The 1st Workshop on Machine Learning and Systems (EuroMLSys)

co-located with EuroSys '21

April 26th 2021, Virtually in Edinburgh, Scotland, UK,

The recent wave of research focusing on machine intelligence (machine learning and artificial intelligence) and its applications has been fuelled by both hardware improvements and deep learning frameworks that simplify the design and training of neural models. Advances in AI also accelerate research towards Reinforcement Learning (RL), where dynamic control mechanisms are designed to tackle complex tasks. Further, machine learning based optimisation, such as Bayesian Optimisation, is gaining traction in the computer systems community where optimisation needs to scale with complex and large parameter spaces; areas of interest range from hyperparameter tuning to system configuration tuning,

The EuroMLSys workshop will provide a platform for discussing emerging trends in building frameworks, programming models, optimisation algorithms, and software engineering tools to support AI/ML applications. At the same time, using ML for building such frameworks or optimisation tools will be discussed. EuroMLSys aims to bridge the gap between AI research and practice, through a technical program of fresh ideas on software infrastructure, tools, design principles, and theory/algorithms (including issues of instability, data efficiency, etc.), from a systems perspective. We will also explore potential applications that will take advantage of ML.

Key dates

  • Paper submission deadline (hard): February 15, 2021 February 19, 2021 (23:59 AoE, UTC-12)
  • Acceptance notification: March 19, 2021
  • Final paper due: April 1, 2021
  • Registration: April 20, 2021
  • Workshop: April 26, 2021 (full-day workshop)

Call for Papers

The EuroMLSys workshop focuses on research topics at the intersection of Machine Learning and Computer Systems, and it is the first workshop to be co-located with EuroSys that addresses this emerging topic.

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

  • Scheduling algorithms for data processing clusters
  • Custom hardware for machine learning
  • Programming languages for machine learning
  • Benchmarking systems (for machine learning algorithms)
  • Synthetic input data generation for training
  • Systems for training and serving machine learning models at scale
  • Graph neural networks
  • Neural network compression and pruning in systems
  • Systems for incremental learning algorithms
  • Large scale distributed learning algorithms in practice
  • Database systems for large scale learning
  • Model understanding tools (debugging, visualisation, etc.)
  • Systems for model-free and model-based Reinforcement Learning
  • Optimisation in end-to-end deep learning
  • System optimisation using Bayesian Optimisation
  • Acceleration of model building (e.g., imitation learning in RL)
  • Use of probabilistic models in ML/AI application
  • Learning models for inferring network attacks, device/service fingerprinting, congestion, etc.
  • Techniques to collect and analyze network data in a privacy-preserving manner
  • Learning models to capture network events and control actions
  • Machine learning in networking (e.g., use of Deep RL in networking)
  • Analysis of distributed ML algorithms
  • Semantics for distributed ML languages
  • Probabilistic modelling for distributed ML algorithms
  • Synchronisation and state control of distributed ML algorithms

Accepted papers will be published in the ACM Digital Library (you can opt out from this).


Submissions will be up to 6 pages long, including figures, and tables, with 10-point font, in a two-column format. Bibliographic references are not included in the 6-page limit. Submitted papers must use the official SIGPLAN Latex / MS Word templates

Submissions will be single-blind.

Submit your paper at:




Workshop and TPC Chairs

  • Eiko Yoneki, University of Cambridge
  • Paul Patras, University of Edinburgh

Technical Program Committee

  • Sam Ainsworth, University of Edinburgh
  • Sami Alabed, University of Cambridge
  • Laurent Bindschaedler, MIT
  • Jose Cano, University of Glasgow
  • Jon Crowcroft, University of Cambridge
  • Daniel Goodman, Oracle
  • Hamed Haddadi, Imperial College London
  • Zhihao Jia, CMU
  • Alexandros Koliousis, NCH
  • Dawei Li, Amazon
  • Luisi Nardi, Stanford University/Lund University
  • Amir Payberah, KTH
  • Peter Pietzuch, Imperial College London
  • Valentin Radu, University of Sheffield
  • Amitabha Roy, Google
  • Adam Ścibior, UBC
  • Ryota Tomioka, MSR Cambridge
  • Peter Triantafillou, University of Warwick
  • Aaron Zhao, University of Cambridge

Web Chair

  • Alexis Duque, University of Edinburgh


For any question(s) related to EuroMLSys 2021, please contact us:

Follow us on Twitter: @euromlsys