Overview:
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PAPIs ’16 (http://www.papis.io/2016) is the 3rd International Conference on Predictive APIs and Apps, featuring 3 tracks (Technical, Business, Research) and the 1st AI Startup Battle where the jury is an AI system. PAPIs is the premier forum for the presentation of new machine learning APIs, techniques, architectures and tools to build predictive applications. It brings together practitioners from industry, government and academia to present new developments, identify new needs and trends, and discuss the challenges of building real-world predictive applications.

Important dates:
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Submission Deadline: June 10, 2016
Notification of paper acceptance: July 11, 2016
When: Oct 10, 2016 - Oct 12, 2016
Location: Boston

Call for Proposals
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Link: http://cfp.papis.io/

The conference program will feature 4 types of presentations:
- Research presentations (as part of the Research Track)
- Talks (as part of the Technical and Business Tracks): use cases, innovations, stories and lessons learnt
- Tutorials (as part of the Technical Track)
- Startup pitches (as part of the AI Startup Battle)

Research presentations
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The Research Track targets people who develop new predictive APIs (open source and commercial), Machine-Learning-as-a-Service products, systems and frameworks that facilitate the integration of ML in real-world applications.

Topics include (but are not limited to):
- Software engineering: design patterns and best practices
- Distributed systems: scaling out services and APIs
- Automation in Machine Learning and Data Science (e.g. model selection and data wrangling)
- Interoperability between services/APIs/tools
- Privacy and security.

Paper submission and Publication:
- In addition to submitting the proposal form (http://cfp.papis.io/events/2016), please send a research paper (or extended abstract) in pdf format by email to cfp@papis.io, and please include the name of your proposal (as given in the proposal form) in the email’s subject.
- Papers should be up to 8 pages long, using the JMLR style (http://www.jmlr.org/format/format.html).
- We will aim to have accepted papers published in JMLR proceedings (http://www.jmlr.org/proceedings/) after the conference (as we did in 2015 (http://jmlr.org/proceedings/papers/v50/)).

Talks
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We’re interested in hearing about the predictive apps and APIs you’ve built, showing domain-specific aspects (such as user interfaces, real-world impact, etc.) and technical aspects (such as architecture, data, performance measurement/monitoring, etc.). We’re also interested in hearing your story: how you did it and lessons learnt — do’s and dont’s.
Anything involving both APIs and machine learning should be highly relevant to the conference. Regarding predictive apps, topics of interest include (but are not limited to) predictive apps for good, for the office, for IoT, in the industry (transportation, finance, insurance, legal, energy, healthcare, etc.), and for decision making.

Tutorials
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Tutorials are longer presentations that focus on teaching valuable skills (e.g. “how to do this in your organization”). Here are a few ideas of topics to inspire you — this list is by no means exhaustive and these are just ideas:
- feature engineering, data cleansing and data transformation for predictive apps
- how to measure performance for model selection, live monitoring, and post-deployment validation
- how to turn your data processing back-end into REST APIs
- how to use churn detection in your organization.