Research Projects

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Research Projects:

  • Student Name: Matthew Levan
    Project: High-performance computing using Multi-core architecture:
    Using graphics processing units (GPUs) for performing faster computation on Big Data is been continuously explored. With large number of cores the GPUs are a powerhouse for computation, however, GPUs are limited in terms of computing memory. Therefore, processing large data sets is a major challenge and this bottleneck often leads to wasted compute cycles on these processors. In this project this issues would be studied and addressed. The major focus would be on identifying techniques to store large data sets by compressing the same, and perform computation on the compressed data itself. The techniques would be evaluated both for space and time overheads as compared to existing methods.

  • Student Name: Crosby Lanham
    Project: Non-text based analysis for Online Social Networks
    With the growth of Online Social Networks (OSNs), millions of users have started using it as a resource for information; this has spurred growth and promotion of business as well on this platform. People often express their feeling and feedback using emoticons instead of plain text. Analyzing emoticons to predict the correct mood when combined with words can help identify sarcasm and other hidden expressions. In this project the issue of emoticons and other non-text based information in OSNs would be studied. The major focus would be on identifying the usage and importance of non-text based information in OSNs.

  • Student Name: Mishael Zerrudo
    Project: Performance prediction and optimization for cloud computing
    With the pervasive usage of cloud computing, both storage and computing have evolved for small and large industry as well as individuals. Placing data for optimal storage and retrieval is important from the perspective of both the user and the provider of such service. In this project the performance prediction and optimization of using cloud resources would be studied. The major focus would be on introducing and comparing techniques to utilize the cloud resources to maximize provider revenue and minimize bandwidth usage.



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