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 the value of patented inventions using deep learning models. This is anticipate to improve the valuation of technologies using multiple proxies with variable time horizons.

Research projects

PhD Research:

Innovation and Intellectual Property Management (IIPM) Research Group:

  • EPSRC The Future of Patent Analytics
  • Climate change mitigation analysis (CCMT): machine learning models and patent analysis
  • Exploring disruptive innovation and disruptive technologies using machine learning
  • 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:

  • Maximilian Elsen (2020), Climate-Change Mitigation Technologies (CCMTs): A Patent Analysis and Forecasting Approach, Master of Science, Business Analytics, University College London (UCL)
  • 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
  • Sustainable innovation and engineering
  • Intellectual Property Management
  • Intellectual Property Analytics
  • Technology Development Process Models
  • Strategic Decision-making

Biography

Leonidas is a 4th year PhD student, supervised by Dr. Frank Tietze, from the Centre for Technology ManagementInstitute for ManufacturingDepartment of EngineeringUniversity of Cambridge. His thesis, 'Identifying valuable patents: a deep learning approach' will be examined by Prof. Tim Minshall and Prof. Ove Granstrand.  Leonidas had joined the The Alan Turing Institute as a PhD Enrichment student for the academic year 2018-2019. 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, a member of St. Edmund’s College, and a member of the European Artificial Intelligence Alliance.

Publications:

  • Tietze, F., Vimalnath, P., Aristodemou, L., and Molloy, J., 2020. Crisis-Critical Intellectual Property: Findings From the COVID-19 Pandemic, IEEE Transactions on Engineering Managementhttps://doi.org/10.1109/TEM.2020.2996982

  • Aristodemou, L., Tietze F., and Shaw, M., 2020. Stage Gate Decision making: a scoping review of Technology Strategic Selection Criteria for Early Stage Projects, IEEE Engineering Management Review, 48, 118-135. https://doi.org/10.1109/EMR.2020.2985040

  • 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., 2018. 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.2018.05.001

Conference articles:

  • Li, X., Aristodemou L., Tietze, F., Jeong, Y., 2020. Disruptive technologies: characteristics and early identification, using machine learning and text mining from patent data, In Theme: The future of R&D and Innovation, R&D Management Conference 2020 (postponed due to COVID19), University of Strathclyde, Glasgow, Scotland, United Kingdom

  • Aristodemou L., & Tietze, F., 2020. Technological value characteristics and identification of valuable technologies: a novel deep learning based methodology using patent data and intellectual property analytics, In Theme: The future of R&D and Innovation, R&D Management Conference 2020 (postponed due to COVID19), University of Strathclyde, Glasgow, Scotland, United Kingdom

  • Aristodemou L., & Tietze F., 2019. Early Stage Identification of Valuable Technologies: a Deep Learning approach, In Topic: Intellectual Property and New Research Methods, European Policy for Intellectual Property (EPIP) Conference 2019, ETH Zurich & EPFL, Zurich, Switzerland

  • Jeong Y., Aristodemou L., & Tietze F., 2019. Exploring disruptive innovation opportunity using patent analysis and deep learning, In Track 1: Artificial Intelligence and Data Science, R&D Management Conference 2019L' École Polytechnique & HEC Paris, Paris, France

  • Silva, R., Koshiyama, A., & Aristodemou, L., 2019 Linking Research Entities to Industrial Sectors: a hybrid methodology applied to Brazil’s nanotechnology sector, In Data for Policy 2019: Digital Trust and Personal Data Conference, University College London, London, United Kingdom

  • 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, Politecnico di Milano, Milan, Italy

  • Aristodemou, L., Tietze, F., Athanassopoulou, N., & 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, KU Leuven, Leuven, Belgium

Working papers:

  • Tietze, F., Vimalnath, P., Aristodemou, L., Mollow, J., 2020. Crisis-Critical Intellectual Property: Findings from the COVID-19 Pandemic. Centre for Technology Management (CTM) Working Paper Series, April 2020 (2), pp. 1-18. https://doi.org/10.17863/CAM.51142 ; http://dx.doi.org/10.2139/ssrn.3569282

  • Aristodemou, L., Tietze, F., Brintrup, A., & Deeble, S., 2019. Intellectual Property Analytics Decisions Support Tool (IPDST) for Early Stage Technology Decision Making. Centre for Technology Management (CTM) Working Paper Series, January 2019 (1), pp.1-7. https://doi.org/10.17863/CAM.35544

  • Aristodemou, L., Tietze, F., O'Leary, E., & Shaw, M., 2019. A Literature Review on Technology Development Process (TDP) Models. Centre for Technology Management (CTM) Working Paper Series, January 2019 (6), pp.1-32, Cambridge, UKhttps://doi.org/10.17863/CAM.35692

  • Aristodemou, L., & Tietze, F., 2019. Technology Strategic Decision Making (SDM): an overview of decision theories, processes and methods. Centre for Technology Management (CTM) Working Paper Series, January 2019 (5), pp.1-22, Cambridge, UKhttps://doi.org/10.17863/CAM.35691

  • Aristodemou, L., Tietze, F., Athanassopoulou, N., & Minshall, T., 2017. Exploring the Future of Patent Analytics: A Technology Roadmapping Approach, Centre for Technology Management (CTM) Working Paper Series, November 2017 (5), pp.1-10, Cambridge, UK. 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 2017 (2), pp.1-15, Cambridge, UKhttp://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, UKhttps://www.ifm.eng.cam.ac.uk/uploads/Resources/Reports/Future_patent_analytics.pdf

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

Online Articles:

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