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Xuemin Jin

Xuemin Jin

Verified

Northeastern University · Engineering Management and Systems Engineering

Active 1994–2024

h-index25
Citations2.2k
Papers9126 last 5y
Funding$1.0M1 active
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Research topics

  • Computer Science
  • Artificial Intelligence
  • Data science
  • Engineering
  • Management science
  • Manufacturing engineering
  • Business
  • Engineering management
  • Knowledge management
  • Marketing

Selected publications

  • Data science skills and domain knowledge requirements in the manufacturing industry: A gap analysis

    Journal of Manufacturing Systems · 2021 · 87 citations

    Senior authorCorresponding
    • Computer Science
    • Artificial Intelligence
    • Manufacturing engineering

    Manufacturing has adopted technologies such as automation, robotics, industrial Internet of Things (IoT), and big data analytics to improve productivity, efficiency, and capabilities in the production environment. Modern manufacturing workers not only need to be adept at the traditional manufacturing technologies but also ought to be trained in the advanced data-rich computer-automated technologies. This study analyzes the data science and analytics (DSA) skills gap in today's manufacturing workforce to identify the critical technical skills and domain knowledge required for data science and intelligent manufacturing-related jobs that are highly in-demand in today's manufacturing industry. The gap analysis conducted in this paper on Emsi job posting and profile data provides insights into the trends in manufacturing jobs that leverage data science, automation, cyber, and sensor technologies. These insights will be helpful for educators and industry to train the next generation manufacturing workforce. The main contribution of this paper includes (1) presenting the overall trend in manufacturing job postings in the U.S., (2) summarizing the critical skills and domain knowledge in demand in the manufacturing sector, (3) summarizing skills and domain knowledge reported by manufacturing job seekers, (4) identifying the gaps between demand and supply of skills and domain knowledge, and (5) recognize opportunities for training and upskilling workforce to address the widening skills and knowledge gap.

  • A cross-disciplinary comparison of multimodal data fusion approaches and applications: Accelerating learning through trans-disciplinary information sharing

    Expert Systems with Applications · 2020 · 56 citations

    • Computer Science
    • Artificial Intelligence
    • Computer Science

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