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Computer Science Professor Awarded NSF Grant to Improve Machine Learning Systems | CUNY Graduate Center
A story from the CUNY Graduate Center news!
Professor Raffi Khatchadourian receives nearly $600K to address technical debt, which affects machine learning systems found in self-driving cars, medicine, and other fields.
Multiple fully-funded Ph.D. student positions in combating technical debt in Machine Learning (ML) systems in New York City
I am currently seeking multiple fully-funded Ph.D. students interested in programming languages and software engineering research for an NSF-funded project on combating technical debt in Machine Learning (ML) systems. The project—based in the heart of New York City—focuses on facilitating the long-lived evolution of ML systems through automated refactoring.
Potential research topics explored during the project may include (static/dynamic) program and data analysis and transformation, empirical software engineering, natural language processing (NLP), and Large Language Models (LLMs). 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 and 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. Students wishing to start earlier should speak with the PI.
(more…)Received three-year NSF research grant on combating technical debt in Machine Learning systems as PI
I am pleased to announce that I have received a three-year standard research grant from the National Science Foundation (NSF) Software & Hardware Foundations (SHF) program as principal investigator (PI) for a project entitled “Knowledge, Methodologies, and Tool-support for Combating Technical Debt in Machine Learning Systems.” The total grant amount is ~$600K.
(more…)Preprint of ICSE ’21 ML systems study paper now available
A preprint of our ICSE ’21 paper on studying refactoring and technical debt in Machine Learning systems is now available.
Paper on refactorings and technical debt in Machine Learning systems accepted at ICSE 2021
Our paper entitled, “An empirical study of refactorings and technical debt in Machine Learning systems,” has been accepted to the main technical research track at the 2021 International Conference on Software Engineering (ICSE)! Out of 602 papers, 138 were accepted, amounting to a 23% acceptance rate. Congrats to Yiming, Mehdi, Rhia, Ajani, and Anita, and thank you for all of your hard work!
