The First ACM SIGPLAN Workshop on Machine Learning and Programming Languages (MAPL)

(co-located with PLDI 2017)

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. MAPL seeks to bring together programming language and machine learning communities to encourage collaboration and exploration in cross disciplinary research. The workshop will include a combination of peer-reviewed papers and invited events, such as invited talks, panels and/or town hall discussions.

MAPL seeks papers on a diverse range of topics related to programming languages and machine learning including:

- Programming languages and compilers for machine learning

- Deep learning frameworks

- Machine learning for compilation and run-time scheduling

- Improving programmer productivity via machine learning

- Inductive programming

- Formal verification of machine learning systems

- Probabilistic programming

- Collaborative human / computer programming

- Interoperability of machine learning frameworks and existing code bases


MAPL paper submissions should be made through EasyChair.

Papers must be submitted in PDF and be no more than 8 pages in standard two-column SIGPLAN conference format including figures and tables but not including references. Shorter submissions are welcome. The submissions will be judged based on the merit of the ideas rather than the length. Submissions must be made through the on-line submission site. Formal proceedings will be included in the ACM digital archive and available at the workshop. The camera-ready page count is no more than 10 pages but this includes references. All accepted papers must be presented by a co-author of the paper at the workshop. Accepted papers which are not presented at the workshop will be summarily dismissed from the program and proceedings.

Registration and Workshop Information

Registration information coming soon.

Important Dates:

  • Submission Deadline: 11:59pm, April 3, 2017 (Anywhere on Earth Time)
  • Author Notification: April 24, 2017
  • Camera-ready Deadline: May 10, 2017
    • The camera-ready page count is no more than 10 pages but this includes references.
  • Workshop: June 18, 2017

MAPL 2017 Program (Sunday, June 18):

Room: Vertex WS219

9:15-9:30: Introduction and Welcome (Tatiana Shpeisman and Justin Gottschlich, MAPL General and Program Chairs)

9:30-10:30: Keynote: "Programming by Examples: PL Meets ML" by Sumit Gulwani

10:30-11:00: Break

Languages and Frameworks (Session Chair: Peter Hawkins, Google)

11:00-11:30: "A Computational Model for TensorFlow (An Introduction)" by Martin Abadi, Michael Isard, and Derek Murray (Google).

11:30-12:00: "Dyna: Toward a Self-Optimizing Declarative Language for Machine Learning Applications" by Tim Vieira (Johns Hopkins), Matthew Francis-Landau (Johns Hopkins), Nathaniel Wesley Filardo (Johns Hopkins), Farzad Khorasani (Rice), and Jason Eisner (Johns Hopkins).

Debugging, Analysis, and Verification (Session Chair: Tatiana Shpeisman, Intel Labs)

12:00-12:30: "Debugging Probabilistic Programs" by Chandrakana Nandi (U. Washington), Dan Grossman (U. Washington), Adrian Sampson (Cornell University), Todd Mytkowicz (Microsoft Research), and Kathryn S. McKinley (Google).

12:30-2:00: Lunch

Debugging, Analysis, and Verification (continued, Session Chair: Tatiana Shpeisman, Intel Labs)

2:00-2:30: "Combining the Logical and the Probabilistic in Program Analysis" by Xin Zhang, Xujie Si, and Mayur Naik (University of Pennsylvania).

2:30-3:00: "Learning a Classifier for False Positive Error Reports Emitted by Static Code Analysis Tools" by Ugur Koc, Parsa Saadatpanah, Jeffrey S. Foster, and Adam A. Porter (University of Maryland, College Park).

3:00-3:30: "Verified Perceptron Convergence Theorem" by Charlie Murphy (Princeton), Patrick Gray (Ohio University), and Gordon Stewart (Ohio University).

3:30-4:00: Break

Town Hall Discussion

4:00-4:45: Town Hall Discussion (Moderator: Justin Gottschlich, Intel Labs)

4:45-5:00: Concluding Remarks (Tatiana Shpeisman and Justin Gottschlich, , MAPL General and Program Chairs)

General Chair: Tatiana Shpeisman (

Program Chair: Justin Gottschlich (

Program Committee:

Raj Barik (Intel Labs)

Stefano Ermon (Stanford University)

Justin Gottschlich (Program Chair, Intel Labs)

Mary Hall (University of Utah)

Peter Hawkins (Google)

Costin Iancu (Lawrence Berkeley National Lab)

Guillaume Melquiond (Inria, LRI)

Michael O’Boyle (University of Edinburgh)

Kunle Olukotun (Stanford University)

Tatiana Shpeisman (Intel Labs)

Organizing Committee:

Raj Barik (Intel Labs)

Stefano Ermon (Stanford University)

Justin Gottschlich (Intel Labs)

Costin Iancu (Lawrence Berkeley National Lab)

Kunle Olukotun (Stanford University)

Tatiana Shpeisman (Intel Labs)

For questions, please contact the general or program chair (email addresses listed above).