CV Lattes
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Universidade Federal de São Carlos (UFSCAR). Campus de Sorocaba. Centro de Ciências em Gestão e Tecnologia (CCGT) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Jurandy Almeida is an Associate Professor at the Department of Computing of the Federal University of São Carlos (UFSCAR), campus of Sorocaba (02/2022-present) and a Collaborator Professor in the Computer Science Post-Graduate Program from the Federal University of São Paulo (UNIFESP), campus of São José dos Campos (02/2022-present). He received his B.Sc. in Computer Science (2004) from Sao Paulo State University (UNESP) and his M.Sc. (2007) and Ph.D. (2011) degrees in Computer Science from University of Campinas (UNICAMP). He used to hold a position as an Assistant Professor at the Institute of Science and Technology of the Federal University of São Paulo (UNIFESP), campus of São José dos Campos (08/2013-01/2022). He was a visiting scholar at the Dept. of Computing Science, University of Alberta in Edmonton, Canada (05/2011-08/2011), a postdoctoral research fellow at the Institute of Computing, UNICAMP (12/2011-08/2013), an academic visitor at the School of Computer Science and Electronic Engineering, Bangor University in Bangor, United Kingdom (01/2017-02/2017), and a visiting researcher at the Dept. of Information Engineering and Computer Science, University of Trento in Trento, Italy (02/2019-02/2020). Jurandy is author/co-author of more than 100 articles in refereed journals and conferences and serves as program committee member and reviewer for several international and national conferences. His publications have been cited over 2,000 times and his h-index on Google Scholar is 26. He has been developing multidisciplinary research projects involving Computer Vision, Deep Learning, Image Processing, Information Retrieval, Machine Learning, and Pattern Recognition. (Source: Lattes Curriculum)
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This project proposes a collaborative research initiative between the Federal University of São Carlos (UFSCar) and the Sorocaba Engineering School (FACENS), in Brazil, and the Technical University of Ingolstadt (THI), in Germany, under the Seed Fund Call for Proposals 2024, Track 1. The primary objective is to strengthen international partnerships between Brazil and Germany while deliver…
Digital videos have become the medium of choice for a growing number of people communicating via Internet and their mobile devices. Over the past decade, world has witnessed an explosive growth in the amount of video data fostered by astonishing technological developments. In this scenario, there is a growing demand for efficient systems to reduce the work and information overload for peo…
Several machine learning techniques have relied on large labeled data sets to construct predictive models and solving supervised learning tasks. The use of deep learning techniques can be highlighted, since it have been broadly and successfully used in various domains. On the other hand, in many circumstances, the labeled sets are unavailable or insufficient to train effective supervised …
Due to the rapid advances in data acquisition and transmission technologies, people are constantly inundated by information in form of digital video. In this scenario, there is a growing demand for efficient systems able to manage large volumes of video data and reduce the work and information overload when seeking a given content of interest. One of the main challenges in developing effe…
With the popularization of social media platforms focused on user-generated content, a huge volume of images and videos are being generated daily. The cataloging and searching of this content is necessary, however, manually analyzing this immense amount of content is practically impossible, making the use of systems capable of automatically capturing the meaning of this content crucial. T…
Nowadays the use of computational vision methods on real-world problems is growing mainly by the achievement of better results on image and video recognition tasks. These results have been achieved by the evolution of machine learning methods, specifically by deep neural networks. However, these methods are usually adapted for tasks where their limits are well known, and have low flexibil…
Digital videos have become the medium of choice for a growing number of people communicating via Internet and their mobile devices. Over the past decade, world has witnessed an explosive growth in the amount of video data fostered by astonishing technological developments. In this scenario, there is a growing demand for efficient systems to reduce the work and information overload for peo…
Deep learning (DL) methods have brought revolutionary advances to many research areas due to their capacity of learning from raw data. Particularly, convolutional neural networks (CNNs) have substantially increased the development of intelligent multimedia systems, like natural language processing and computer vision systems. However, when those methods are applied to non-controllable env…
Digital videos have become the medium of choice for a growing number of people communicating via Internet and their mobile devices. Over the past decade, world has witnessed an explosive growth in the amount of video data fostered by astonishing technological developments. In this scenario, there is a growing demand for efficient systems to reduce the work and information overload for peo…
| 2 / 2 | Ongoing grants |
| 2 / 2 | Completed research grants |
| 1 / 1 | Ongoing scholarships in Brazil |
| 6 / 3 | Completed scholarships in Brazil |
| 2 / 2 | Completed scholarships abroad |
| 13 / 10 | All research grants and scholarships |
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