Welcome to the Wiki page for Prof. Viktor Prasanna's Groups (PGroup)
Research in the parallel computing group is focused on solving problems from high speed network processing, data intensive computing and high performance computing. Examples are virtualized packet classification and deep packet inspection for the Internet backbone, large-scale pattern matching, belief propagation and graph inference. We investigate new algorithms and algorithm-architecture mapping to optimize our solutions on parallel and/or heterogeneous architectures including Field-Programmable Gate Arrays (FPGA), general purpose multi-core (CPU) and graphics (GPU) processors.
Objective of our team is to address complex interdependence issues currently faced by big data community. Particularly, we focus our attention on data integration and processing requirements recognized in the community. In practice, data processing and analytics processes are carried out essentially to aid decision making process of various stakeholders in a specific domain. Therefore, effectiveness of any approach can be established based on the ability to accurately identify and meet information needs for stakeholders. Dr. Anand Panangadan manages this group. Big Data Integration and Analytics Page | Publications | Members
In this research project, we examine knowledge generation under informal social communications, based on semantically enriched user generated data. Modeling users with their generated content, we are able to dynamically capture users’ interests. Knowledge networks of users emerge, exhibiting collective intelligence. To capture such collective knowledge, we propose a novel knowledge base paradigm, which seamlessly integrates information from multiple platforms and facilitates knowledge extraction, mining, discovery and inferencing. We use such integrated, semantically rich information to calculate semantic similarity between people under the context of informal social interactions, driving numerous applications. Dr. Anand Panangadan manages this group. Computational Social Sciences Page | Publications | Members
The group is engaged in research into the software components needed to build a Smart Power Grid, as part of the DOE sponsored Los Angeles Smart Grid Demonstration project. It brings together techniques from complex event and stream processing, machine learning and semantic information integration to build a scalable software architecture on a Cloud platform that will support the next generation of Smart Grid applications. The project explores the informatics challenges and possibilities that can transform a physical power grid into a cyber-physical smart grid that actively informs, engages and responds to millions of consumers to achieve a sustainable ecosystem. This ﬁve year project is conducting innovative research to identify, deploy and demonstrate effective tools and technology to make the Los Angeles power grid a smart grid. Dr. Yogesh Simmhan manages this group. Smart Grid Home || Data Analytics | Complex Event Processing | Cloud Computing || Publications
The Cloud group explores scalable frameworks for data driven applications and algorithms for large scale data analytics. Our research on graph analytics compares the flexibility and scalability of different programming models for common graph algorithms on Cloud platforms such as Microsoft Azure. We also investigate optimal tuning of Hadoop for training machine learning models over massive datasets. Another active area of research is adaptive dataflow engines to support the dynamic, distributed and data intensive needs of emerging applications on elastic and unreliable Cloud infrastructure. We also focus on scientific data repositories that ensure security of sensitive datasets on public Clouds while offering the scalability and manageability. Dr. Yogesh Simmhan manages this group. Cloud Computing Home || Graph Analytics | Adaptive Dataflows | Scalable Machine Learning | Secure Data Repository || Publications
- Viktor K. Prasanna
- Professor of Electrical Engineering & Computer Science
- Ming Hsieh Department of Electrical Engineering
- University of Southern California
- 3740 McClintock Ave, EEB 200C, Los Angeles, CA 90089-2562.
- Ph: (213) 740-4483 | Fax: (213) 740-4418 | E-Mail: prasanna AT usc.edu
- Web page: http://ceng.usc.edu/~prasanna
- Yogesh Simmhan
- Research Assistant Professor, Electrical Engineering
- 3740 McClintock Ave, EEB 218, Los Angeles, CA 90089.
- Ph: (213) 740-9129 | E-Mail: simmhan AT usc.edu
- Web page: http://ceng.usc.edu/~simmhan
- Anand Panangadan
- Senior Research Associate, Electrical Engineering
- 3740 McClintock Ave, EEB 218, Los Angeles, CA 90089.
- Ph: (213) 740-9129 | E-Mail: anandvp AT usc.edu
- Web page: http://ceng.usc.edu/~anandvp
- Shel Swenson
- Postdoctoral Research Associate, Electrical Engineering
- Ph: TBD | E-Mail: shel.swenson AT usc.edu
- Research Positions
We have openings for highly motivated students interested in pursuing a Ph.D. in any of the above topic areas. Research internships are also available for highly qualified M.S. students. Send an email with a brief research statement, the group/project you are interested in, copies of publications (if any) and your detailed CV including courses taken and grades obtained to Prof.Prasanna. Research assistants receive a competitive stipend plus paid tuition. Applicants can apply to either the Computer Science department or to the Computer Engineering program within the Electrical Engineering department.