Vítor Nascimento LourençoPh.D. Student at Computing Intitute, Federal Fluminense UniversityData Science Advisor at Dell Technologies Emails: vitorlourenco@id.uff.br | CV | ORCID | Google Scholar | LinkedIn | Lattes | GitHub |
Bio
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Hi, there!
My name is Vítor Lourenço. I am an Applied Researcher committed to bringing state-of-the-art AI solutions to industry challenges. I have over four years of experience in the research, development, and deployment (at scale) of machine learning solutions. My goal as a researcher is to tackle two of our century's core challenges: food and energy production and distribution; and social media and communications.
Currently, I am positioned as a Data Science Advisor at Dell Technologies and a Ph.D. Student at Computing Institute, Federal Fluminense University.
My research interests include: applied machine learning; explainable and interpretable AI; neural-symbolic integration; and representation learning.
News
- [20/10/2022] Our paper "A Modality-level Explainable Framework for Misinformation Checking in Social Networks" was accepted for both oral and poster presentations at LXAI @ NeurIPS 2022;
- [05/09/2022] Started as Data Science Advisor at Dell Technologies.
Experiences
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Data Science Advisor, Dell Technologies. Remote, Brazil [Sep 2022 - present]
Machine Learning Engineer, Gaivota. Remote, Brazil [Nov 2020 - Aug 2022]
Research Intern, IBM. Rio de Janeiro, Brazil [Aug 2018 - Jul 2020]
In the first year of my internship at IBM Research, I supported the development of learning-based solutions on knowledge engineering for natural resource challenges and worked on integrating RDF-based triplestores using SPARQL and Gremmelin queries with the technologies. Meanwhile, I developed the Python framework, called HKpy, for IBM Hyperlinked Knowledge Graph technologies. HKpy was the first asset in the research group to be published and maintained open-source. During the second year of my internship, I worked with more than 30 scientists on the Gazprom Neft-IBM Research Cooperation Agreement. We researched and developed a system for managing cyclic machine learning workflows; the results improved the quality and shortened lead times from three years to six to 12 months of geological prospecting operations.Research Intern, LDSOFT. Rio de Janeiro, Brazil [Jul 2017 - Jul 2018]
During my internship, we accomplished the first Brazilian trademark image retrieval system (also awarded as best paper), now used by over 3000 clients in Brazil. Thus, I was responsible for the coordination and development of the system using computer vision and pattern recognition techniques using OpenCV and scikit-learn frameworks. Further, I was a development team member of a natural language processing system for trademark name matching.Selected Publications
Looking for full publication list? See my CV or Google Scholar.
2022
2021
Workflow Provenance in the Lifecycle of Scientific Machine Learning.
Renan Souza, Leonardo Azevedo, Vítor Lourenço, Elton Soares, Raphael Thiago, Rafael Brandão, Daniel Civitarese, Emilio Vital Brazil, Marcio Moreno, Patrick Valduriez, Marta Mattoso, Renato Cerqueira, and Marco AS Netto.
Concurrency Computation Practice and Experience, 2021.
[Paper] [Preprint] [] 2020
2019
Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval.
Vítor N. Lourenço, Gabriela G. Silva, and Leandro A.F. Fernandes.
Conference on Graphics, Patterns and Images (SIBGRAPI), 2019.
Best Computer Vision/Image Processing/Pattern Recognition Main Track Paper Award.
Best Work Award in the Workshop of Undergraduate Works (WUW).
[Paper] [Preprint] [Code] [] Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering.
Renan Souza, Leonardo Azevedo, Vítor Lourenço, Elton Soares, Raphael Thiago, Rafael Brandão, Daniel Civitarese, Emilio Vital Brazil, Marcio Moreno, Patrick Valduriez, Marta Mattoso, Renato Cerqueira, and Marco AS Netto.
Workflows in Support of Large-scale Science (WORKS), 2019 co-located with the ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC), 2019.
[Paper] [Preprint] [] 2018