How did you come to pursue a career in research? Was it something you had in mind while you were a student?
I didn't expect it; it just sort of happened. However, I remember that during my senior year of college, a professor told me I would probably return to academia, and that's exactly what happened. I spent some time in the software industry and gradually realized that I wanted to do something that would make a meaningful contribution to the issues I considered important.
When I first got involved in research, I became passionate about it, and from that point on, I knew exactly what I wanted to do. That process was largely thanks to Martín Rocamora and my advisors, Pablo Cancela and Guillermo Moncecchi.
What was it like to get into such prestigious universities?
At the academy, I met many people who were willing to help me and work with me. In the same way, today I try to help everyone as much as I can when they write to me or ask for my support.
In my case, whenever I applied for a job, I felt my profile was a good fit for what was required. That’s not always the case; it depends a bit on what you’re looking for and the opportunities available at the time.
The group where I completed my PhD—the Music Technology Group in the Department of Information and Communication Technologies at Pompeu Fabra University—is one of the largest groups focused on music and technology. I felt right at home from day one; it’s a group that prioritizes the human element and is home to excellent researchers.
Meanwhile, Mila, the Quebec Artificial Intelligence Institute, is a research center that brings together several universities in Montreal, including McGill University, with which I am affiliated. Mila conducts research on artificial intelligence and is led by Yoshua Bengio (Turing Award winner for his work on deep learning), one of the most influential scientists in the field of computing.
In any case, the reason I decided to apply to Mila is the emphasis it places on the social impact of artificial intelligence. Its core values include diversity, equity, and inclusion, as well as ethics and social responsibility.
The aim is to contribute to society by considering the impact that algorithms can have on different groups of people. For example, the Biasly AI project is developing a tool that identifies bias in text; it can be used on text that is either automatically generated or written by people.
There seems to be a common thread running through your research, and it has to do with music. Your final thesis project aimed to create a web-based tool to serve as a bridge between people with an interest in music.
I’ve always been interested in topics related to music. Without even realizing it, my senior thesis was the first time I started wondering how to build a music recommendation system, and today, a few years later, that’s what I specialize in.
Where does that interest come from, and what is the research about?
The work I'm currently doing is a continuation of what I did during my PhD in Barcelona in 2018. When I had to choose a thesis topic, I started asking myself what problem I was interested in tackling and thought would be important.
In recent years, awareness has begun to grow about the impact that artificial intelligence can have on people, and I decided to focus on this topic because, until now, it hadn't received much attention from a musical perspective.
Specifically, the thesis focused on examining how algorithms can influence the music we consume and how that affects artists.
One of the projects I worked on examined how certain algorithms tend to recommend fewer female artists. It generated quite a bit of buzz; the story was picked up by various media outlets around the world, and we did interviews on radio and television. It was all a bit unexpected, but it’s nice to see that people are interested in the work you do.
Furthermore, the importance of reaching beyond the academic sphere lies in the fact that it raises public awareness and provides an opportunity to demand change from platforms and the industry.
Today, I am part of an interdisciplinary team focused on music recommendation systems with goals that go beyond the commercial. I am supervised by Fernando Diaz, an associate professor at McGill and a researcher at Google.
We are part of a larger project involving researchers from around the world. It is an ERC (European Research Council) project, led by Georgina Born of the University of Oxford, and aims to study the use of artificial intelligence in music, not only in terms of recommendations but also in the production process. This includes, for example, the use of artificial intelligence in instruments, recording consoles, etc., used to produce music, as well as its use to generate music automatically.
How do you rate the skills you gained from ORT’s Systems Engineering program?
First, it’s worth noting that Uruguay has excellent universities and outstanding professors. This is evidenced by the number of Uruguayans who are doing highly acclaimed work, both within and outside the country.
One of the things I’ve always appreciated about the program at ORT is the ethics course we took—I think it was in our third year. At first, I didn’t really understand the point of it, but now I realize how important it is to train professionals who can think critically.