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Paper on speculative refactoring of imperative Deep Learning programs to graph execution directly accepted to ASE ’25
Our paper entitled, “Speculative Automated Refactoring of Imperative Deep Learning Programs to Graph Execution” has been directly accepted to the research papers track at the 2025 IEEE/ACM International Conference on Automated Software Engineering (ASE)! Out of 1190 submissions, 113 were directly accepted, amounting to a 9.5% acceptance rate for directly accepted papers. The conference will take place later this year in Seoul, South Korea. Congratulations to Tatiana, Mehdi, Nan, and Anita!
Tool demo paper on refactoring imperative DL programs to graph execution accepted at FASE ’25
Our paper entitled, “Hybridize functions: A tool for automatically refactoring imperative Deep Learning programs to graph execution,” has been accepted at the 2025 International Conference on Fundamental Approaches to Software Engineering (FASE) as a formal tool demonstration! Out of 31 papers (including 4 tool papers), 11 (including 1 tool paper) were accepted, amounting to a 35% acceptance rate. Congrats to Tatiana, Mehdi, Nan, and Anita!
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.
Paper on refactoring imperative Deep Learning programs to graphs accepted at ASE ’23 NIER
Our paper entitled, “Towards Safe Automated Refactoring of Imperative Deep Learning Programs to Graph Execution” has been accepted to the New Ideas and Emerging Results (NIER) track at the IEEE/ACM 2023 International Conference on Automated Software Engineering (ASE)! Out of 70 papers, 25 were accepted, amounting to a 35.7% acceptance rate. The conference will take place later this year in Kirchberg, Luxembourg.
Congratulations to Tatiana, Mehdi, Nan, and Anita, and thank you for all of your hard work!
Received three-year NSF research grant on imperative Deep Learning program robustness and evolution as PI
I am pleased to announce that I, along with co-PI Anita Raja, 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 “Practical Analyses and Safe Transformations for Imperative Deep Learning Programs.” The total grant amount is $600K.
The project will facilitate the robustness and automated evolution and maintenance of large, industrial Deep Learning (DL) software systems that use imperative style programming. More information may be found on NSF’s website; stay tuned for more details and funded research opportunities!
“Migrating Imperative Deep Learning Programs to Graph Execution” guest lecture on YouTube
Thanks to Stevens Institute of Technology for posting my guest lecture on imperative Deep Learning program execution to YouTube!
Paper on hybridization challenges in imperative Deep Learning programs accepted at MSR ’22
Our paper entitled, “Challenges in migrating imperative Deep Learning programs to graph execution: An empirical study,” has been accepted to the main technical research track at the IEEE/ACM SIGSOFT 2022 International Conference on Mining Software Repositories (MSR)! Out of 138 papers, 45 were accepted, amounting to a 32.6% acceptance rate. The conference will take place later this year in Pittsburgh and is co-located with ICSE 2022.
A special congratulations to Tatiana for publishing her first full conference paper as first-author in the second year of her Ph.D. studies! Also congrats to Mehdi and Anita, and thank you for all of your hard work!
