Andrea de Giorgio
Academic Titles and Experience
Andrea is currently a Research Associate at the University of Luxembourg. He received his PhD in Production Engineering from KTH in 2021. He was a member of the Board of Directors at KTH from July 2016 to June 2018. He has a BSc in Electronic Engineering and a MSc in Computer Science and Engineering with major in Machine Learning. He is also an experienced freelance Web Developer since 2005.
Andrea's current research focuses on procedural knowledge transfer for human-machine collaboration, applied to operator-centered assembly lines, runtime failure prevention in process planning and path planning of robot manipulators. He is also researching on industrial uses of augmented and/or virtual reality and safety of operators working with industrial robots.
PhD Chapter at KTH
Andrea was Chairperson 2016/2018 of the PhD Chapter at KTH, the doctoral students' network. He had been member of their Board from November 2015 to June 2018. PhD students' representations included the KTH Board of Directors (US), the Directors of Third Cycle Research Education (FA) Committee, the Faculty Council (FR) Committee, the Employment Committee (AU) and the Promotion Committee (BN).
Courses and Projects
Andrea currently trains Judo (Black belt, 3rd dan) in Luxembourg. In Sweden, he was a trainer at the Stockholms Judoklubb. He also loves to go skiing and spends his time with Rotary (Rotary Club of Stockholm International), during the year 2018/19 in the role of President. A list of many books he has read is on aNobii.
Links for direct access to some published material may be working only if you or your institution are subscribing to the host library.
 Andrea de Giorgio, (2021). Thesis: Introducing a procedural knowledge model for enhancing industrial process adaptiveness. KTH Royal Institute of Technology. DOI: 10.13140/RG.2.2.20678.60485
 Andrea de Giorgio, Malvina Roci, Antonio Maffei, Milan Jocevski, Mauro Onori, Lihui Wang, (2021). Article: Measuring the effect of automatically authored video aid on assembly time for procedural knowledge transfer among operators in adaptive assembly stations. International Journal of Production Research. DOI: 10.1080/00207543.2021.1970850
 Andrea de Giorgio, Antonio Maffei, Mauro Onori, Lihui Wang, (2021). Article: Towards online reinforced learning of assembly sequence planning with interactive guidance systems for industry 4.0 adaptive manufacturing. Journal of Manufacturing Systems, vol. 60, 22-34, 2021. DOI: 10.1016/j.jmsy.2021.05.001.
 Andrea de Giorgio, Lihui Wang, (2020). Article: Artificial Intelligence Control in 4D Cylindrical Space for Industrial Robotic Applications. IEEE Access, vol. 8, s. 174833-174844, 2020. DOI: 10.1109/ACCESS.2020.3026193
 Andrea de Giorgio, Magnus Lundgren, Lihui Wang, (2020). Conference paper: Procedural knowledge and function blocks for smart process planning. Procedia Manufacturing, Volume 48, 2020, Pages 1079-1087. DOI: 10.1016/j.promfg.2020.05.148.
 Franco Angotti, Giuseppe Pelosi, Mario Cospito, Andrea de Giorgio, Sarah Ouakim, Benedetta Pelosi, Stefano Selleri, Nello Carrara (2019). Book: Guglielmo Marconi and Enrico Fermi: Two Nobel Prizes seen by the Rotarian Nello Carrara. Amazon. Download the PDF here.
 Andrea de Giorgio, Sarah Ouakim (2019). Chapter: Preface to Guglielmo Marconi and Enrico Fermi: Two Nobel Prizes seen by the Rotarian Nello Carrara. Amazon, pp. v-vi. Download the PDF here.
 Andrea de Giorgio, Mario Romero, Mauro Onori, Lihui Wang (2017). Conference paper: Human-machine Collaboration in Virtual Reality for Adaptive Production Engineering. Procedia Manufacturing, Volume 11, 2017, Pages 1279-1287, ISSN 2351-9789. Keywords: Virtual Reality; Augmented Reality; Unity Game Engine; Human-Robot Collaboration; Industry 4.0; Robotics; Adaptive Production. DOI: 10.1016/j.promfg.2017.07.255.
 Francesco Donnarumma, Roberto Prevete, Andrea de Giorgio, Guglielmo Montone, Giovanni Pezzulo (2016). Article: Learning programs is better than learning dynamics: A programmable neural network hierarchical architecture in a multi-task scenario. Adaptive Behavior, February 2016, vol. 24, no. 1, pp. 27-51. DOI: 10.1177/1059712315609412.
 Andrea de Giorgio (2015). Thesis: A study on the similarities of Deep Belief Networks and Stacked Autoencoders. KTH, Royal Institute of Technology. Download the PDF here.
 Andrea de Giorgio (2013). Thesis: Learning and Neural Encoding of Multiple Behaviors (in Italian). / Tesi di laurea: Apprendimento e Codifica Neurale di Comportamenti Multipli. Università degli Studi di Napoli Federico II. Download the PDF here.
Andrea's ORCID: http://orcid.org/0000-0001-6064-5634.
Andrea's ResearcherID: D-5407-2016.