World's Best Scientists 2026 revealed!
Aditya Khosla

Aditya Khosla

D-Index & Metrics

Computer Science

D-Index
35
Citations
80749
World Ranking
11409
National Ranking
4687

Aditya Khosla publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Aditya Khosla sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 53 publications — 1st percentile

1% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Aditya Khosla D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Aditya Khosla sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 35 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Aditya Khosla is affiliated with MIT in the United States and has focused their research primarily on intersections of medicine and computer science. Their scholarly output includes works that span fields such as radiology, nuclear medicine and imaging, artificial intelligence, oncology, genetics, and cancer research. The central themes of their work concentrate especially on radiomics and machine learning applied to medical imaging as well as the use of AI in cancer detection.

Their main topics of research cover:

  • Radiomics and Machine Learning in Medical Imaging
  • AI in cancer detection
  • Inflammatory Bowel Disease
  • Colorectal Cancer Screening and Detection
  • Cancer Genomics and Diagnostics
  • Genetic factors in colorectal cancer
  • Cancer Cells and Metastasis

Among their recent papers are:

  • "Artificial Intelligence Enables Quantitative Assessment of Ulcerative Colitis Histology" (2023) published in Modern Pathology
  • "Distinct hepatic immunological patterns are associated with the progression or inhibition of hepatocellular carcinoma" (2022) in Cell Reports
  • "AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis" (2024) featured in npj Precision Oncology
  • "Abstract 5099: Quantification of TGFβ protein levels and digital pathology-based immune phenotyping reveal biomarkers for TGF-β blockade therapy patient selection in NSCLC" (2022) found in Cancer Research
  • "Improved statistical benchmarking of digital pathology models using pairwise frames evaluation" (2023) in arXiv (Cornell University)

Khosla collaborates frequently with several researchers, including Ilan Wapinski, Michael G. Drage, Fedaa Najdawi, Amaro Taylor-Weiner, and Jacqueline A. Brosnan-Cashman. These collaborations have resulted in multiple co-authored publications.

Their work has appeared repeatedly in well-established venues such as Cancer Research, bioRxiv (Cold Spring Harbor Laboratory), Gastroenterology, Regular and Young Investigator Award Abstracts, and Modern Pathology, indicating a consistent presence in both clinical and preprint research platforms.

Best Publications

  • ImageNet Large Scale Visual Recognition Challenge

    Olga Russakovsky;Jia Deng;Hao Su;Jonathan Krause

  • Learning Deep Features for Discriminative Localization

    Bolei Zhou;Aditya Khosla;Agata Lapedriza;Aude Oliva

  • 3D ShapeNets: A deep representation for volumetric shapes

    Zhirong Wu;Shuran Song;Aditya Khosla;Fisher Yu

  • Places: A 10 Million Image Database for Scene Recognition

    Bolei Zhou;Agata Lapedriza;Aditya Khosla;Aude Oliva

  • Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.

    Babak Ehteshami Bejnordi;Mitko Veta;Paul Johannes van Diest;Bram van Ginneken

  • Multimodal Deep Learning

    Jiquan Ngiam;Aditya Khosla;Mingyu Kim;Juhan Nam

  • Network Dissection: Quantifying Interpretability of Deep Visual Representations

    David Bau;Bolei Zhou;Aditya Khosla;Aude Oliva

  • Object Detectors Emerge in Deep Scene CNNs

    Bolei Zhou;Aditya Khosla;Agata Lapedriza;Aude Oliva

  • Eye Tracking for Everyone

    Kyle Krafka;Aditya Khosla;Petr Kellnhofer;Harini Kannan

  • Deep Learning for Identifying Metastatic Breast Cancer

    Dayong Wang;Aditya Khosla;Rishab Gargeya;Humayun Irshad

  • Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

    Radoslaw Martin Cichy;Aditya Khosla;Dimitrios Pantazis;Antonio Torralba

  • Human action recognition by learning bases of action attributes and parts

    Bangpeng Yao;Xiaoye Jiang;Aditya Khosla;Andy Lai Lin

  • Undoing the damage of dataset bias

    Aditya Khosla;Tinghui Zhou;Tomasz Malisiewicz;Alexei A. Efros

  • What makes an image popular

    Aditya Khosla;Atish Das Sarma;Raffay Hamid

  • Places: An Image Database for Deep Scene Understanding

    Bolei Zhou;Aditya Khosla;Àgata Lapedriza;Antonio Torralba

  • HOGgles: Visualizing Object Detection Features

    Carl Vondrick;Aditya Khosla;Tomasz Malisiewicz;Antonio Torralba

  • Combining randomization and discrimination for fine-grained image categorization

    Bangpeng Yao;Aditya Khosla;Li Fei-Fei

  • Understanding and Predicting Image Memorability at a Large Scale

    Aditya Khosla;Akhil S. Raju;Antonio Torralba;Aude Oliva

  • Large-Scale Video Summarization Using Web-Image Priors

    Aditya Khosla;Raffay Hamid;Chih-Jen Lin;Neel Sundaresan

  • Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes.

    James A. Diao;Jason K. Wang;Wan Fung Chui;Victoria Mountain

  • ImageNet Large Scale Visual Recognition Challenge

    Olga Russakovsky;Jia Deng;Hao Su;Jonathan Krause

Frequent Co-Authors

Bolei Zhou
Bolei Zhou University of California, Los Angeles
Carl Vondrick
Carl Vondrick Columbia University
Jianxiong Xiao
Jianxiong Xiao AutoX, Inc.
Li Fei-Fei
Li Fei-Fei Stanford University
Hao Su
Hao Su University of California, San Diego
Michael S. Bernstein
Michael S. Bernstein Stanford University
Jia Deng
Jia Deng Princeton University
Alexander C. Berg
Alexander C. Berg University of North Carolina at Chapel Hill

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