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Fully-funded Ph.D. student position(s) in analysis and transformations of Deep Learning programs in New York City

I am currently seeking (potentially multiple, fully-funded) Ph.D. students interested in programming languages and software engineering research for an NSF-funded project on analysis and transformations for (imperative) Deep Learning (DL) programs. The project—based in the heart of New York City—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 project may include (static/dynamic) program analysis and transformation (e.g., automated refactoring) and empirical software engineering. 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. They 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.

Please see below for additional details on applying. Again, the Ph.D. program deadline is January 15.

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Alexi Turcotte from Northeastern visits CUNY

Alexi Turcotte (website) from Northeastern University visited CUNY last week and gave a talk on asynchronous JavaScript at our graduate student event at the CUNY Graduate center. Alexi is a 5th year Ph.D. candidate at Northeastern University. Frank Tip and Jan Vitek advise him; with Frank, he works on optimizing asynchronous JavaScript programs; with Jan, he works on fuzzing and type system design for the R programming language. He is interested in anything related to dynamic and data science languages.

The talk, entitled “Detecting and Repairing Anti-Patterns in Asynchronous JavaScript,” was the keynote that kicked off a series of lightning talks by other graduate students. An abstract and photos from the event may be found below. Thank you, Alexi, for visiting CUNY!

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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!

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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.

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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.

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Highlights of “Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study”

In this blog post, we summarize, using code examples, our recent empirical study on challenges in migrating imperative Deep Learning programs to graph execution.

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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.

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