Cybersecurity and Information Assurance

 

Photo of Lance Hoffman

 

Cyber Security and Privacy Research Institute (CSPRI)

The Cyber Security and Privacy Research Institute is a center for GW and the Washington area to promote technical research and policy analysis of problems that have a significant computer security and information assurance component. CSPRI's mission is to encourage, promote, facilitate, and execute interdisciplinary research in these areas, including the exploration of the norms, governance issues, and operating systems of cyberspace. Professor Lance Hoffman is the founder of CSPRI. Learn more about Professor Hoffman.

 

Photo of Professor Poorvi Vora

 

Professor Poorvi Vora

Professor Vora has been at the forefront of research in the field of independently-verifiable voting systems. These modern cryptographic voting systems provide a digital audit trail for the election, which can be checked by anyone. In particular, voters and observers can independently verify the outcome of a secret-ballot election, even if election officials and voting machines do not perform their functions correctly. Research prototypes developed by us and collaborators have been used by the City of Takoma Park to hold the world's first ever independently-verifiable secret-ballot election for public office. Learn more about Professor Vora.

 

Photo of Claire Monteleoni

 

Machine Learning Group

Machine Learning Group's research is concerned with developing principled methods (known as algorithms) to automatically detect patterns in data. In this era of "Big Data," the various forms of complexity inherent in real data sources increasingly pose challenges for machine learning algorithm design. The GW Machine Learning Group works on the design, analysis, and application of machine learning algorithms, motivated by problems in real data sources, including learning from data streams, learning from raw (unlabeled) data, learning from private data, and climate informatics: accelerating discovery in climate science with machine learning. Learn more about Professor Monteleoni.