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Challenges and opportunities for developing new smart data processing techniques
时间:2019-08-01 16:00    点击:   所属单位:电子工程学院
讲座名称 Challenges and opportunities for developing new smart data processing techniques
讲座时间 2019-08-07 15:00:00
讲座地点 北校区主楼III-430
讲座人 Marcelo Keese Albertini
讲座人介绍 Marcelo Keese Albertini,毕业于巴西圣保罗大学,获巴西圣保罗大学计算机科学与计算数学博士学位,计算机科学系博士后研究员,现就职于巴西尤伯兰迪亚联邦大学(Federal University of Uberlandia),担任计算机科学系副教授,研究领域包括基于机器学习、数据挖掘和统计学的数据科学研究,用于大数据建模的算法和数据结构研究。作为与会会员参加了2017年巴西智能系统会议、第三十二届巴西数据库研讨会以及第四届国际信息和电信技术研讨会。

 
讲座内容 Humanity has reached a point where the technology capacity to produce data has long surpassed our means to meaningfully analyze them. In contrast, data have become more complex, which makes their analysis a challenging task. Nowadays, we are at the dawn of a new era when all human activities will be assisted and guided by smart data processing. Technologies for processing complex data have tremendous potential to improve the quality of life in emerging societies. Such technologies are in high demand to tackle the most urgent needs in the Brazilian society. Better and cheaper networking technology are needed for ubiquitous and low-cost education, including people living in remote sites. Improving data modelling and prediction will help us to optimize the generation of environment-friendly energy with solar and wind farms. Monitoring large areas through high-quality image processing will feed smarter models to understand and possibly act on the effects of climate change. For improving large urban sprawls, high performance data processing systems and efficient drone imaging can be employed for monitoring traffic congestion; help public security; and imaging poorly maintained neighborhoods to detection of Aedes aegypt-prone regions. All these opportunities need new algorithms and techniques to ensure the quality of the knowledge extracted from the huge amount of non-structured, noisy, non-stationary, and highly interconnected data. In this talk I will discuss the opportunities that this new era brings and how multi-national researchers can team up to share expertise and align efforts.

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