PhD Candidate (Computer Science, Privacy, Security), competitive billiards player, mountaineer, coffee and music connoisseur, blogger, amateur writer and taker-of-pictures, armchair philosopher, bookworm, surfer, and lover of leather, scotch, and engaging conversations. Caleb Shortt is a man of many hats.
Caleb received his B.Sc. in Computer Science (Software Engineering option) in 2012, and his M.Sc. in Computer Science (Using learning algorithms, static analysis, and fuzz testing to contribute to security assurance) in 2015.
His research interests include software security (and assurance), privacy, genetic (and other learning) algorithms, data mining, automated test systems, and black and white-box testing.
Really, anything that is security in nature peaks the interest of this handsome devil.
Working with an industry leader in software security to explore applications of data mining and machine learning algorithms for security applications.
Context: M.Sc. Thesis
Hermes is an automated white-box fuzz testing framework. It uses genetic algorithms and code coverage to selectively target the sections of code that contain the highest-severity ‘potential defects’.
R-Gauge Metrics Inc
Context: External Project
R-Gauge Metrics Inc. was the brainchild of Caleb. The company’s flagship product was an automated system that utilized learning algorithms and data mining techniques to generate a reputation “score” for companies and products.