Edge is adapting EdgeCon 2021 to a virtual gathering where leaders from Higher Education, K-12, Government, and Healthcare organizations will come together to discuss technology breakthroughs and best practices. With the theme of “Vision to Value,” EdgeCon will provide insights to help you execute on the promise of innovation and transformation through
Topic: Data Analytics
Dr. R. Scott Starsman is the Director for Defense Systems with Avineon, Inc. He is responsible for a portfolio of Information Technology projects supporting Defense and Federal customers. He has successfully delivered Machine Intelligence, Knowledge Management, Business Process Modeling, and advanced Geographical Information Systems solutions to demanding
FloCon provides a forum for exploring large-scale, next-generation data analytics in support of security operations. FloCon is geared toward operational analysts, tool developers, researchers, security professionals, and others interested in applying cutting-edge techniques to analyze and visualize large datasets for protection and defense of networked
Dr. Wemlinger is a Senior Data Scientist at Syracuse Research Corporation (SRC) with over 15 years of research experience in the areas of low-pressure high-temperature plasma, high-pressure low-temperature plasma, energetic materials, finite element modeling, physics education, data analysis, and algorithm development. He is skilled at integration of
Monika Akbar is an Assistant Professor in Computer Science at the University of Texas at El Paso. Her research interests include information storage and retrieval, data and information management, data analytics, and cybersecurity.
The goal of the symposium is to showcase current cyber-enabled technologies to provide new approaches to national security and military operations. New technologies will expand as innovators see the potential for cyberspace beyond support for intelligence and traditional military operations. The symposium will offer a forum for industry, government, and
In this presentation, we present a novel approach to detection of bots on social networks in near real-time. Our approach comprises of computationally simple comparisons and calculations, as opposed to the all too common machine learning approach to this problem, or non-real-time approaches that involve network analysis which is both expensive and