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PLDI 2021
Sun 20 - Sat 26 June 2021 Virtual Conference

The 5th Annual Symposium on Machine Programming

Due to recent algorithmic and computational advances, machine learning has seen a surge of interest in both research and practice. From natural language processing to self-driving cars, machine learning is creating new possibilities that are changing the way we live and interact with computers. However, the impact of these advances on programming languages remains mostly untapped. Yet, incredible research opportunities exist when combining machine learning and programming languages in novel ways.

This symposium seeks to bring together programming language and machine learning communities to encourage collaboration and exploration in the areas of mutual benefit. The symposium will include a combination of rigorous peer-reviewed papers and invited events. The symposium will seek papers on a diverse range of topics related to programming languages and machine learning including (and not limited to):

  • Application of machine learning to compilation and run-time scheduling
  • Collaborative human / computer programming (i.e., conversational programming)
  • Deterministic and stochastic program synthesis
  • Infrastructure and techniques for mining and analyzing large code bases
  • Interoperability between machine learning frameworks and existing code bases
  • Probabilistic and differentiable programming
  • Programming language and compiler support for machine learning applications
  • Programming language support and implementation of machine learning frameworks
  • Neurosymbolic and intentional programming

Keynote

Koushik Sen (UC Berkeley)

Invited Talks

Mike Carbin (MIT)

Phitchaya Mangpo Phothilimthana (Google)

Mukund Raghothaman (USC)

Organization

General Chair: Roopsha Samanta (Purdue)

Program Chair: Isil Dillig (UT-Austin)

Publicity Chair: Aws Albarghouthi (University of Wisconsin-Madison)

Ethics Chair: Jesmin Tithi (Intel Labs)

Program Committee

  • Farhana Aleen (NVIDIA)
  • Dana Drachsler Cohen (Technion)
  • Vinod Grover (NVIDIA)
  • Niranjan Hasabnis (Intel Labs)
  • Rania Khalaf (IBM Research)
  • Kuldeep S. Meel (National University of Singapore)
  • Erik Meijer (Facebook)
  • Sasa Misailovic (University of Illinois at Urbana-Champaign)
  • Augustus Odena (Google)
  • Alex Polozov (Microsoft Research)
  • Alex Ratner (University of Washington)
  • Calvin Smith (University of Texas Austin)

Steering Committee

  • Raj Barik (Uber)
  • Alvin Cheung (UC-Berkeley)
  • Stefano Ermon (Stanford University)
  • Justin Gottschlich (chair, Intel Labs / Penn)
  • Costin Iancu (Lawrence Berkeley National Lab)
  • Mayur Naik (University of Pennsylvania)
  • Kunle Olukotun (Stanford University)
  • Tatiana Shpeisman (Google)
  • Armando Solar-Lezama (MIT)

Call for Papers

Submission deadline has been extended to Apr 10

Due to recent algorithmic and computational advances, machine learning has seen a surge of interest in both research and practice. From natural language processing to self-driving cars, machine learning is creating new possibilities that are changing the way we live and interact with computers. However, the impact of these advances on programming languages remains mostly untapped. Yet, incredible research opportunities exist when combining machine learning and programming languages in novel ways.

This symposium seeks to bring together programming language and machine learning communities to encourage collaboration and exploration in the areas of mutual benefit. The symposium will include a combination of rigorous peer-reviewed papers and invited events. The symposium will seek papers on a diverse range of topics related to programming languages and machine learning including (and not limited to):

  • Application of machine learning to compilation and run-time scheduling
  • Collaborative human / computer programming (i.e., conversational programming)
  • Deterministic and stochastic program synthesis
  • Infrastructure and techniques for mining and analyzing large code bases
  • Interoperability between machine learning frameworks and existing code bases
  • Probabilistic and differentiable programming
  • Programming language and compiler support for machine learning applications
  • Programming language support and implementation of machine learning frameworks
  • Neurosymbolic and intentional programming

Evaluation Criteria

As in previous years, reviewers will evaluate each contribution for its significance, originality, and clarity to the topics listed above. Submissions should clearly state how their novelty and how they improve upon existing work.

