(Note: this is a DRAFT, copied from PAPIs ’16)
The call for proposals has officially closed. However, there are slots available for lightning talks (5') on October 11, and we may have gaps in the program open up – feel free to submit your proposal and we'll let you know if an opportunity arises.
June 18: Technical and Business TalksJuly 10: Tutorials and Research Presentations- October 11-12: Main conference takes place in Boston
If you have not heard about the status of your submission after July 26, please contact us.
In this document:
PAPIs is the premier forum for the presentation of new machine learning APIs, techniques, architectures and tools to build predictive applications. It is a community conference that 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, intelligent applications.
PAPIs ’16 is the 3rd International Conference on Predictive Applications and APIs, featuring 3 tracks (Technical, Business, Research) and the 1st AI Startup Battle where the jury is an AI. The audience is a mix of developers, software engineers, all-round data scientists, machine learning specialists, researchers, decision makers, managers, strategists and innovators.
Previous editions took place in Sydney (PAPIs ’15), Barcelona (PAPIs ’14), Paris and Valencia (PAPIs Connect). Feel free to have a look at videos of previous presentations and proceedings of last year’s Research Track for examples of the types of presentations we’ve had.
Presenting at the conference is a great way to share your learnings, showcase leadership on behalf of your organisation, and engage with your peers. We’ve had 700+ attendees come from 25 different countries to our 4 previous events, and even more people watch our presentations on YouTube.
Please read these guidelines all the way through for the best chance of having your proposal selected. If you have questions or concerns about anything you see here, please don’t hesitate to email us at cfp@papis.io.
We’re looking forward to receiving your best proposals!
The conference program will feature 4 types of presentations:
- Technical and Business Talks (use cases, innovations, challenges, lessons learnt)
- Tutorials
- Research presentations
- Startup pitches (as part of the AI Startup Battle)
We welcome practical presentations on a wide range of experience levels — from beginner-friendly how-tos to cautionary tales to deep dives for experienced professionals. We’re looking for a diverse and creative line-up of speakers, preferably with experience in public speaking.
We’re interested in hearing about innovative machine learning use cases in predictive applications and APIs. This can include domain-specific aspects (human-machine interface, decisions from predictions, real-world impact, ethics, etc.) as well as technical aspects (software engineering, architecture, data, performance measurement/monitoring, tools, etc.). We’re also interested in hearing your story: how you did it, challenges, and lessons learnt — do’s and dont’s.
Anything involving both APIs and machine learning should be highly relevant to the conference. Regarding predictive and intelligent applications, topics of interest include (but are not limited to) predictive applications in the industry (finance, insurance, legal, energy, healthcare, transportation, etc.), in technology (e.g. security, IoT), for decision making, for the office, and predictive applications for good.
Talks are 20-minute long, plus 5 minutes for Q&A. It may seem like a short time, but it allows for dynamic and focused talks that keep the audience engaged.
Tutorials are 30-minute long presentations that focus on teaching valuable skills (e.g. “how to do this in your organization”). They shouldn’t be product-oriented or rely on commercial software (otherwise please email sponsoring@papis.io for product workshop opportunities). 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 applications
- 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.
The Research Track is dedicated to the presentation of new techniques, architectures, standards, frameworks and open source software to build predictive APIs, Machine Learning as a Service (MLaaS), and to facilitate usage 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.
Applications to the AI Startup Battle will be via a different platform (coming soon). You can pre-apply by contacting us with a short presentation of your startup, and you can sign up to our newsletter to make sure you get notified when applications open.
Thank you for making it this far :) We will be receiving lots of proposals covering the same topics. They will be evaluated by our Program Committee (see our Team) on the basis of their novelty and/or significance and/or clarity of presentation. Reading the following will help you craft a proposal that stands out.
Start with a topic of interest to our audience (see above). Attendees have different levels of experience — from totally new to very experienced. Your presentation should either directly help them, or inspire/inform them about something they don’t already know. The core value to our attendees of what you’re presenting should be clearly stated in your proposal. What will they be able to do after they see your presentation that they can’t do now?
We believe that great presentations should be practical and focused. For instance, instead of presenting something general or showing a portfolio of things you / your company have done, it’s better to show one specific, unique thing in enough detail.
Try to cover some of the following in your proposal:
- what makes your work novel
- problems/challenges you’ve overcome or are currently facing
- how you architected what you built and why
- which results you got
- how people are using what you’ve built and how it’s creating value for them
- what you plan to do as future work.
Screenshots and live demos are appreciated to make your presentation more concrete and engaging.
It depends… Our objective is to ensure that PAPIs does not turn into a platform for pitching products/services/companies. Your presentation should provide practical information and be centered on real-world applications. The audience will be interested in hearing about practical integration for the use case being discussed, and unique features of the tool/product you’re featuring.
Some tips:
- It is best to submit your proposal early to get our feedback and adjust before the deadline if necessary.
