Three professors in the department receive CAREER Awards
Three professors from the Department of Computer Science have received CAREER Awards from the National Science Foundation—the most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization.
Assistant professor and security researcher Jeremiah Blocki, received a CAREER Award from the National Science Foundation (NSF). His project is dedicated to developing cryptographic tools to improve the security and usability of human authentication, especially password authentication. The project plans to develop new combinatorial techniques to design and analyze memory hard functions, a cryptographic primitive which can be used to protect low-entropy secrets such as master passwords, general passwords, and biometrics against brute-force attacks.
Assistant professor and security researcher Christina Garman, received a CAREER Award from the National Science Foundation (NSF) to study solutions to the human element problem. Her project is dedicated to building tools to aid in the deployment of complex cryptography, automating the discovery of vulnerabilities in popular zero-knowledge proof instantiation, and automate the discovery and identification of modern cryptographic algorithms and techniques.
Associate Professor Dan Goldwasser received a CAREER Award from the National Science Foundation (NSF) to develop novel modeling techniques and learning algorithms for combining linguistic content and its necessary social context under a common innovative principle - creating a socially grounded language representation that views opinion understanding as part of a larger framework of understanding real-world scenarios and their participants. This new way to conceptualize opinionated text analysis will produce highly nuanced analysis of social media that captures the stances, attitudes, and relationships between the different stakeholders of a given real-world scenario.