Hello,


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I am Lorenzo, a research scientist with a strong interest in Social Computing and Information Retrieval, studying how Artificial Intelligence and Information Technology are affecting human life.

I hold a PhD in Information and Communication Technology (Music Technology Group, University Pompeu Fabra, Spain). During the PhD, I have been working under the supervision of Dr Emilia Gómez and Dr Carlos Castillo, and my research has been at the intersection between Music IR and Social Computing. The main goal of my PhD has been to assess the impact of music recommendation diversity on listeners’ attitudes. You can find my PhD dissertation at this link, and some outcomes of my research at this link.

I have been part of TROMPA Project (Towards Richer Online Music Public-domain Archives) an international research project, sponsored by the European Union, investigating how to make public-domain digital music resources more accessible. I have also collaborated with the Musical AI project, funded by the Ministry of Science and Innovation of the Spanish Government, investigating AI to support musical experiences towards a data-driven, human-centred approach.

I hold a Bachelor’s degree in Applied Mathematics from “La Sapienza” University of Rome (2009-2014), a Master’s degree in Sound and Music Computing (2014-2015), and a Master’s degree in Intelligent Interactive Systems (2016-2018) from Universitat Pompeu Fabra. I also had several work experiences in the music industry as Data Engineer (SoundCloud, MonkingMe, BMAT).

Here is what has been my journey until now:

Timeline Lorenzo2022

 

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My top recommended readings

  1. Benjamin, W. (1969). The Work of Art in the Age of Mechanical Reproduction, translated by Harry Zohn, from the 1935 essay. Hannah Arendt, ed., Illuminations. London: Fontana. (pdf)
  2. Molino, J., Underwood, J., & Ayrey, C. (1990). Musical fact and the semiology of music. Music Analysis, 9(2), 105–156. (pdf)
  3. Celma, O., & Cano, P. (2008). From hits to niches? or how popular artists can bias music recommendation and discovery. In 2nd Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition (ACM KDD). (pdf)
  4. Born, G. (2020). Diversifying MIR : Knowledge and Real-World Challenges , and New Interdisciplinary Futures. Transactions of the International Society for Music Information Retrieval, 3, 193–204. (pdf