World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
6289
World Ranking
11161
National Ranking
4626

Overview

Pedro Szekely is affiliated with Amazon in the United States. Their research primarily spans the field of Computer Science, with a focus on several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research, Information Systems, and Computer Networks and Communications.

The scientist's work covers a variety of topics in the domain of advanced computational methods and semantic data management. Key research topics include:

  • Natural Language Processing Techniques
  • Topic Modeling
  • Semantic Web and Ontologies
  • Advanced Graph Neural Networks
  • Data Quality and Management
  • Multimodal Machine Learning Applications
  • Web Data Mining and Analysis

Pedro Szekely has contributed to numerous academic publications, with a strong presence in venues such as Zenodo (CERN European Organization for Nuclear Research), arXiv (Cornell University), and conferences like the North American Chapter of the Association for Computational Linguistics and the International Joint Conference on Artificial Intelligence. Some selected recent papers include:

  • Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning, 2022, Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
  • Augmenting Knowledge Graphs for Better Link Prediction, 2022, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
  • Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering, 2020, arXiv (Cornell University)
  • TOMATE: A heuristic-based approach to extract data from HTML tables, 2021, Information Sciences
  • Hybrid forecasting of geopolitical events†, 2023, AI Magazine

The scientist collaborates frequently with several peers in the research community. Regular coauthors include Filip Ilievski, Daniel Garijo, Kartik Shenoy, Hans Chalupsky, and Daniel Schwabe. These collaborations reflect engagement across multiple aspects of artificial intelligence and data science research.

Pedro Szekely's publication record displays a concentrated interest in the practical and theoretical aspects of knowledge representation, natural language processing, and graph neural networks, often addressing challenges in data quality and structured information extraction. With a substantial number of works available on preprint servers and open repositories, their work contributes to evolving methodologies for semantic analysis and machine learning applications.

Best Publications

  • MAAN: a multi-attribute addressable network for grid information services

    M. Cai;M. Frank;J. Chen;P. Szekely

  • Retrospective and Challenges for Model-Based Interface Development

    Pedro A. Szekely

  • Declarative interface models for user interface construction tools: the MASTERMIND approach

    Pedro A. Szekely;Piyawadee Noi Sukaviriya;Pablo Castells;Jeyakumar Muthukumarasamy

  • Semi-automatically mapping structured sources into the semantic web

    Craig A. Knoblock;Pedro Szekely;José Luis Ambite;Aman Goel

  • Beyond interface builders: model-based interface tools

    Pedro Szekely;Ping Luo;Robert Neches

  • Building Mashups by example

    Rattapoom Tuchinda;Pedro Szekely;Craig A. Knoblock

  • Facilitating the exploration of interface design alternatives: the HUMANOID model of interface design

    Pedro Szekely;Ping Luo;Robert Neches

  • Design alternatives for user interface management sytems based on experience with COUSIN

    Philip J. Hayes;Pedro A. Szekely;Richard A. Lerner

  • A user interface toolkit based on graphical objects and constraints

    Pedro Szekely;Brad Myers

  • MAAN: A Multi-Attribute Addressable Network for Grid Information Services

    Unknown

  • Building and Using a Knowledge Graph to Combat Human Trafficking

    Pedro Szekely;Craig A. Knoblock;Jason Slepicka;Andrew Philpot

  • Connecting the Smithsonian American Art Museum to the Linked Data Cloud

    Pedro A. Szekely;Craig A. Knoblock;Fengyu Yang;Xuming Zhu

  • Semantic Labeling: A Domain-Independent Approach

    Minh Pham;Suresh Alse;Craig A. Knoblock;Pedro A. Szekely

  • Knowledgeable development environments using shared design models

    Robert Neches;Jim Foley;Pedro Szekely;Piyawadee Sukaviriya

  • Learning the semantics of structured data sources

    Mohsen Taheriyan;Craig A. Knoblock;Pedro Szekely;José Luis Ambite

  • Karma: A System for Mapping Structured Sources into the Semantic Web

    Shubham Gupta;Pedro A. Szekely;Craig A. Knoblock;Aman Goel

  • Exploiting Semantics for Big Data Integration

    Craig A. Knoblock;Pedro A. Szekely

  • The importance of pointer variables in constraint models

    Brad Vander Zanden;Brad A. Myers;Dario Giuse;Pedro Szekely

  • Management of interface design in humanoid

    Ping Luo;Pedro Szekely;Robert Neches

  • Assigning Semantic Labels to Data Sources

    S.K. Ramnandan;Amol Mittal;Craig A. Knoblock;Pedro Szekely

  • Representing Numbers in NLP: a Survey and a Vision.

    Avijit Thawani;Jay Pujara;Filip Ilievski;Pedro A. Szekely

Frequent Co-Authors

Craig A. Knoblock
Craig A. Knoblock University of Southern California
José Luis Ambite
José Luis Ambite University of Southern California
Shrikanth S. Narayanan
Shrikanth S. Narayanan University of Southern California
Brad A. Myers
Brad A. Myers Carnegie Mellon University
Pablo Castells
Pablo Castells Autonomous University of Madrid
Aram Galstyan
Aram Galstyan University of Southern California
Xiang Ren
Xiang Ren University of Southern California
Shih-Fu Chang
Shih-Fu Chang Columbia University
Gabor Karsai
Gabor Karsai Vanderbilt University

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