Fédérale de Lausanne
H. El Gamal
H. El Gamal University of Sydney
Robert W. Heath
Robert W. Heath University of California, San Diego
Vivek K. Goyal
Vivek K. Goyal Boston University
Ashok Veeraraghavan
Ashok Veeraraghavan Rice University
Ness B. Shroff
Ness B. Shroff The Ohio State University

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