World's Best Scientists 2026 revealed!
Richard D. Braatz

Richard D. Braatz

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
91
Citations
31169
World Ranking
286
National Ranking
141

Richard D. Braatz 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 Richard D. Braatz 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: 562 publications — 88th percentile

88% of scientists in this discipline score the same or lower.

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

Richard D. Braatz 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 Richard D. Braatz 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: 91 D-Index — 96th percentile

96% 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

  • 2019 - Member of the National Academy of Engineering For contributions to diagnosis and control of large-scale and molecular processes for materials, microelectronics and pharmaceuticals manufacturing.
  • 2008 - Fellow of the International Federation of Automatic Control (IFAC)
  • 2008 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2007 - IEEE Fellow For contributions to robust control of industrial systems

Overview

Richard D. Braatz is affiliated with MIT in the United States and has contributed extensively to research at the intersection of engineering and biochemistry, genetics, and molecular biology. Their work spans several specialized subfields, including molecular biology, control and systems engineering, electrical and electronic engineering, automotive engineering, and materials chemistry.

The scientist's research topics cover various advanced technological and biological areas, notably:

  • Advanced Battery Technologies Research
  • Viral Infectious Diseases and Gene Expression in Insects
  • Advancements in Battery Materials
  • Fault Detection and Control Systems
  • Advanced Control Systems Optimization
  • Protein purification and stability
  • Advanced Battery Materials and Technologies

Richard D. Braatz has published numerous papers in well-known academic venues. Frequent publication sites include:

  • arXiv (Cornell University)
  • Computers & Chemical Engineering
  • Journal of The Electrochemical Society
  • Biotechnology and Bioengineering
  • IFAC-PapersOnLine

Noteworthy recent papers reflect the scientist's involvement in battery research, electrochemical processes, and gene therapies. These include:

  • Water electrolysis: from textbook knowledge to the latest scientific strategies and industrial developments, 2022, Chemical Society Reviews
  • Closed-loop optimization of fast-charging protocols for batteries with machine learning, 2020, Nature
  • Perspective-Combining Physics and Machine Learning to Predict Battery Lifetime, 2021, Journal of The Electrochemical Society
  • Fictitious phase separation in Li layered oxides driven by electro-autocatalysis, 2021, Nature Materials
  • Analytical methods for process and product characterization of recombinant adeno-associated virus-based gene therapies, 2021, Molecular Therapy - Methods & Clinical Development

The scientist collaborates frequently with colleagues, including:

  • Martin Z. Bazant
  • Allan S. Myerson
  • Anthony J. Sinskey
  • Jacqueline M. Wolfrum
  • Stacy L. Springs

In recognition of their contributions, Richard D. Braatz has received several awards. They became a Member of the National Academy of Engineering in 2019 for work on diagnosis and control of large-scale and molecular processes related to materials, microelectronics, and pharmaceuticals manufacturing.

Additional honors include being named a Fellow of the American Association for the Advancement of Science (AAAS) in 2008 and a Fellow of the International Federation of Automatic Control (IFAC) in the same year. The scientist was also recognized as an IEEE Fellow in 2007 for contributions to robust control of industrial systems.

Best Publications

  • Data-driven prediction of battery cycle life before capacity degradation

    Kristen A. Severson;Peter M. Attia;Norman Jin;Nicholas Perkins

  • Fault Detection and Diagnosis in Industrial Systems

    Leo H. Chiang;Evan L. Russell;Richard D. Braatz

  • Closed-loop optimization of fast-charging protocols for batteries with machine learning.

    Peter M. Attia;Aditya Grover;Norman Jin;Kristen A. Severson

  • Modeling and Simulation of Lithium-Ion Batteries from a Systems Engineering Perspective

    Venkatasailanathan Ramadesigan;Paul W. C. Northrop;Sumitava De;Shriram Santhanagopalan

  • A tutorial on linear and bilinear matrix inequalities

    Jeremy G. VanAntwerp;Richard D. Braatz

  • Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis

    Leo H Chiang;Evan L Russell;Richard D Braatz

  • End‐to‐End Continuous Manufacturing of Pharmaceuticals: Integrated Synthesis, Purification, and Final Dosage Formation

