Practice
Every theoretical concept is anchored to an exercise, a dataset, a project. You learn by building, deepening what you study in class.
At LUMSA, Big Data and Artificial Intelligence are already in the classroom: the LM-77 master's programme and the university's courses give students solid foundations in data, algorithms and their implications. That training is our starting point, and we want to carry it beyond lecture hours: more time to experiment, more shared projects, more occasions for students to learn from one another.
This is why Lumina exists: a student-led, independent, non-profit initiative that works together with LUMSA to deepen what is learned in class. We take what the university teaches us and turn it into peer-run labs, real projects and critical discussions — giving back to the university students who are better prepared, more curious and more aware. Not an alternative to its courses, but a plus that naturally extends them.
Lumina started as an initiative by three LUMSA students from the LM-77 master's programme. We are not a company or a training provider: we are students who want to learn, and to do it alongside other students, peer to peer, working with our university to deepen what we study.
Want to help organise Lumina with us? Get in touch — we're students just like you.
Curious about data and models: he handles the technical side, from analytics tools to the first AI prototypes.
Drawn to the social impact of algorithms: he keeps the thread of ethics, responsibility and critical thinking.
Builds the community and the learning paths: he holds together people, events and the link with the university.
Grow as students by learning to understand, build and question the technologies that will shape our work — with scientific rigour, ethical attention and cultural care, peer to peer and in dialogue with our university.
Recognising biases, social impacts and the responsibility of algorithmic choices before their performance metrics.
Distinguishing what technology can truly do from what is attributed to it in public discourse.
Designing solutions that are useful to people, not just efficient with respect to aggregate metrics.
Activities alternate throughout the academic year, complementing lectures, to combine theory, practice, ethical reflection and peer relationships. A living calendar, built together, not a catalogue.
Intensive sessions on real tools, datasets and pipelines.
Deep dives into research, case studies and public debate.
Open spaces to build, fail and iterate together.
Fundamentals of statistics, data and models, starting from scratch together.
Modern architectures, LLMs, MLOps and data infrastructure.
Continuous updates on emerging technologies.
Student-to-student support, with the help of professors and researchers.
Projects on real problems, with real constraints.
Meetings, community and peer-to-peer exchange opportunities.
Timed challenges to apply what you've learned.
Articles, videos and open repositories for self-study.
Awareness-raising on bias, privacy, social and environmental impact.
Every theoretical concept is anchored to an exercise, a dataset, a project. You learn by building, deepening what you study in class.
Every technology is studied for its consequences too: bias, privacy, work, environment, and the responsibility of choices.
Students, professors, researchers and external organisations dialogue in a horizontal network that grows alongside its own questions.
We measure Lumina not by the number of enrolled students, but by what those who take part manage to do, understand and demand — during the programme and in the years that follow.
Concrete, verifiable knowledge immediately applicable to theses, internships and first professional roles.
A critical stance towards what one builds and what one chooses not to build.
A portfolio of projects and a network of contacts cultivated together during the path.
A contribution to academic and public debate on how to use these technologies well.
We are students looking for academic, institutional and professional organisations willing to support a grassroots initiative that complements and deepens LUMSA's training: those who share our approach — technical rigour, ethical sensitivity and trust in students learning from students.
Propose a collaborationStudents, professors, researchers, external organisations: there is a concrete way to contribute from the very first week.