Main Page

From PgroupW

Jump to: navigation, search

Welcome to the Wiki page for Prof. Viktor Prasanna's Groups (PGroup)

Research Groups

FPGA/Parallel Computing Group

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.

Cloud Computing Group

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

Big Data Analysis Group

Research in this group is focused on addressing the complex integration issues and analyzing heterogeneous datasets that arise in real-world Big Data applications. Current projects include developing a Semantic Web-based framework for integration of large databases, time-series modeling and analysis of large spatio-temporal data streams, and processing of unstructured text data. We also develop machine learning and networked data modeling algorithms to extract patterns in social network communications and build collaboration applications for the enterprise. Dr. Anand Panangadan manages this group. Big Data Analysis | Publications | Members

Smart Grid Group

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

Team Members

The P Group Team.


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
Web page:
Charalampos Chelmis
Research Associate, Electrical Engineering
3740 McClintock Ave, EEB 218, Los Angeles, CA 90089.
Ph: (213) 740-9129 | E-Mail: chelmis AT
Web page:

Yogesh Simmhan
Adjunct Research Assistant Professor, Electrical Engineering
3740 McClintock Ave, EEB 218, Los Angeles, CA 90089.
Ph: (213) 740-9129 | E-Mail: simmhan AT
Web page:


  • Postdoc Position in Big Data Analytics

A Postdoctoral Research Associate position in Big Data Analytics is open for appointment at the Ming Hsieh Department of Electrical Engineering (Computer Engineering), University of Southern California, Los Angeles. The postdoc will lead a team of graduate students in performing independent research on information extraction and analysis of large heterogeneous data sources with techniques from data mining and scalable machine learning and apply these to address challenges in the energy sector. The position will also involve management of existing projects, with regular reporting and presentations of project progress to sponsors. The postdoc will be expected to collaborate with faculty and other researchers in writing innovative research proposals to funding agencies such as NSF, DARPA, and DoE.

The position will provide the opportunity to participate in the activities of The Center for Energy Informatics (CEI), one of the first institutions to conduct exploratory and applied research into information collection, integration, management, and analysis of energy assets to enhance energy sustainability. The center, led by Viktor Prasanna (Professor, EE and CS, USC), brings together USC experts from the departments of Electrical, Civil, Environmental and Chemical engineering, Computer Science, Sociology and Office of Sustainability.

The position is ideally suited for candidates interested in advancing to a faculty or industrial research career. We invite candidates with a research background in any of the following areas: Big Data management, machine learning, data mining, databases, and Semantic Web. Experience in working on inter-disciplinary and team projects is a plus. Applicants must have earned a Ph.D. or foreign equivalent in a relevant field at the time of appointment. In addition to technical skills, applicants must also have demonstrated written and oral communication skills (publications, presentations, talks, etc.). The candidate will work under the guidance of Prof. Viktor Prasanna. The successful candidate is expected to start in Fall 2015.

Applicants should email their detailed CV, list of references, three representative publications, and a brief research statement to

  • 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.

Personal tools