Call For Demonstration Proposals
AIMLSystems invites submissions for software demonstration proposals. The emphasis of the demonstration program is on next-generation systems in the intersection of AI/ML and systems engineering. Proposals on prototypes that demonstrate new ideas in the following topics are highly encouraged.
- Systems for AI/ML (e.g., improvements in CPU/GPU architectures, data-intensive infrastructure, communications etc.)
- AI/ML for Systems (e.g., self-tuning databases, learning compiler optimizers, learnable network systems etc.)
- Applications of AI/ML in Socio-Economic Systems Design (e.g., public healthcare and security).
The evaluation criteria include the AIMLSystems audience experience and novelty of the system. In particular, the proposal should describe what the audience will see, how they can interact with the system, and user journeys that will be demonstrated.
Demonstration proposals must not exceed 2 pages, including any appendix and bibliography. Only electronic submissions in PDF format will be considered. Page limits and formatting instructions for all remaining tracks will be announced on the conference website. Proposal reviews will follow a single-blind policy.
The manuscripts can be submitted in PDF, formatted using ACM sigconf style through https://cmt3.research.microsoft.com/AIMLSystems2021.
Submission deadline: July 13, 2021 Submission deadline: July 20, 2021
- Decision notification: September 12, 2021
- Camera ready deadline: September 19, 2021
- Conference Dates: October 21-23, 2021
Dual Submission Policy
A proposal submitted to AIMLSystems can not be under review at any other conference or journal during the entire time it is considered for review at AIMLSystems, and it must be substantially different from any previously published work or any work under review. After submission and during the review period, submissions to AIMLSystems must not be submitted to other conferences / journals for consideration. However, authors may publish at non-archival venues, such as workshops without proceedings, or as technical reports (including arXiv).
- Chitra Babu, SSN College of Engineering,
- Hwanjun Song, NAVER AI Lab
- Ki Hyun Tae, KAIST
- Lavanya Sita Tekumalla, AiFonic Labs
- Marios Fragkoulis, TU Delft
- Neelima Vobugari, Tarah AI
- Paris Carbone, KTH Royal Institute of Technology