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Preprint of ASE ’23 DL refactoring paper now available
A preprint of our ASE ’23 paper on refactoring imperative Deep Learning programs to graphs is now available.
Fully-funded Ph.D. student position(s) in analysis and transformations of Deep Learning programs in New York City
I am currently seeking (potentially multiple, fully-funded) Ph.D. students interested in programming languages and software engineering research for an NSF-funded project on analysis and transformations for (imperative) Deep Learning (DL) programs. The project—based in the heart of New York City—focuses on enhancing the robustness, increasing run-time performance, and facilitating the long-lived evolution of DL systems, particularly, large, industrial DL systems. For more information on the project, please see the project announcement.
Potential research topics explored during the project may include (static/dynamic) program analysis and transformation (e.g., automated refactoring) and empirical software engineering. Successful candidates will be expected to work on projects that generally yield open-source developer tool research prototypes, plug-ins to popular IDEs, build systems, or static analyzers. Applicants may find additional information on the PI’s web page. They should also apply to the City University of New York (CUNY) Graduate Center (GC) Ph.D. program in Computer Science (deadline January 15) following a discussion with the PI.
Please see below for additional details on applying. Again, the Ph.D. program deadline is January 15.
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