Alexander Aved


Lecture Information:
  • April 28, 2016
  • 11:00 AM
  • ECS: 243

Speaker Bio

Dr. Alex J. Aved received the BA degree in Computer Science and Mathematics in 1999 from Anderson University in Anderson, Indiana, an MS degree in Computer Science from Ball State University, Muncie, Indiana and PhD in Computer Science in 2013 from the University of Central Florida, Orlando, Florida. Alex’s research interests include multimedia databases, stream processing (via CPU, GPU or coprocessor) and dynamically executing models with feedback loops incorporating measurement and error data to improve the accuracy of the model. Dr. Aved has been a researcher with the Air Force Research Lab, Information Directorate, located in Rome, NY for three years.


“With heightened security concerns across the globe and increasing need to monitor, preserve and protect critical infrastructure and public spaces to ensure proper operation, quality assurance and safety, video is typically leveraged, resulting in a proliferation of real-time streaming video content. Accordingly, there is a need for it to be monitored effectively and efficiently. However, leveraging human operators to constantly monitor all the video streams is not scalable or cost effective. People can become subjective, fatigued, even exhibit bias and it is difficult to maintain high levels of vigilance when capturing, searching and recognizing events that occur infrequently or in isolation. Some of these limitations are addressed in the Live Video Computing framework for managing and fusing live motion imagery data via user-defined queries. It enables rapid development of video surveillance software much like traditional database applications are developed around relational databases. A user requesting information can refine the analysis via queries, which may also be semantic in nature. A query that is continuously refined reduces uncertainty and the information can be used in the ontology as part of the source, evaluation, and information quality. For example, a user receiving a response to a query can determine if the response meets the quality, evaluation and source criteria.