Antonio Cavalcante
About Antonio Cavalcante
Antonio Cavalcante is a Research Engineer at Borealis AI in Toronto, Ontario, Canada, specializing in scalable density-based clustering through graph theory and computational geometry. He holds a PhD in Computer Science from the University of Alberta and has extensive research experience in graph mining and unsupervised learning.
Work at Borealis AI
Antonio Cavalcante has been employed as a Research Engineer at Borealis AI since 2021. His role focuses on advancing research in artificial intelligence, particularly in scalable density-based clustering. He operates from the Toronto, Ontario, Canada office, contributing to the organization's mission of developing innovative AI solutions.
Education and Expertise
Antonio Cavalcante holds a Doctor of Philosophy (PhD) in Computer Science from the University of Alberta, where he studied from 2015 to 2020. He also earned a Master of Science (MSc) in Computer Science from the Federal University of Ceara from 2013 to 2015, and a Bachelor of Science (BSc) in Computer Science from the same institution from 2008 to 2013. Additionally, he studied at ENSIIE - École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise for one year in 2011-2012.
Background
Before joining Borealis AI, Antonio Cavalcante worked as a Postdoctoral Fellow at the University of Alberta from 2015 to 2021. He also served as a Visiting PhD Student at the University of Newcastle for three months in 2019. His early experience includes a research internship at TELECOM SudParis in 2012.
Research Focus and Interests
Antonio's research primarily centers on scalable density-based clustering, utilizing perspectives from graph theory and computational geometry. His interests also encompass graph mining, hierarchical clustering, and the efficient exploration of parameter spaces for unsupervised learning. His PhD research specifically involved the use of proximity graphs for density-based clustering and classification.
Publications and Contributions
Antonio Cavalcante has published a research blog discussing the use of Great Expectations for validating Delta Tables. He has also contributed equally to a publication focused on the validation of Delta Tables using Great Expectations, showcasing his involvement in advancing methodologies in data validation.