CISE Faculty Affiliates Win Hariri Institute Research Awards

Profs. Starobinski, Egele, and Kulis are Spring 2020 Research Incubation Awardees

The Hariri Institute for Computing announced their 2020 Spring Research Incubation Awards to faculty who have the potential to define new areas of research; three CISE faculty affiliates are authors or co-authors of these incubation projects.

David Starobinski,
Prof. ECE & SE
 

CISE faculty affiliate Prof. David Starobinski is the PI of the Automated Threat Modeling for Connected Vehicles project, which will investigate challenges surrounding compromised vehicles that may send forged safety messages. This can potentially harm other road participants who are in the vicinity of the vehicle. In order to combat this problem, Starobinski’s team will explore and expand the use of open-source threat modeling methods to detect dangers in the vehicular environment.

Portrait of Manuel Egele
Manuel Egele,
Assistant Prof. ECE

CISE faculty affiliate Assistant Prof. Manuel Egele is the PI of the reFuzz: Reusing Fuzzing Results to Improve Security Assessments project. As the cyber-security of products has been a growing concern for manufacturers, various security assessment and analysis tools have been developed. Premiere among them is fuzzing, a repeated invocation of a Program Under Test (PUT) on random inputs to elicit erroneous program behavior. Egele is working toward fuzzers that will work with leverage security knowledge of previous fuzzing experiments, and will specifically aim to have fuzzers assess newly added codes or modified functionality in a PUT rather than codes that already exist.

ece.faculty.profile.picture.brian.kulis
Brian Kulis,
Assistant Prof. ECE & SE
 

CISE faculty affiliate Assistant Prof. Brian Kulis is Co-PI of the Multi-scale Modeling of Complex  Interfaces Aided by Machine Learning project, which will investigate the complex interfaces in material systems.  Kulis will work with PI Emily Ryan and Co-PI Sahar Sharifzadeh to create a multi-scale modeling framework leveraging state-of-the-art machine learning tools to stimulate and understand the fundamental physics at the interface, with the goal of designing improved material systems

Adapted from a BU College of Engineering ECE News article authored by Colbi Edmonds published June 15, 2020.