World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
55
Citations
10652
World Ranking
3020
National Ranking
904

Ram Rajagopal 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 Ram Rajagopal 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: 309 publications — 78th percentile

78% of scientists in this discipline score the same or lower.

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

Ram Rajagopal 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 Ram Rajagopal 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: 55 D-Index — 70th percentile

70% of scientists in this discipline score the same or lower.

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

Overview

Ram Rajagopal is affiliated with Stanford University in the United States and specializes in engineering with a focus on electrical and electronic engineering. Their research encompasses several subfields including automotive engineering, global and planetary change, renewable energy, sustainability and the environment, as well as computer vision and pattern recognition.

The main topics of Ram Rajagopal's work include smart grid energy management, electric vehicles and infrastructure, advanced battery technologies research, energy and environment impacts, energy load and power forecasting, microgrid control and optimization, and optimal power flow distribution.

Frequent collaborators in their research include Chad Zanocco, June A. Flora, Zhecheng Wang, Yang Weng, and Siobhan Powell.

Rajagopal has published extensively across various venues, with numerous publications in arXiv (Cornell University), Applied Energy, SSRN Electronic Journal, Nature Energy, and IEEE Transactions on Smart Grid.

Recent papers authored by or coauthored with Rajagopal include the following:

  • Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption, 2022, Nature Energy
  • Improving Probabilistic Load Forecasting Using Quantile Regression NN With Skip Connections, 2020, IEEE Transactions on Smart Grid
  • Scalable probabilistic estimates of electric vehicle charging given observed driver behavior, 2022, Applied Energy
  • Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding, 2020, Proceedings of the AAAI Conference on Artificial Intelligence
  • Global changes in electricity consumption during COVID-19, 2021, iScience

In addition to journal articles, Ram Rajagopal has contributed to book literature, including a publication with Springer Nature titled Data Science and Applications for Modern Power Systems released in 2023.

Best Publications

  • Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior

    Amir Kavousian;Ram Rajagopal;Martin Fischer

  • Household Energy Consumption Segmentation Using Hourly Data

    Jungsuk Kwac;June Flora;Ram Rajagopal

  • Bringing Wind Energy to Market

    Eilyan Y. Bitar;Ram Rajagopal;Pramod P. Khargonekar;Kameshwar Poolla

  • DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States

    Jiafan Yu;Zhecheng Wang;Arun Majumdar;Ram Rajagopal

  • Smart Meter Driven Segmentation: What Your Consumption Says About You

    Adrian Albert;Ram Rajagopal

  • Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors

    Karric Kwong;Robert Kavaler;Ram Rajagopal;Pravin Varaiya

  • PaToPa: A Data-Driven Parameter and Topology Joint Estimation Framework in Distribution Grids

    Jiafan Yu;Yang Weng;Ram Rajagopal

  • Distributed Energy Resources Topology Identification via Graphical Modeling

    Yang Weng;Yizheng Liao;Ram Rajagopal

  • Detection and Statistics of Wind Power Ramps

    Raffi Sevlian;Ram Rajagopal

  • Optimal bidding strategy for microgrids in joint energy and ancillary service markets considering flexible ramping products

    Jianxiao Wang;Haiwang Zhong;Wenyuan Tang;Ram Rajagopal

  • Context-Aware Generative Adversarial Privacy

    Chong Huang;Peter Kairouz;Xiao Chen;Lalitha Sankar

  • Optimal dynamic parking pricing for morning commute considering expected cruising time

    Zhen (Sean) Qian;Ram Rajagopal

  • Urban MV and LV Distribution Grid Topology Estimation via Group Lasso

    Yizheng Liao;Yang Weng;Guangyi Liu;Ram Rajagopal

  • In-pavement wireless sensor network for vehicle classification

    Ravneet Bajwa;Ram Rajagopal;Pravin Varaiya;Robert Kavaler

  • Risk-limiting dispatch for integrating renewable power

    Ram Rajagopal;Eilyan Bitar;Pravin Varaiya;Felix Wu

  • Data-driven planning of distributed energy resources amidst socio-technical complexities

    Rishee K. Jain;Junjie Qin;Ram Rajagopal

  • Network-Based Consensus Averaging With General Noisy Channels

    R Rajagopal;M J Wainwright

  • Scalable probabilistic estimates of electric vehicle charging given observed driver behavior

    Unknown

  • A scaling law for short term load forecasting on varying levels of aggregation

    Raffi Sevlian;Ram Rajagopal

  • PaToPaEM: A Data-Driven Parameter and Topology Joint Estimation Framework for Time-Varying System in Distribution Grids

    Jiafan Yu;Yang Weng;Ram Rajagopal

  • The role of co-located storage for wind power producers in conventional electricity markets

    E. Bitar;R. Rajagopal;P. Khargonekar;K. Poolla

  • System and method for signal matching and characterization

    Ram Rajagopal;Lothar Wenzel;Dinesh Nair;Darren Schmidt

  • FedGAN: Federated Generative Adversarial Networks for Distributed Data.

    Mohammad Rasouli;Tao Sun;Ram Rajagopal

Frequent Co-Authors

Pravin Varaiya
Pravin Varaiya University of California, Berkeley
Yang Weng
Yang Weng Arizona State University
Anne S. Kiremidjian
Anne S. Kiremidjian Stanford University
Baosen Zhang
Baosen Zhang University of Washington
Abbas El Gamal
Abbas El Gamal Stanford University
June A. Flora
June A. Flora Stanford University
H. Vincent Poor
H. Vincent Poor Princeton University
Ramesh Johari
Ramesh Johari Stanford University
Andrea Goldsmith
Andrea Goldsmith Stony Brook University
Kameshwar Poolla
Kameshwar Poolla University of California, Berkeley

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