- If you don’t represent the company/team behind the product, it would be great to hear why you chose the tool/product you are featuring rather than a competitor, and what your critical opinion is.
- We want to increase representation of open source tools at the conference and we will dedicate some slots for them. In particular we’d like to hear about projects that make it easier to process data, to create and experiment with predictive models, or to operationalize them with APIs.
- If your tool/product is commercial (and even if there is a free tier in your commercial offer):
- You should make sure that your presentation is centered on a real-world use case and not on the product itself.
- We reserve a limited number of slots for sponsored talks (subject to approval) where we are more flexible and allow presentations to be centered on products. That being said, it’s best to avoid sales pitches as our audience tends to dislike them. We invite you to write to sponsoring@papis.io if you want to book a slot and to tell us more about what you would like to present by submitting a proposal here.
Our proposal form has two fields that attendees will see on the program (title and abstract), and two fields that only reviewers will see (details and pitch). Please also use tags for reviewers to identify the type of proposal you’re submitting. Note that you can submit several proposals through our CfP application.
Remarks for Research papers and Industrial experience reports:
Research papers or Industrial experience reports. Short papers of 4 pages or long Ines of up to 8 pages.
- In addition to submitting the proposal form, please also send one of the following (anonymized) documents via this upload form, in PDF format and using the JMLR style:
- an extended abstract of 2 pages or more (references and comparisons to related work should be included, but details of implementation can be omitted)
- a short paper of 4 pages
- a long paper / report of up to 8 pages
- Please make sure the PDF file has the same name as your proposal.
- We will aim to publish final papers in JMLR proceedings after the conference (as we did in 2015).
These are what attendees will see in the program. Title and abstract should be compelling and to-the-point. Tell a story. Why should attendees come to your presentation and what will they get out of it?
Reviewers will see your title and abstract, and also your details and pitch. Details is a good place to go into more depth about what you’ll cover. It’s a good place to put an outline, reveal the secret sauce, and explain any twists you’ll include in your presentation that may not be evident in the title or abstract. Please also let us know if you intend to include a live demo or live coding (and intended duration).
Pitch is a good place to tell reviewers why PAPIs needs this presentation, and why you’re the right person to give it at PAPIs. How will your presentation help the program, or fill a specific need? Why are you excited about this topic?
Please take care here to refrain from identifying who you are since our first round of review is blind, and we appreciate your efforts to respect that as much as possible.
Please assign (multiple) tags to help us identify the type of your presentation (Technical, Business, Tutorial, Research, Startup Battle) and the level (if applicable: Beginner, Intermediate, Advanced).
This won’t be seen by reviewers in the 1st round but we will use it when advertising your presentation if it is accepted. Please include your current position and organization.
Program Chairs:
- Florian Douetteau (CEO at Dataiku)
- Keiran Thompson (Founder & Chief Scientist at Datagami)
Local Chairs:
- Andy Thurai (Program Director at IBM)
- Slater Victoroff (CEO at Indico)
Reviewers:
- Danny Lange, Ph.D. (Head of Machine Learning at Uber)
- Francisco J Martin, Ph.D. (CEO & Co-Founder at BigML)
- Alex Housley (Founder & CEO at Seldon)
- Kiri Wagstaff, Ph.D. (Principal Researcher at NASA Jet Propulsion Laboratory)
- Ikaro Silva, Ph.D. (Research Affiliate at MIT / MC10)
- Jean-Baptiste Tristan, Ph.D. (ML group at Oracle / Lecturer at Harvard)
- Nuria Oliver, Ph.D. (Scientific Director at Telefónica R&D)
- Gabriel Synnaeve, Ph.D. (Researcher at Facebook)
- Misha Bilenko, Ph.D. (Principal Researcher at Microsoft)
- Nicolas Hohn, Ph.D. (Head of Data Science at Dun & Bradstreet Australia and New Zealand)
- Ramon Lopez de Mantaras, Ph.D. (Director AI Research Institute at Spanish Research Council)
- Sabrina Kirstein (Data Science Consultant at Comma Soft AG)
- David Talby, Ph.D. (CTO at Atigeo)
- Sudarshan Raghunathan, Ph.D. (Principal SDE at Microsoft)
- Sharat Chikkerur, Ph.D. (Principal Data Scientist at Nanigans)
- Ines Almeida (Data Science Principal Investigator at Crowd Process)
- Elena Álvarez Mellado (Intrepid linguist at Molino de Ideas)
Submissions Chairs:
- Joan Capdevila Pujol (Ph.D. candidate at Universitat Politecnica de Catalunya)
- Leonardo Noleto (Data Scientist at OVH)
- Vincent Van Steenbergen (CEO at w00t data)
Acknowledgement: These guidelines were partly inspired from railsconf. This website is based on rubycentral’s cfp-app. Many thanks to Joan Capdevila Pujol for his help in setting it up!