Daniel D’souza

509 citations
3 papers · 64 · h-index 3

Impact in

    • Topic Modeling
    • Natural Language Processing Techniques
    • Machine Learning and Data Classification
    • Adversarial Robustness in Machine Learning
    • Domain Adaptation and Few-Shot Learning
    • Advanced Text Analysis Techniques

Papers in

    • Topic Modeling 2
    • Advanced Text Analysis Techniques 1
    • Sentiment Analysis and Opinion Mining 1
    • Natural Language Processing Techniques 1
    • Adversarial Robustness in Machine Learning 1
    • Machine Learning and Data Classification 1
    • Anomaly Detection Techniques and Applications 1

Daniel D’souza

3 papers receiving 62 citations

Peers

Daniel D’souza
Comparison fields: 5 of 34
  • Health Informatics 2
  • Artificial Intelligence 43
  • Computer Vision and Pattern Recognition 14
  • Family Practice 1
  • Computer Science Applications 2
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Fereshte Khani United States
Abhinav Ramesh Kashyap Singapore
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Massih-Réza Amini France
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Citations per field
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Citations per year

Countries citing papers authored by Daniel D’souza

Since Specialization
Citations

This map shows the geographic impact of Daniel D’souza'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 Daniel D’souza with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel D’souza more than expected).

Fields of papers citing papers by Daniel D’souza

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel D’souza. 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 Daniel D’souza. The network helps show where Daniel D’souza may publish in the future.

Co-authors

The 12 scholars most cited alongside Daniel D’souza, 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 Daniel D’souza Line = papers co-authored together Daniel D’souza links everyone, so they are left out of the graph.

All Works

3 of 3 papers shown
#Work
1 202233
2 202425
3 20226

About Daniel D’souza

Daniel D’souza is a scholar working on Artificial Intelligence, Infectious Diseases, Organic Chemistry, Surgery and Communication, having authored 3 papers that have together received 64 indexed citations. Recurring topics across this work include Topic Modeling (2 papers), Advanced Text Analysis Techniques (1 paper), Sentiment Analysis and Opinion Mining (1 paper), Natural Language Processing Techniques (1 paper), Adversarial Robustness in Machine Learning (1 paper), Machine Learning and Data Classification (1 paper) and Anomaly Detection Techniques and Applications (1 paper). The work is most often cited by research in Health Informatics (2 citations), Artificial Intelligence (43 citations), Computer Vision and Pattern Recognition (14 citations), Family Practice (1 citation) and Computer Science Applications (2 citations). Daniel D’souza has collaborated with scholars based in United States, Philippines and India. Frequent co-authors include Sara Hooker, Chirag Agarwal, Shayne Longpre, Narayana Darapaneni, Anwesh Reddy Paduri, Marzieh Fadaee, Ahmet Üstün, Niklas Muennighoff, Julia Kreutzer and Zheng Yong. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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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