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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…)Best paper award nomination at AISafety
I am happy to announce that our paper entitled, “ReLESS: A framework for assessing safety in Deep Learning systems,” has been nominated for a best paper award at AI Safety ’24! Congrats to Nan and Anita. It’s Nan’s first workshop paper!
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!
Program committee (PC) member for ASE ’24
Excited and honored to be invited to the program committee (PC) for the ASE ’24 research track! Please consider submitting!
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…)Program committee (PC) member for ICSE ’25
Excited and honored to be invited to the program committee (PC) for the ICSE ’25 research track! The conference will take place in Ottawa, Ontario, Canada between April 27 and May 3. The first deadline is on March 22, 2025. Please consider submitting!
Tatiana presenting on DL refactoring at ASE ’23
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!
