Associate Professor
Telephone: (01792) 602323
Room: Academic Office - C_216
Second Floor
Engineering Central
Bay Campus

Dr Cinzia Giannetti is a Senior Lecturer at College of Engineering Swansea University. Her expertise includes smart manufacturing and sensors technologies, with emphasis on knowledge and information management, data analytics and machine learning techniques to support decision making, through the use of sensors and large-scale databases.

She has significant experience in delivering and supporting applied industrial R&D projects gained both in industry and academia. Before moving to academia, she has worked for a decade in industry in senior technical roles delivering, planning and coordinating software development projects for existing systems and new products. Her research interests include the development of autonomous, collaborative and intelligent production systems by using knowledge-intensive advanced ICT technologies and sensors. 

Areas of Expertise

  • Manufacturing Systems Engineering
  • Big Data
  • Manufacturing Informatics and IoTs


  1. & Machine Learning as a universal tool for quantitative investigations of phase transitions. Nuclear Physics B 944, 114639
  2. & Segmentation and generalisation for writing skills transfer from humans to robots. Cognitive Computation and Systems 1(1), 20-25.
  3. & Optimisation process for robotic assembly of electronic components. The International Journal of Advanced Manufacturing Technology
  4. & A robust design of an innovative shaped rebar system using a novel uncertainty model. Structural and Multidisciplinary Optimization 58(4), 1351-1365.
  5. & Risk based uncertainty quantification to improve robustness of manufacturing operations. Computers & Industrial Engineering 101, 70-80.

See more...


  • EG-219 Statistical Methods in Engineering

    This module offers a balanced, streamlined one-semester introduction to Engineering Statistics that emphasizes the statistical tools most needed by practicing engineers. Using real engineering problems students see how statistics fits within the methods of engineering problem solving and learn how to apply statistical methodologies to their field of study. The module teaches students how to think like an engineer when analysing real data. Mini projects, tailored to each engineering discipline, are intended to simulate problems that students will encounter professionally during their future careers. Emphasis is placed on the use of statistical software for tackling engineering problems that require the use of statistics.

  • EGSM24 Smart Manufacturing

    Industrial production is entering a new and exciting era, called Smart Manufacturing that will bring a complete transformation in the way products are designed, manufactured and serviced through pervasive and ubiquitous use of ICT, sensors and intelligent robots. To fully exploit the potential of these new technologies there is the need to develop awareness of what Smart Manufacturing is and how organisations can embrace the necessary changes to drive efficiency and boost productivity. This module covers fundamental concepts, technologies and business strategies of Smart Manufacturing to equip the participants with the necessary interdisciplinary skills to become leaders and innovators in the design, deployment and operation of smart production systems or Factories of the Future.


  • Big Data Analytics of the Port Talbot Hot Strip Mill (current)

    Student name:
    Other supervisor: Prof Cameron Pleydell-Pearce
    Other supervisor: Dr Cinzia Giannetti
  • Development and integration of Industry 4.0 Collaborative Robots into Engine Manufacture (current)

    Student name:
    Other supervisor: Dr Christian Griffiths
    Other supervisor: Dr Cinzia Giannetti
  • Development and Integration of Industry 4.0 into Engine Assembly (current)

    Student name:
    Other supervisor: Dr Christian Griffiths
    Other supervisor: Dr Cinzia Giannetti
  • To be confirmed. (current)

    Student name:
    Other supervisor: Dr Cinzia Giannetti
    Other supervisor: Dr Paul Ledger
  • Learning Algorithm Design for Human Robot Skill Transfer (awarded 2019)

    Student name:
    Other supervisor: Dr Cinzia Giannetti
    Other supervisor: Dr Cinzia Giannetti

Career History

Start Date End Date Position Held Location
September 2016 Present Senior Lecturer in College of Engineering Zienkiewicz Centre for Computational Engineering (ZC2E), Swansea University
January 2016 August 2016 Post-doctoral Researcher CHERISH-DE, College of Science, Swansea University
October 2010 December 2015 Research Assistant ASTUTE, College of Engineering, Swansea University

Administrative Responsibilities

  • Mechanical Engineering Portfolio Employability Champion

    2016 - Present

  • Mechanical Engineering Portfolio Exam Officer (PG)

    2016 - Present

Academic History

Date Qualification Location
October 2015 Engineering Doctorate (EngD) Swansea University
June 2009 Postgraduate Certificate in Education (PGCE) in Mathematics Swansea Metropolitan University
April 1996 Master Degree in Applied Mathematics University of Pisa, Italy

Research Groups

  • Zienkiewicz Centre for Computational Engineering (ZC2E)

    Advanced Manufacturing Research Group

Industrial/Teaching Experience

Sept 2008 – June 2010 Teacher training and Mathematics Supply Teacher

Dec 2005 - June 2008 - Engineering Manager NextGen Holdings LTD Swansea, UK

Mar 2000 - June 2003 - Senior Software Engineer Motorola LTD, Swindon, UK

Sept 1997 - Jan 2000 - Software Engineer SIEMENS ICN SpA Milan, Italy

Nov 1996 - Aug 1997 - Technical Support Engineer Lan Systems Srl Bologna, Italy