CV Lattes
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Universidade Estadual Paulista (UNESP). Campus de Bauru. Faculdade de Ciências (FC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Initially, Gustavo has pursued his undergraduate diploma not knowing which was the right path. Bachelor in Computer Science at Universidade Estadual Paulista Júlio de Mesquita Filho, FC/Bauru (2016), he could grasp some good signs by taking part into a research laboratory and being a Scientific Initiation FAPESP's scholarship holder, also having done a BEPE internship at Harvard University.And so it seemed, definitely, that Artificial Intelligence trends appeared to be the righteous path. Image processing formulations, pattern recognition, pattern classification, machine learning algorithms, meta-heuristic optimization, all of these trends encouraged him to invest in his academic career and achieve a Master of Science in Computer Science at Universidade Estadual Paulista Júlio de Mesquita Filho, IBILCE/Rio Preto (2018). Fostering even more on machine learning algorithms, research on artificial neural networks and deep learning areas were made conceivable due to a former Master of Science FAPESP's scholarship holder. Also, FAPESPs funding made it possible to participate in a BEPE internship at the University of Virginia. Finally, he achieved his Ph.D. (former FAPESP scholarship holder) at Universidade Estadual Paulista Júlio de Mesquita Filho, FC/Bauru (2022), with emphasis in natural language processing and adversarial learning.Recently, he is taking part into a new journey as a Senior Research Software Engineer at Microsoft Research. (Source: Lattes Curriculum)
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Machine learning techniques have been paramount in the latest years, mainly due to their high effectiveness in computer vision and Natural Language Processing (NLP) problems. Despite their significant discriminatory power, their generative capacities are far from having an ideal performance in specific applications, often related to text-data mining ones. The most significant setback occu…
Deep learning architectures have been extensively studied in the last years, mainly due to their discriminative power and effectiveness in many crucial problems in computer vision, such as face and people identification, as well as object recognition, just to name a few. However, one problem related to these models concerns with their number of parameters, which can easily reach thousands…
Person biometric recognition has been widely employed in the last decades aiming a better security level in a wide sort of applications. Amongst the available techniques, one of the most reliable is the fingerprint, which is commonly used for forensic detectives, since it can be acquired without an individual consent in a crime scene, for instance. In this work, we propose to study the vi…
Deep learning-based approaches have been paramount in the last years, mainly due to their outstanding results in several application domains, that range from face and object recognition to handwritten digits identification. Convolutional Neural Networks (CNN) have attracted a considerable attention since they model the intrinsic and complex brain working mechanism. However, one main short…
Deep learning techniques have been widely used in the last years due to their promising results in several applications, mainly face and object detection. However, one of their main shortcomings is related to the selection of suitable parameters in order to allow reasonable results. Since we may have millions of parameters, a manual fine tuning of them seems to be impractical. In this res…
| 4 / 3 | Completed scholarships in Brazil |
| 2 / 2 | Completed scholarships abroad |
| 6 / 5 | All Scholarships |
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