[TYPES/announce] ProbProg 2021: CALL FOR PARTICIPATION

[Jean-Baptiste Tristan]

The 3rd International Conference on Probabilistic Programming (ProbProg21)
will be held from October 20 to October 22, 2021. The conference will be
held virtually.



Website <https://urldefense.com/v3/__https://probprog.cc/__;!!IBzWLUs!HfVrZU5-_9dobRo2PXKGLJJZzUqosClKY2Dwb_4Yx6NQXZbm6rWu92pWSMRWzIOVwRQDujgS_COXiA$ >

Registration
<https://urldefense.com/v3/__https://www.eventbrite.com/e/probprog-2021-tickets-166674416667?aff=ebdssbeac__;!!IBzWLUs!HfVrZU5-_9dobRo2PXKGLJJZzUqosClKY2Dwb_4Yx6NQXZbm6rWu92pWSMRWzIOVwRQDujgUELF0Sw$ >

Schedule <https://urldefense.com/v3/__https://probprog.cc/schedule/__;!!IBzWLUs!HfVrZU5-_9dobRo2PXKGLJJZzUqosClKY2Dwb_4Yx6NQXZbm6rWu92pWSMRWzIOVwRQDujhUziqccQ$ >

Accepted submissions <https://urldefense.com/v3/__https://probprog.cc/posters/__;!!IBzWLUs!HfVrZU5-_9dobRo2PXKGLJJZzUqosClKY2Dwb_4Yx6NQXZbm6rWu92pWSMRWzIOVwRQDujhUZZv_HQ$ >



KEYNOTES



Katie Bouman, California Institute of Technology

John winn, Microsoft Research

Bob Carpenter, Flatiron Institute



ORGANIZERS

Program Chairs: Guy van den Broeck & Lawrence Murray

General Chairs: Jan-Willem van de Meent & Jean-Baptiste Tristan

SPONSORS



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CONFERENCE SCOPE

Probabilistic programming is an emergent field based on the idea that
probabilistic models can be efficiently represented as executable code.
This idea has enabled researchers to formalize, automate, and scale up many
aspects of modeling and inference; to make modeling and inference
accessible to a broader audience of developers and domain experts; and to
develop new programmable AI systems that integrate modeling and inference
approaches from multiple domains.

PROBPROG is the first international conference dedicated to probabilistic
programming. PROBPROG includes presentations on basic research, applied
research, open source, and the practice of probabilistic programming.
PROBPROG attendees come from academia, industry, non-profits, and
government. The conference aims to achieve three goals:

   1.

   Create a venue where researchers from multiple fields — e.g. programming
   languages, statistics, machine learning, and artificial intelligence — can
   meet, interact, and exchange ideas.
   2.

   Grow a diverse and inclusive probabilistic programming community, by
   actively seeking participation from under-represented groups, and providing
   networking opportunities, mentorship, and feedback to all members.
   3.

   Support the development of the practice of probabilistic programming,
   including open-source systems and real-world applications, and provide a
   bridge between the practice of probabilistic programming and basic research.

PROBPROG welcomes abstract submissions for contributed research
presentations, demonstrations, open-source systems, participants in open
discussions, and consideration for invited publication in an online
journal. Submissions should indicate alignment with one or more of the
following themes:

   1.

   Artificial and Natural Intelligence. Probabilistic programs and
   probabilistic programming technology for formulating and solving the core
   problems of intelligence, including research relevant for engineering
   artificial intelligence and for reverse-engineering natural intelligence. A
   central theme in this track is new AI architectures based on probabilistic
   programming that integrate statistical, symbolic, neural, Bayesian, and
   simulation-based approaches to knowledge representation and learning.
   Another central theme is proposals for learning probabilistic programs from
   data, and modeling high-level forms of human learning using probabilistic
   program synthesis. This track also includes research at the intersection of
   probabilistic programming and intelligence augmentation, collective
   intelligence, machine learning, and the development and analysis of
   intelligent infrastructure.
   2.

   Statistics and Data Analysis. Probabilistic programs and probabilistic
   programming technology for formulating and solving problems in statistics
   and data analysis. Topics include latent variable models, parameter
   estimation, automated data modeling, Bayesian inference, calibration, model
   checking, model criticism, visualization, and testing of statistical models
   and inference algorithms. This track also includes statistical applications
   and deployments of probabilistic programming for data analysis.
   3.

   Languages, Tools, and Systems. The design, implementation, and formal
   semantics of probabilistic programming languages and systems, including
   domain-specific and general-purpose languages, interpreters, compilers,
   probabilistic meta-programming techniques, probabilistic meta-programming
   languages, and runtime systems. This track also includes research on
   dynamic and static analysis of probabilistic programs, and empirical and
   theoretical study of the usability, performance, and accuracy of
   probabilistic programming languages and systems.
   4.

   The Practice of Probabilistic Programming. This track is centered on
   four themes: (i) probabilistic programs and systems based on probabilistic
   programming that solve problems in industry, government, philanthropic
   work, applied research, and teaching, as well as potential use cases for
   probabilistic programs or probabilistic programming technology in these
   areas; (ii) challenges that arise when using probabilistic programming in
   practice, including inspection, debugging, testing, and performance
   engineering; (iii) human-centric design of probabilistic programs and
   probabilistic programming technology; and (iv) probabilistic programming
   tools, probabilistic program analyses, probabilistic programming
   styles/workflows, probabilistic programming practices/guidelines/experience
   reports, and probabilistic programming environments with the potential to
   address issues faced by practitioners.


-- 
Jean-Baptiste Tristan
Associate Professor
Computer Science Department
Boston College
Website <https://urldefense.com/v3/__https://jtristan.github.io/__;!!IBzWLUs!HfVrZU5-_9dobRo2PXKGLJJZzUqosClKY2Dwb_4Yx6NQXZbm6rWu92pWSMRWzIOVwRQDujioC32foA$ >
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