Call for presentations at the ECIR 2018 Industry Day
29 March 2018
From Research To Production
Do you have experience in translating IR/ML research into a product, a tool or a framework that is used by customers, developers or researchers? Have you taken a research prototype or an algorithm published in research and implemented it in a production ready environment or in an open-source framework or tool? Have you contributed to IR/ML libraries or built IR/ML products that use those libraries? If the answer to any of these questions is yes, then we want to hear from you!
What were the main challenges you encountered applying research in a commercial setting? What process did you follow to select which algorithms or methods best suit your domain and your efforts to best productionise them? What factors became more important, and which ones less critical compared to the research world? What were the biggest stumbling blocks? Have you encountered issues with reproducibility? What are the approaches that work well in industry? What better support should the research world offer?
This coming Spring, the ECIR 2018 Industry Day will focus on the application of IR techniques in production environments and the development of production ready IR/ML systems, whether they make use of open-source or in-house libraries or involve the development of those libraries. Our goal is to connect researchers and practitioners, tool and product developers to promote closer collaborations and synergies to close the gap between research and production systems.
We would like to have representatives from the whole research to product pipeline. Say, you invented a new topic model and published it, or you contributed a production ready implementation of that topic model into an open-source tool, like gensim, or you work at a startup company and used that new technology in your product stack. Come and exchange your experiences and learn from others’ experiences!
Here are some of the questions you may want to share your thoughts on:
- How do the priorities regarding IR or ML technique use and implementation differ in academia vs industry?
- Is there any specific angle of IR that is more critical in real world settings than the importance the research community gives it?
- What were the stumbling blocks you encountered when building production quality IR or ML systems and how did you identify and step over or around them?
- Are there any ‘untold’ truths about the application of IR technologies that the research world should know?
- How could we ensure a closer collaboration between research and industry?
By sharing your experiences you will be generating enthusiasm among the bright graduate students, postdocs or like-minded practitioners in the audience to pursue the application of their ideas to real world tasks.
In addition to the talks, we are planning an industry panel of experts who will share their experiences in implementing research into open-source IR/ML frameworks like Terrier, TensorFlow, Infer.NET, CNTK, Gensim or similar. The goal of this panel is to understand the process of taking a research idea (i.e., starting from the decision of what research to incorporate) to the moment when it is released to the public.
Submit your talk proposal (maximum length of 1 page) or your interest to serve as a participant on the panel directly via email to Gabriella Kazai (email@example.com) and Miguel Martinez (firstname.lastname@example.org). Submissions will be considered by a panel, judged on relevance to the theme and value to the audience, until the programme is filled, so submit soon!