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2nd Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML)

April 21, 2020 | Sydney, Australia |

In conjunction with CPS-IoT Week 2020

The second Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML) focuses on work that combines sensor signals from the physical world with machine learning, particularly in ways that are distributed to the device or use edge and fog computing. The development and deployment of ML at the very edge remains a technological challenge constrained by computing, memory, energy, network bandwidth and data privacy and security limitations. This is especially true for battery operated devices and always-on use cases and applications. This workshop will provide a forum for sensing, networking and machine learning researchers to present and share their latest research on building machine learning enabled sensor systems. Sensys-ML focuses on providing extensive feedback on Work In Progress papers involving machine learning (TinyML/ UltraML) on sensor systems.

Topics of interest include, but are not limited to, the following:

  • Advancement in Hardware for enabling TinyML capabilities at the edge
  • System Architecture for supporting TinyML and UltraML
  • Parallel and Distributed Machine Learning for Sensor and Network systems
  • Machine Learning driven Data Analytics
  • System and Algorithm co-design for practical TinyML at Sensor Systems
  • Security and Privacy at the Edge
  • Video Analytics at the Edge
  • Validation and debugging of TinyML and UltraML
  • Emerging Sensing Applications using TinyML

Important Dates

  • Paper Abstract Submission: 7th January 2020 AoE (8th January, 2020 7:59:59am EDT on submission website)
  • Paper Full Submission: 14th January 2020 AoE (15th January, 2020 7:59:59am EDT on submission website)
  • Notification of Paper Acceptance: 1st February 2020
  • Camera-Ready: 14th February, 2020
  • Workshop Date: 21st April 2020
  • Submission Guidelines

Submitted papers must be unpublished and must not be currently under review for any other publication. There are two submission tracks:

  • Full papers, which will be eligible for publication at most 6 single-spaced 8.5" x 11" pages with 9-pt font size in two-column format, including figures and tables and references. All submissions must use the LaTeX (preferred) or Word styles found here. LaTeX submissions should use the acmart.cls template (sigconf option), with the 9-pt font, make sure to use \documentclass[9pt, sigconf]{acmart} in your document. This format will be used also for the camera-ready version of accepted papers. Papers will go through double-blind peer reviewing by the PC. Papers that do not meet the size, formatting, and anonymization requirements will not be reviewed. We require each paper to be in Adobe Portable Document Format (PDF) and submitted through the Sensys-ML HoTCRP submission site. Accepted papers will be published in the ACM Digital Library. At least one of the authors of every accepted paper must register and present the paper at the workshop. The program committee will elect one paper for the Best Paper Award.
  • Work In Progress, which is a presentation-only format. We request a 1 single spaced 8.5" x 11" page abstract submission. Please note that these submissions will not be published as this is a presentation-only format.

Workshop Committee

Workshop and TPC Chairs

  • Poonam Yadav (University of York, UK and University of Cambridge, UK)
  • Valerie Liptak (Amazon, USA)
  • Manik Gupta (BITS Pilani, Hyderabad, India)

Steering Committee

  • Jon Crowcroft (University of Cambridge, UK)
  • Cecilia Mascolo (University of Cambridge, UK)

Technical Program Committee

  • Octav Chipara (University of Iowa, USA)
  • Syed Shabih Hasan (Delos, USA)
  • Jon Crowcroft (University of Cambridge, UK)
  • Manu Rastogi (HP Labs, USA)
  • Cecilia Mascolo (University of Cambridge, UK)
  • Prateek Jain (Microsoft Research, India)
  • Nic Lane (University of Oxford, UK)
  • Matthew Mattina (ARM, USA)
  • Carles Gomez Montenegro (UPC, Spain)
  • Diana Andreea Popescu (University of Cambridge, UK)
  • Akshay Nambi (Microsoft Research, India)
  • Stylianos I. Venieris (Samsung AI, UK)
  • Shiqiang Wang (IBM Research, NY, USA)
  • Kamin Whitehouse (Amazon/University of Virginia, USA)