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 Pedro Szekely sits on this spectrum.
This scientist: 229 publications — 56th percentile
56% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 991 publications or more.
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 Pedro Szekely sits on this spectrum.
This scientist: 36 D-Index — 23rd percentile
23% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
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:
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:
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.
M. Cai;M. Frank;J. Chen;P. Szekely
Pedro A. Szekely
Pedro A. Szekely;Piyawadee Noi Sukaviriya;Pablo Castells;Jeyakumar Muthukumarasamy
Craig A. Knoblock;Pedro Szekely;José Luis Ambite;Aman Goel
Pedro Szekely;Ping Luo;Robert Neches
Rattapoom Tuchinda;Pedro Szekely;Craig A. Knoblock
Pedro Szekely;Ping Luo;Robert Neches
Philip J. Hayes;Pedro A. Szekely;Richard A. Lerner
Pedro Szekely;Brad Myers
Unknown
Pedro Szekely;Craig A. Knoblock;Jason Slepicka;Andrew Philpot
Pedro A. Szekely;Craig A. Knoblock;Fengyu Yang;Xuming Zhu
Minh Pham;Suresh Alse;Craig A. Knoblock;Pedro A. Szekely
Robert Neches;Jim Foley;Pedro Szekely;Piyawadee Sukaviriya
Mohsen Taheriyan;Craig A. Knoblock;Pedro Szekely;José Luis Ambite
Shubham Gupta;Pedro A. Szekely;Craig A. Knoblock;Aman Goel
Craig A. Knoblock;Pedro A. Szekely
Brad Vander Zanden;Brad A. Myers;Dario Giuse;Pedro Szekely
Ping Luo;Pedro Szekely;Robert Neches
S.K. Ramnandan;Amol Mittal;Craig A. Knoblock;Pedro Szekely
Avijit Thawani;Jay Pujara;Filip Ilievski;Pedro A. Szekely
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