Evaluation will be double-blind and papers must be properly anonymized. This means that author names and affiliations must be omitted from the submission. Additionally, if the submission refers to prior work done by the authors, that reference should be made in third person. These are firm submission requirements. If you have questions about making your paper double blind, please contact the Program Chair.

Broader Impact

Due to the growing concerns regarding potential positive and negative impacts of any research work, this year, the authors of MAPS submissions are asked to include a section on the potential broader impact of their work. This section should highlight an evaluation of potential misuses and negative impacts of the presented technology on its users and those indirectly affected, such as their friends and families, communities, society, and the planet. Authors should ponder and discuss the negative outcomes of their research 1) using its current form, 2) if enhanced in the future with new capabilities. They should also discuss potential ways to mitigate those harms (policy, law, alternative design choice, etc.).

The broader impact section will be outside the page limit of the original paper. This section should be at least one paragraph but should not exceed 1 page. Although this section is a must-have, this year, the decision to accept the paper will not be influenced by the discussed negative impacts. However, it might influence the acceptance decision in future MAPS.

Some helpful tips to think about broader impact

Paper Submissions

Submissions must be in English. Papers should be in PDF and format and no more than 8 pages in standard two-column SIGPLAN conference format including figures and tables but excluding references and appendices. Submissions must be made through the online submission site https://maps2021.hotcrp.com/.

All accepted papers will appear in the published proceedings and available on the ACM Digital Library. Authors will have the option of having their final paper accessible from the workshop website as well.

Authors must be familiar with and abide by SIGPLAN’s republication policy, which forbids simultaneous submission to multiple venues and requires disclosing prior publication of closely related work.

General Chair: Roopsha Samanta (Purdue)

Program Chair: Isil Dillig (UT-Austin)

Publicity Chair: Aws Albarghouthi (University of Wisconsin-Madison)

Ethics Chair: Jesmin Tithi (Intel Labs)

Program Committee

  • Farhana Aleen (NVIDIA)
  • Dana Drachsler Cohen (Technion)
  • Vinod Grover (NVIDIA)
  • Niranjan Hasabnis (Intel Labs)
  • Rania Khalaf (IBM Research)
  • Kuldeep S. Meel (National University of Singapore)
  • Erik Meijer (Facebook)
  • Sasa Misailovic (University of Illinois at Urbana-Champaign)
  • Augustus Odena (Google)
  • Alex Polozov (Microsoft Research)
  • Alex Ratner (University of Washington)
  • Calvin Smith (University of Texas Austin)

Steering Committee

  • Raj Barik (Uber)
  • Alvin Cheung (UC-Berkeley)
  • Stefano Ermon (Stanford University)
  • Justin Gottschlich (chair, Intel Labs / Penn)
  • Costin Iancu (Lawrence Berkeley National Lab)
  • Mayur Naik (University of Pennsylvania)
  • Kunle Olukotun (Stanford University)
  • Tatiana Shpeisman (Google)
  • Armando Solar-Lezama (MIT)

  1. Generating Bug-Fixes Using Pretrained Transformers. Dawn Drain, Chen Wu, Alexey Svyatkovskiy, Neel Sundaresan

  2. Learning to Make Compiler Optimizations More Effective. Rahim Mammadli, Marija Selakovic, Felix Wolf, Michael Pradel

  3. Pure, Low-Level Tensor Program Rewriting via Access Patterns (Representation Pearl). Gus Henry Smith, Andrew Liu, Steven Lyubomirsky, Scott Davidson, Joseph McMahan, Michael Taylor, Luis Ceze, Zachary Tatlock

  4. ControlFlag: A Self-supervised Idiosyncratic PatternDetection System for Software Control Structures. Niranjan Hasabnis, Justin Gottschlich

  5. Predictive Data Locality Optimization for Higher-Order Tensor Computations. Tharindu R. Patabandi, Anand Venkat, Abhishek Kulkarni, Pushkar Ratnalikar, Mary Hall, Justin Gottschlich