Justin Chen is a PhD candidate whose research explores the intersection of algorithms, machine learning, and data analysis. Specifically, Justin is making important contributions in the emerging area of learning-augmented algorithms. As a MathWorks Fellow, Justin will build upon several recent projects in this domain, including research on counting triangles in a data stream (a fundamental tool of network analytics); the classic optimization problem of online bipartite matching; and offline algorithms for fundamental graph problems. His interests also include new applications of algorithms for data science problems. In these and other projects, Justin is making innovative use of MATLAB to develop powerful new algorithms that improve speed and efficiency, with possible applications spanning from Google’s ad market to the kidney exchange program. His work has also yielded broader benefits, such as a new framework of reductions for learning-based algorithms and major progress in an open question posed by Sealfon (related to differentially private computation of shortest graph paths) that enhances his own work and holds the potential to advance many paths of discovery.