Florida International University
Samira Pouyanfar is a Ph.D. candidate at the School of Computing and Information Sciences (SCIS), Florida International University (FIU), under the supervision of Professor Shu-Ching Chen. She received a Master degree in Artificial Intelligence from Sharif University of Technology (SUT), Iran in 2012, and a Bachelor degree in Computer Engineering from University of Isfahan, Iran in 2008. She is currently working as a research assistant in the Distributed Multimedia Information System (DMIS) lab at FIU. Her research interests include data science, data mining, machine learning, multimedia processing, big data, healthcare, and information retrieval. She has published over 20 research papers in international journals and conference proceedings. During her Ph.D., she received several awards including overall Outstanding Graduate Student Award at the School of Computing and Information Sciences 2017, second place in the oral paper presentation at FIU GSAW 2017 and 2018, and SGA Graduate Scholarship 2016. Samira is also vice president of FIU’s Computer Science Graduate Student Association (CSGSA).
With the proliferation of online services and mobile technologies, the world has stepped into a multimedia big data era, where new opportunities and challenges appear with the high diversity multimedia data together with the huge amount of social data. Nowadays, multimedia data consisting of audio, text, image, and video has grown tremendously. With such an increase in the amount of multimedia data, the main question raised is how one can analyze this high volume and variety of data in an efficient and effective way. A vast amount of research has been done in the multimedia area, targeting different aspects of big data analytics, such as the capture, storage, indexing, mining, and retrieval of multimedia big data. However, there is insufficient research that provides a comprehensive framework for multimedia big data analytics and management.
To address the major challenges in this area, a new framework is proposed based on deep neural networks for multimedia semantic concept detection with a focus on spatio-temporal information analysis and rare event detection. The proposed framework is able to discover the pattern and knowledge of multimedia data using both static deep data representation and temporal semantics. Specifically, it is designed to handle data with skewed distributions. The proposed framework includes four main components: (1) an automatic sampling model to overcome the imbalanced data issue in multimedia data, (2) a deep representation learning model leveraging novel deep learning techniques to generate the most discriminative static features from multimedia data, (3) a spatio-temporal deep learning model to analyze dynamic features from multimedia data, and (4) a multimodal deep learning fusion model to integrate different data modalities using the Multiple Correspondence Analysis (MCA) algorithm. The whole framework has been evaluated using various large-scale multimedia datasets that include the newly collected disaster-events video dataset and other public datasets.