World's Best Scientists 2026 revealed!
Jonathan P. How

Jonathan P. How

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Electronics and Electrical Engineering
USA
2026
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Mechanical and Aerospace Engineering
USA
2026

D-Index & Metrics

Mechanical and Aerospace Engineering

D-Index
100
Citations
39448
World Ranking
64
National Ranking
33

Electronics and Electrical Engineering

D-Index
102
Citations
40251
World Ranking
165
National Ranking
80

Jonathan P. How publication distribution in Mechanical and Aerospace Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mechanical and Aerospace Engineering in 2026. The highlighted bar marks where Jonathan P. How sits on this spectrum.

47–56 publications: 10 scientists 57–66 publications: 23 scientists 67–76 publications: 32 scientists 77–86 publications: 62 scientists 87–96 publications: 67 scientists 97–106 publications: 91 scientists 107–116 publications: 113 scientists 117–126 publications: 115 scientists 127–136 publications: 130 scientists 137–146 publications: 140 scientists 147–156 publications: 155 scientists 157–166 publications: 132 scientists 167–176 publications: 133 scientists 177–186 publications: 130 scientists 187–196 publications: 140 scientists 197–206 publications: 115 scientists 207–216 publications: 125 scientists 217–226 publications: 117 scientists 227–236 publications: 99 scientists 237–246 publications: 92 scientists 247–256 publications: 100 scientists 257–266 publications: 95 scientists 267–276 publications: 88 scientists 277–286 publications: 77 scientists 287–296 publications: 74 scientists 297–306 publications: 74 scientists 307–316 publications: 62 scientists 317–326 publications: 70 scientists 327–336 publications: 59 scientists 337–346 publications: 58 scientists 347–356 publications: 45 scientists 357–366 publications: 44 scientists 367–376 publications: 36 scientists 377–386 publications: 41 scientists 387–396 publications: 32 scientists 397–406 publications: 23 scientists 407–416 publications: 28 scientists 417–426 publications: 27 scientists 427–436 publications: 25 scientists 437–446 publications: 23 scientists 447–456 publications: 23 scientists 457–466 publications: 20 scientists 467–476 publications: 12 scientists 477–486 publications: 24 scientists 487–496 publications: 18 scientists 497–506 publications: 12 scientists 507–516 publications: 13 scientists 517–526 publications: 21 scientists 527–536 publications: 12 scientists 537–546 publications: 8 scientists 547–556 publications: 16 scientists 557–566 publications: 3 scientists 567–576 publications: 11 scientists 577–586 publications: 6 scientists 587–596 publications: 5 scientists 597–606 publications: 6 scientists 607–616 publications: 7 scientists 617–626 publications: 7 scientists 627–636 publications: 10 scientists 637–646 publications: 4 scientists 647–656 publications: 3 scientists 657–658 publications: 2 scientists 659+ publications: 100 scientists
47 publications 659+

This scientist: 698 publications — 98th percentile

98% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 659 publications or more.

Jonathan P. How D-index placement in Mechanical and Aerospace Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Mechanical and Aerospace Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Jonathan P. How sits on this spectrum.

30 D-Index: 83 scientists 31 D-Index: 113 scientists 32 D-Index: 144 scientists 33 D-Index: 153 scientists 34 D-Index: 189 scientists 35 D-Index: 158 scientists 36 D-Index: 139 scientists 37 D-Index: 127 scientists 38 D-Index: 130 scientists 39 D-Index: 126 scientists 40 D-Index: 104 scientists 41 D-Index: 100 scientists 42 D-Index: 107 scientists 43 D-Index: 101 scientists 44 D-Index: 103 scientists 45 D-Index: 79 scientists 46 D-Index: 88 scientists 47 D-Index: 70 scientists 48 D-Index: 83 scientists 49 D-Index: 44 scientists 50 D-Index: 64 scientists 51 D-Index: 56 scientists 52 D-Index: 50 scientists 53 D-Index: 48 scientists 54 D-Index: 58 scientists 55 D-Index: 52 scientists 56 D-Index: 48 scientists 57 D-Index: 42 scientists 58 D-Index: 34 scientists 59 D-Index: 42 scientists 60 D-Index: 37 scientists 61 D-Index: 42 scientists 62 D-Index: 44 scientists 63 D-Index: 22 scientists 64 D-Index: 33 scientists 65 D-Index: 29 scientists 66 D-Index: 23 scientists 67 D-Index: 29 scientists 68 D-Index: 24 scientists 69 D-Index: 19 scientists 70 D-Index: 34 scientists 71 D-Index: 26 scientists 72 D-Index: 19 scientists 73 D-Index: 18 scientists 74 D-Index: 19 scientists 75 D-Index: 14 scientists 76 D-Index: 19 scientists 77 D-Index: 8 scientists 78 D-Index: 18 scientists 79 D-Index: 16 scientists 80 D-Index: 12 scientists 81 D-Index: 17 scientists 82 D-Index: 11 scientists 83 D-Index: 16 scientists 84 D-Index: 7 scientists 85 D-Index: 9 scientists 86 D-Index: 8 scientists 87 D-Index: 6 scientists 88 D-Index: 6 scientists 89 D-Index: 7 scientists 90 D-Index: 10 scientists 91 D-Index: 4 scientists 92 D-Index: 4 scientists 93+ D-Index: 100 scientists
30 D-Index 93+

This scientist: 100 D-Index — 98th percentile

98% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 93 D-Index or more.

