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Paper on reliably refactoring Deep Learning systems accepted at AISafety ’24

Our paper on reliability refactoring Deep Learning systems has been accepted to the 2024 AISafety workshop at the International Joint Conference on Artificial Intelligence (IJCAI ’24). Congratulations to Nan and Anita!

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.


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.


Tatiana presenting on DL refactoring at ASE ’23

Tatiana presenting at ASE ’23

Slides for ASE ’23 NIER paper on imperative Deep Learning refactoring now available

Slides for our ASE ’23 NIER paper on our ongoing work towards automated refactoring of imperative Deep Learning programs to graph execution are now available. The talk will take place tomorrow at 1:54 pm CEST.


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.

Tatiana to present at the ASE 2023 doctoral forum

Tatiana will present at the Doctoral Forum of the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023) next month in Luxembourg! The goal of the ASE 2023 Doctoral Forum is to provide PhD students the opportunity to present and discuss their doctoral research with senior researchers in the software engineering community. Tatiana will be presenting her ongoing work on analyzing and transforming imperative Deep Learning programs in Python. Congrats, Tatiana, for having your paper accepted!

2023 NYU GSTEM students visit during the summer

Ifra Ishaq and Stephanie Yeh will join our research group this summer through the NYU GSTEM program. NYU GSTEM is a summer program for high school juniors that allows them to participate in research laboratories. The NYU Courant Institute of Mathematical Sciences offers the program and helps promote STEM to traditionally underrepresented groups, particularly females and minorities. Ifra and Stephanie will be working on a programming language project as part of our funded NSF project on imperative Deep Learning system programming and evolution‘s broader impacts.


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!

New doctoral student Benjamin Prud’homme starts Fall 2023

A new doctoral student, Benjamin Prud’homme, will join the team starting this Fall semester! Benjamin will enter the Ph.D. program in Computer Science at the CUNY Graduate Center as a CUNY Graduate Center Fellow (GCF).

Benjamin received his bachelor’s degree from Vassar College in 2022, where he was a double major in computer science and mathematics and did a correlate (minor) in music performance (classical guitar and voice). He has research experience in building and optimizing algorithms for working with simple temporal networks. His current interests lie at the intersection of software engineering, machine learning, and data science.

Benjamin is very excited to be pursuing his PhD in New York, where he has lived for the past 18 years! Benjamin has studied classical guitar since he was six years old, and plays guitar in various ensembles as well as a guitar orchestra. He also enjoys choral singing and has been a soloist with the Hudson Valley BachFest, the Vassar Chamber Singers, and the New Amsterdam Singers. In his free time, Benjamin enjoys working on pop/rock covers and watching/cheering on the New York Giants and Yankees, as well as the US Women’s National Soccer Team. Welcome to the team, Benjamin!