INVITED: Building robust machine learning systems: Current progress, research challenges, and opportunities

Jeff Jun Zhang, Kang Liu, Faiq Khalid, Muhammad Abdullah Hanif, Semeen Rehman, Theocharis Theocharides, Alessandro Artussi, Muhammad Shafique, Siddharth Garg

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Machine learning, in particular deep learning, is being used in almost all the aspects of life to facilitate humans, specifically in mobile and Internet of Things (IoT)-based applications. Due to its state-of-the-art performance, deep learning is also being employed in safety-critical applications, for instance, autonomous vehicles. Reliability and security are two of the key required characteristics for these applications because of the impact they can have on human's life. Towards this, in this paper, we highlight the current progress, challenges and research opportunities in the domain of robust systems for machine learning-based applications.

Original languageEnglish (US)
Title of host publicationProceedings of the 56th Annual Design Automation Conference 2019, DAC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781450367257
StatePublished - Jun 2 2019
Event56th Annual Design Automation Conference, DAC 2019 - Las Vegas, United States
Duration: Jun 2 2019Jun 6 2019

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X


Conference56th Annual Design Automation Conference, DAC 2019
Country/TerritoryUnited States
CityLas Vegas


  • Adversarial Attacks
  • Deep Learning
  • Machine Learning
  • Permanent Faults
  • Reliability
  • Robustness
  • Security
  • Timing Errors

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation


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