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 Lalana Kagal sits on this spectrum.
This scientist: 159 publications — 30th percentile
30% 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 Lalana Kagal sits on this spectrum.
This scientist: 40 D-Index — 37th percentile
37% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
Lalana Kagal is affiliated with MIT in the United States and specializes in the field of Computer Science. Their research contributions focus notably on Artificial Intelligence, Information Systems, and subfields including Computer Vision and Pattern Recognition as well as Information Systems and Management. The work broadly spans Privacy-Preserving Technologies in Data, Cryptography and Data Security, and Blockchain Technology Applications and Security.
The scientist's publication record includes works primarily disseminated through venues such as arXiv (Cornell University), the 2022 IEEE International Conference on Big Data, and IEEE Internet Computing. Their research output features both conferences and preprint repositories.
Key recent papers authored or co-authored by Lalana Kagal cover topics related to federated learning, bias in image classification, and synthetic data generation. These include:
Frequent collaborators in their research include Vaikkunth Mugunthan, Oshani Seneviratne, Ravi Rahman, William Van Woensel, and Floriano Scioscia. These associations reflect ongoing work within collaborative networks focused on evolving technologies in machine learning and data privacy.
Their scholarly interests also emphasize Explainable Artificial Intelligence (XAI), Machine Learning applications in healthcare, and Recommender Systems and Techniques, illustrating a breadth of focus that bridges foundational methods and applied domains.
Leilani H. Gilpin;David Bau;Ben Z. Yuan;Ayesha Bajwa
L. Kagal;T. Finin;Anupam Joshi
Lalana Kagal;Tim Finin;Anupam Joshi
Midas Nouwens;Ilaria Liccardi;Michael Veale;David Karger
L. Kagal;T. Finin;A. Joshi
H. Chen;T. Finin;Anupam Joshi;L. Kagal
Lora Aroyo;Chris Welty;Harith Alani;Jamie Taylor
Tim Berners-lee;Dan Connolly;Lalana Kagal;Yosi Scharf
T. Finin;A. Joshi;L. Kagal;J. Niu
L. Kagal;T. Finin;M. Paolucci;Navcen Srinivasan
Grit Denker;Lalana Kagal;Tim Finin;Massimo Paolucci
Lalana Kagal
Alessandra Toninelli;Rebecca Montanari;Lalana Kagal;Ora Lassila
Harith Alani;Lalana Kagal;Achille Fokoue;Paul Groth
Leilani H. Gilpin;David Bau;Ben Z. Yuan;Ayesha Bajwa
R.S. Cost;T. Finin;A. Joshi;Yun Peng
L. Kagal;V. Korolev;H. Chen;Anupam Joshi
Lalana Kagal;Tim Berners-Lee;Dan Connolly;Daniel Weitzner
Grit Denker;Lalana Kagal;Tim Finin
Lalana Kagal;Tim Finin
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