Research.com Recognitions

  • 2026 - Research.com Electronics and Electrical Engineering in United States Leader Award
  • 2026 - Research.com Mechanical and Aerospace Engineering in United States Leader Award
  • 2025 - Research.com Electronics and Electrical Engineering in United States Leader Award
  • 2018 - IEEE Fellow For contributions to guidance and control of air and space vehicles

Overview

Jonathan P. How is affiliated with MIT in the United States and specializes in research primarily within Computer Science and Engineering. Their work extensively covers several focused areas, including Artificial Intelligence, Computer Vision and Pattern Recognition, Aerospace Engineering, Control and Systems Engineering, and Electrical and Electronic Engineering. The scientific topics they cover emphasize Robotics and Sensor-Based Localization, Robotic Path Planning Algorithms, Reinforcement Learning in Robotics, Indoor and Outdoor Localization Technologies, Autonomous Vehicle Technology and Safety, Adversarial Robustness in Machine Learning, and Advanced Image and Video Retrieval Techniques.

Their recent publications include:

  • Collision Avoidance in Pedestrian-Rich Environments With Deep Reinforcement Learning, 2021, IEEE Access
  • Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems, 2022, IEEE Transactions on Robotics
  • Robust Adaptive Control Barrier Functions: An Adaptive and Data-Driven Approach to Safety, 2020, IEEE Control Systems Letters
  • Momelotinib versus danazol in symptomatic patients with anaemia and myelofibrosis (MOMENTUM): results from an international, double-blind, randomised, controlled, phase 3 study, 2023, The Lancet
  • FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments, 2021, IEEE Transactions on Robotics

The researcher frequently collaborates with a consistent group of coauthors, including Michael Everett, Rodolphe Sepulchre, Miroslav Krstić, Yasamin Mostofi, and Thomas Parisini. These collaborators have worked with them on numerous publications, reflecting a network of contributors in the field.

Jonathan P. How's work has been published extensively in well-known venues such as arXiv (Cornell University), IEEE Robotics and Automation Letters, IEEE Transactions on Robotics, IEEE Transactions on Control of Network Systems, and IEEE Transactions on Control Systems Technology. The high number of publications across these channels indicates sustained research activity within their domains of expertise.

In recognition of their contributions to guidance and control of air and space vehicles, they were named an IEEE Fellow in 2018.

Best Publications

  • Consensus-Based Decentralized Auctions for Robust Task Allocation

    Han-Lim Choi;L. Brunet;J.P. How

  • Real-Time Motion Planning With Applications to Autonomous Urban Driving

    Y. Kuwata;S. Karaman;J. Teo;E. Frazzoli

  • Aircraft trajectory planning with collision avoidance using mixed integer linear programming

    A. Richards;J.P. How

  • Spacecraft Formation Flying: Dynamics, Control and Navigation

    Kyle Terry Alfriend;Srinivas Rao Vadali;Pini Gurfil;Jonathan How

  • Mixed integer programming for multi-vehicle path planning

    Tom Schouwenaars;Bart De Moor;Eric Feron;Jonathan How

  • Socially aware motion planning with deep reinforcement learning

    Yu Fan Chen;Michael Everett;Miao Liu;Jonathan P. How

  • Spacecraft trajectory planning with avoidance constraints using mixed-integer linear programming

    Arthur Richards;Tom Schouwenaars;Jonathan P. How;Eric Feron

  • Relative Dynamics and Control of Spacecraft Formations in Eccentric Orbits

    Gokhan Inalhan;Michael Tillerson;Jonathan P. How

  • A New Nonlinear Guidance Logic for Trajectory Tracking

    Sanghyuk Park;John Deyst;Jonathan P. How

  • A perception-driven autonomous urban vehicle

    John Leonard;Jonathan How;Seth Teller;Mitch Berger

  • Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning

    Yu Fan Chen;Miao Liu;Michael Everett;Jonathan P. How

  • Performance and Lyapunov Stability of a Nonlinear Path Following Guidance Method

    Sanghyuk Park;John Deyst;Jonathan P. How

  • Control with random communication delays via a discrete-time jump system approach

    Lin Xiao;A. Hassibi;J.P. How

  • Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

    Michael Everett;Yu Fan Chen;Jonathan P. How

  • Real-time indoor autonomous vehicle test environment

    J.P. How;B. Bethke;A. Frank;D. Dale

  • Autonomous driving in urban environments: approaches, lessons and challenges

    Mark E. Campbell;Magnus Egerstedt;Jonathan P. How;Richard M Murray

  • COORDINATION AND CONTROL OF MULTIPLE UAVs

    Arthur Richards;John Bellingham;Michael Tillerson;Jonathan How

  • A path-following method for solving BMI problems in control

    A. Hassibi;J. How;S. Boyd

  • A Perception Driven Autonomous Urban Robot

    John Leonard;Jonathan How;Seth Teller;Mitch Berger

  • Robust distributed model predictive control

    Arthur Richards;Jonathan P. How

Frequent Co-Authors

Girish Chowdhary
Girish Chowdhary University of Illinois at Urbana-Champaign
John Vian
John Vian Boeing (United States)
Kyle T. Alfriend
Kyle T. Alfriend Texas A&M University
Pini Gurfil
Pini Gurfil Technion – Israel Institute of Technology
Srinivas R. Vadali
Srinivas R. Vadali Texas A&M University
Tom Walsh
Tom Walsh University of Washington
Mark Campbell
Mark Campbell Cornell University

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