Department of Engineering / Profiles / Mr Leonidas Aristodemou

Department of Engineering

Mr Leonidas Aristodemou

la324

Leonidas Aristodemou

Research Student

Academic Division: Manufacturing and Management

Email: la324@eng.cam.ac.uk

Personal website


Research interests

Leonidas' research focuses on the role of machine learning in technology management processes. More specifically, Leonidas analyses big datasets of intellectual property data and forecasts technological value/impact using deep learning models. This is anticipated to improve the technology strategic decision-making processes within innovation management models.

Research projects

PhD Research:

Innovation and Intellectual Property Management (IIPM) Research Group:

  • EPSRC The Future of Patent Analytics
  • Artificial Intelligence usage within the Innovation Process
  • Citations as measure of Technological Impact
  • Analyzing Patent Influence and Patent Importance Across the Industrial Boundary of 3D Printing

Centre for Technology Management: 

Teaching activity

Teaching Assistant:

Course Supervisor: 

  • 3P4 Operations management, Master of Engineering (MEng) Part IIA course, at Cambridge University Engineering Department (CUED)
  • 3P5 Industrial engineering, Master of Engineering (MEng) Part IIA course, at Cambridge University Engineering Department (CUED)
  • 3P9 Industrial economics, strategy and governance, Master of Engineering (MEng) Part IIA course, at Cambridge University Engineering Department (CUED).

Master Thesis Supervisor:

  • Thaksan Sothinathan (2018), Exploring Artificial Intelligence usage within the Innovation Process: a qualitative use case approach, Master of Science, Electrical Engineering and Business Administration, RWTH Aachen University
  • Matthew Shaw (2018), Exploring strategic selection criteria for the technology development stage gate process: a survey approach, Master of Engineering, Manufacturing Engineering Tripos Part IIB, University of Cambridge
  • Elizabeth O'Leary (2018), Exploring strategic decision making and selection criteria motives for technology projects at the front end of innovation, Master of Engineering, Manufacturing Engineering Tripos Part IIB, University of Cambridge
  • Ekaterina Essina (2018), Data Science Applications and Text Representation of Patent Datasets, Master of Engineering, Manufacturing Engineering Tripos Part IIB, University of Cambridge.
  • Samuel Deeble (2018), Application of Supervised Machine Learning Methods for Technology Management, Master of Engineering, Manufacturing Engineering Tripos Part IIB, University of Cambridge.

Research opportunities

Computer Science:

  • Big Data and Algorithms
  • Artificial Intelligence
  • Machine Learning
  • Deep Learning
  • Natural Language Processing

Management Science:

  • Technology and Innovation Management
  • Intellectual Property Management
  • Intellectual Property Analytics
  • Technology Development Process Models
  • Strategic Decision-making

Biography

Leonidas is a 3rd year PhD student, who has recently joined the The Alan Turing Institute as a PhD Enrichment student. He is supervised by Dr. Frank Tietze, from the Centre for Technology ManagementInstitute for ManufacturingDepartment of EngineeringUniversity of Cambridge, and advised by Prof. Tim Minshall and Dr. Alexandra Brintrup. He obtained a Masters in Engineering (MEng) with Distinction, and a Bachelor of Arts (BA) in Engineering, from the University of Cambridge. He went on to work for Procter and Gamble in the field of Technology and Operations Management. He is an executive board member of the Cambridge University Engineers Association and a member of St. Edmund’s College

Publications:

  • Aristodemou, L., & Tietze, F. (2018). The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data. World Patent Information, 55 37-51https://doi.org/10.1016/j.wpi.2018.07.002

  • Aristodemou, L., & Tietze, F. Citations as a measure of technological impact: a review of forward citation-based measures. World Patent Information, 53 39-44. https://doi.org/10.1016/j.wpi.2018.05.001Conference articles:

Conference articles:

  • Aristodemou, L., Tietze, F., Brintrup A., 2018. Early Stage Technology Strategic Decision Making: a machine learning approach using Intellectual Property Analytics, In Track 18: Big Data Analytics for R&D Management, R&D Management Conference 2018, Milan, Italy

  • Aristodemou, L., Tietze, F., Athanassopoulou, N., and Minshall, T., 2017. Exploring the Future of Patent Analytics: A Technology Roadmapping Approach. In Theme MC-4: Intellectual Property Management in Innovation, R&D Management Conference 2017, Leuven, Belgium

Working papers:

  • Aristodemou, L., Tietze, F., Athanassopoulou, N., and Minshall, T., 2017. Exploring the Future of Patent Analytics: A Technology Roadmapping Approach, Centre for Technology Management (CTM) Working Paper Series, November(5), pp.1-10, Cambridge, UK. Available at: http://doi.org/10.17863/CAM.13967

  • Aristodemou, L. & Tietze, F., 2017. A literature review on the state-of-the-art on intellectual property analytics, Centre for Technology Management (CTM) Working Paper Series, November(2), pp.1-15, Cambridge, UK. Available at:http://doi.org/10.17863/CAM.13928

Reports:

  • Aristodemou, L. & Tietze, F., 2017. Exploring the Future of Patent Analytics. Centre for Technology Management (CTM) Insights Report, ISBN: 978-1-902546-84-1, Institute for Manufacturing, University of Cambridge, Cambridge, UK. Available at:https://www.ifm.eng.cam.ac.uk/insights/innovation-and-ip-management/expl... the-future-of- patent-analytics/

  • Aristodemou, L., 2015. Analysing Patent Influence and Patent Importance Across the Industrial Boundary of 3D Printing, Masters Thesis, Institute for Manufacturing, University of Cambridge

Department role and responsibilities

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