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Talk at NEPLS at Harvard University

I gave a talk at the New England Programming Languages and Systems (NEPLS) symposium at Harvard University earlier this month.

Reviewer for Empirical Software Engineering (EMSE)

I am pleased to announce that I have been asked to review for the Empirical Software Engineering (EMSE) international journal!


Program committee (PC) member for ICSE ’24

Excited and honored to be invited to the program committee (PC) for the ICSE ’24 technical track! The conference will take place in Lisbon, Portugal between April 12 and April 21. The first deadline is on March 29, 2023. Please consider submitting!

Invited to serve on ISEC ’23 SRC Program Committee (PC)

I am honored to serve as a program committee member for the ACM SIGSOFT Innovations in Software Engineering Conference (ISEC ’23) Student Research Competition (SRC).

Talk at University of Tokyo

On August 18, I visited Professor Shigeru Chiba at the Core Software Group of the Dept. of Creative Informatics Graduate School of Information Science and Technology at The University of Tokyo. I gave a talk about preliminary research in automated refactoring of Deep Learning software.


Visiting Tokyo Tech

Between August 10 to 24, 2022, I visited the Programming Research Group at the Department of Mathematical and Computing Science of the Tokyo Institute of Technology. I gave a seminar talk and discussed current research with the group members. A JSPS BRIDGE fellowship supported this visit, planned initially two years ago. The trip was postponed due to COVID-19 (three times, in fact), but I was happy to have the opportunity to visit Professor Masuhara and his lab.


NYU GSTEM students visit during the summer of 2022

Medha Belwadi and Pranavi Gollanapalli 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. Medha and Pranavi will be working on our recently funded NSF project on imperative Deep Learning system programming and evolution as part of the project’s broader impacts.


Ph.D. student position(s) in analysis and transformations of imperative Deep Learning programs

I am currently seeking (potentially multiple) Ph.D. students interested in programming languages and software engineering research for a newly NSF-funded project on analysis and transformations for imperative Deep Learning (DL) programs. The project focuses on enhancing the robustness, increasing run-time performance, and facilitating the long-lived evolution of DL systems, particularly, large, industrial DL systems. For more information on the project, please see the project announcement.

Potential research topics explored during the course of the project may include (static/dynamic) program analysis and transformation (e.g., automated refactoring). The successful candidates will be expected to work on projects that generally yield open-source developer tool research prototypes, typically plug-ins to popular IDEs, build systems, or static analyzers. Potential applications may find more information  on the principal supervisor’s web page. After discussing with me, potential students should apply to the City University of New York (CUNY) Graduate Center (GC) Ph.D. program in Computer Science.

Please see below for additional details on applying. 


Program Committee (PC) member for ICSME ’22 doctoral symposium

I am honored to be invited to serve on the Program Committee (PC) for the IEEE International Conference on Software Maintenance and Evolution (ICSME ’22) Doctoral Symposium! Please consider submitting! Full paper submissions are due July 8, 2022.

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!

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