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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.

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!

Slides from GMU talk about challenges in executing imperative Deep Learning programs as graphs

Slides from my talk at George Mason University (GMU) on “Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study” are now available.

“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!

Talk at Stevens Institute of Technology, March 2022

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!