    Salvatore Mascia;Patrick L. Heider;Haitao Zhang;Richard Lakerveld

  • Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis

    Evan L. Russell;Leo H. Chiang;Richard D. Braatz

  • Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes

    Evan L. Russell;Leo H. Chiang;Richard D. Braatz

  • Assessment of Recent Process Analytical Technology (PAT) Trends: A Multiauthor Review

    Levente L. Simon;Hajnalka Pataki;György Marosi;Fabian Meemken

  • First-principles and direct design approaches for the control of pharmaceutical crystallization

    Mitsuko Fujiwara;Zoltan K. Nagy;Jie W. Chew;Richard D. Braatz

  • Mathematical modeling of drug delivery from autocatalytically degradable PLGA microspheres--a review

    Ashlee N. Ford Versypt;Daniel W. Pack;Daniel W. Pack;Richard D. Braatz;Richard D. Braatz

  • High resolution algorithms for multidimensional population balance equations

    Rudiyanto Gunawan;Irene Fusman;Richard D. Braatz

  • Advances and new directions in crystallization control

    Zoltan K. Nagy;Richard D. Braatz

  • Robust nonlinear model predictive control of batch processes

    Zoltan K. Nagy;Richard D. Braatz

  • Paracetamol Crystallization Using Laser Backscattering and ATR-FTIR Spectroscopy: Metastability, Agglomeration, and Control

    Mitsuko Fujiwara;Pui Shan Chow;and David L. Ma;Richard D. Braatz

  • LIONSIMBA: A Matlab Framework Based on a Finite Volume Model Suitable for Li-Ion Battery Design, Simulation, and Control

    Marcello Torchio;Lalo Magni;R. Bhushan Gopaluni;Richard D. Braatz

  • Advanced control of crystallization processes

    Richard D. Braatz

  • Constrained zonotopes

    Joseph K. Scott;Davide M. Raimondo;Giuseppe Roberto Marseglia;Richard D. Braatz

  • Switched model predictive control of switched linear systems

    Lixian Zhang;Songlin Zhuang;Richard D. Braatz

  • Open-loop and closed-loop robust optimal control of batch processes using distributional and worst-case analysis

    Zoltan K. Nagy;Zoltan K. Nagy;Richard D. Braatz

  • Improved Filter Design in Internal Model Control

    Ian G. Horn;Jeffery R. Arulandu;Christopher J. Gombas;Jeremy G. VanAntwerp

  • Stochastic nonlinear model predictive control with probabilistic constraints

    Ali Mesbah;Stefan Streif;Rolf Findeisen;Richard D. Braatz

  • Designer Dual Therapy Nanolayered Implant Coatings Eradicate Biofilms and Accelerate Bone Tissue Repair

    Jouha Min;Ki Young Choi;Erik C. Dreaden;Robert F. Padera

  • Modelling and control of combined cooling and antisolvent crystallization processes

    Zoltan K. Nagy;M. Fujiwara;Richard D. Braatz

  • Computational complexity of µ calculation

    Richard D. Braatz;Peter M. Young;John C. Doyle;Manfred Morari

  • Distributional uncertainty analysis using power series and polynomial chaos expansions

    Z.K. Nagy;R.D. Braatz

  • Model Predictive Control

    Andrew P. Featherstone;Jeremy G. VanAntwerp;Richard D. Braatz

  • Computational complexity of μ calculation

    Richard D. Braatz;Peter M. Young;John C. Doyle;Manfred Morari

  • On the "Identification and control of dynamical systems using neural networks"

    E. Rios-Patron;R.D. Braatz

Frequent Co-Authors

Venkat R. Subramanian
Venkat R. Subramanian The University of Texas at Austin
Richard C. Alkire
Richard C. Alkire University of Illinois at Urbana-Champaign
Zoltan K. Nagy
Zoltan K. Nagy Purdue University West Lafayette
Rolf Findeisen
Rolf Findeisen Technical University of Darmstadt
Manfred Morari
Manfred Morari University of Pennsylvania
Ali Mesbah
Ali Mesbah University of California, Berkeley
Reginald B. H. Tan
Reginald B. H. Tan National University of Singapore

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