Warren Clarida

1.3k citations
17 papers · 1.0k · 1 hit paper · h-index 6

Impact in

    • Retinal Diseases and Treatments
    • Retinal and Optic Conditions
    • Glaucoma and retinal disorders
    • Artificial Intelligence in Healthcare and Education

Papers in

    • Particle physics theoretical and experimental studies 13
    • High-Energy Particle Collisions Research 11
    • Quantum Chromodynamics and Particle Interactions 8
    • Retinal and Optic Conditions 2

Warren Clarida

14 papers receiving 924 citations

Warren Clarida's Hit Papers

Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning 2016 · 873 citations
8730+3+6Years since publication250500750

Peers

Warren Clarida
Comparison fields: 5 of 68
  • Ophthalmology 443
  • Health Informatics 68
  • Radiology, Nuclear Medicine and Imaging 696
  • Health Information Management 109
  • Computer Vision and Pattern Recognition 208
Replace Ryan E. Amelon with:
Ryan E. Amelon United States
Yiyue Lou United States
Jane Scheetz Australia
Tony Lindsey United States
Luís Mendes Portugal
Hon Tym Wong Singapore
Terry Spitz United States
William Campbell Felch United States
Sharon Galvis United States
Shusuke Hiragi Japan
Warren Clarida relative to Ryan E. Amelon United States Ryan E. Amelon's profile →
Citations per field
00.5×1.5×
Ryan E. Amelon · 1×
Citations per year

Countries citing papers authored by Warren Clarida

Since Specialization
Citations

This map shows the geographic impact of Warren Clarida's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Warren Clarida with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Warren Clarida more than expected).

Fields of papers citing papers by Warren Clarida

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Warren Clarida. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Warren Clarida. The network helps show where Warren Clarida may publish in the future.

Co-authors

The 16 scholars most cited alongside Warren Clarida, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Warren Clarida Line = papers co-authored together Warren Clarida links everyone, so they are left out of the graph.

All Works

17 of 17 papers shown
#Work
1
Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning
Hit paper breakdown →
2016873
2 202242
3 201827
4 201722
5 201615
6 20189
7 20202
8
Measurements of ttbar cross sections in association with b jets and inclusive jets and their ratio using dilepton final states in pp collisions at sqrt(s) = 13 TeV
20172
9 20192
10 20182
11
Coherent $\mathrm{ J } / \psi $ photoproduction in ultra-peripheral PbPb collisions at $\sqrt{s_{ \mathrm{NN}}} = $ 2.76 TeV with the CMS experiment
20161
12 20171
13
Search for heavy Majorana neutrinos in e$^\pm$ e$^\pm$ + jets and e$^\pm$ $\mu^\pm$ + jets events in proton-proton collisions at $\sqrt s$ = 8 TeV
20161
14
arXiv : Search for standard model production of four top quarks with same-sign and multilepton final states in proton-proton collisions at $\sqrt{s} =$ 13 TeV
20171
15
Challenges to the chiral magnetic wave using charge-dependent azimuthal anisotropies in pPb and PbPb collisions at $ \sqrt{\smash[b]{s_{_{\mathrm{NN}}}}} = $ 5.02 TeV : arXiv
20171
16
Measurement of the cross section for electroweak production of a Z boson, a photon and two jets in proton-proton collisions at $\sqrt{s} =$ 13 TeV and constraints on anomalous quartic couplings
20200
17
Performance of the missing transverse energy reconstruction by the CMS experiment in sqrt(s) = 8 TeV pp data
20140

About Warren Clarida

Warren Clarida is a scholar working on Nuclear and High Energy Physics, Ophthalmology, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Health Informatics, having authored 17 papers that have together received 1.0k indexed citations. Recurring topics across this work include Particle physics theoretical and experimental studies (13 papers), High-Energy Particle Collisions Research (11 papers), Quantum Chromodynamics and Particle Interactions (8 papers), Computational Physics and Python Applications (4 papers), Retinal Imaging and Analysis (3 papers), Retinal and Optic Conditions (2 papers), Digital Imaging for Blood Diseases (1 paper) and Particle Accelerators and Free-Electron Lasers (1 paper). The work is most often cited by research in Ophthalmology (443 citations), Health Informatics (68 citations), Radiology, Nuclear Medicine and Imaging (696 citations), Health Information Management (109 citations) and Computer Vision and Pattern Recognition (208 citations). Warren Clarida has collaborated with scholars based in United States, France and Netherlands. Frequent co-authors include Michael D. Abràmoff, James C. Folk, Meindert Niemeijer, Ryan E. Amelon, Ali Erginay, Yiyue Lou, Harold P. Lehmann, Risa M. Wolf, Roomasa Channa and Stephen R. Russell. Their work appears in journals such as Investigative Ophthalmology & Visual Science, The European Physical Journal C, Journal of High Energy Physics, npj Digital Medicine and DESY (CERN, DESY, Fermilab, IHEP, and SLAC).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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