Are you passionate
about Artificial Intelligence Systems?
Do you want to do research
on the development
and maintenance
of the software of the future?
In the sustainability of AI systems team at UPC,
we explore four different
dimensions of sustainability:
environmental, economic, social,
and technical.
Let's explore the research lines
in which you could do you
your
"Introduction to Research" course, or the Bachelor,
Master, and PhD thesis.
In my research
I'm studying how machine learning models
change and evolve over time by mining
large-scale repositories
like Hugging Face
and analyzing them using Large Language Models.
Moreover, by measuring the carbon footprint and labelling the energy efficiency of these models
I'm also identifying
best practices
on how to report and reduce
the energy consumption and how to improve
the overall efficiency
of these systems. To make AI more sustainable
we also need to consider its economic implications
That's why I've been studying machine
learning optimizations
and how they affect cost. My goal is
to estimate the return on investment and also to estimate how many inferences are needed to make the systems both
efficient and
cost-effective in the long run.
AI and machine learning are also relevant
in our societies and communities.
To explore the social impact,
I study
gender diversity and the role of woman
in Hugging Face. This research
highlighted the importance of inclusivity
and the long
way we still have in order to
achieve gender diverse
software engineering teams. Another of our research lines
is on MLOps. And what's MLOps?
Well, it stands for Machine
Learning Operations
and it's basically an approach to
managing and structuring the whole life cycle
of an AI system. Because AI it's not only about building
the best AI model, but also about testing
deploying, and re-training
and much more. Put it
simply, it's what makes
an excelent model
like GPT-4o become the AI system that we know
as ChatGPT. But that's not all. In the group we are also researching
how we can make AI systems more sustainable
by aggregating
the empirical evidence on applying pruning
quantization or the selection
of a runtime engine can
impact the greeness of an AI
system. My role in the lab focuses
on developing tools such
as GAISSA ROI Analyzer.
This tool helps
both researchers
and businesses
understand the impact
of machine learning optimizations.
This project has allowed me to develop
my bachelor's thesis within the Green AI field
in a very strong
and skilled team.
My work focuses on integrating
sustainability as a non-functional requirement
in the development of Machine learning-enabled systems.
My goal is to develop a
sustainable machine learning-enabled systems,
generative AI systems,
generate green code,
and promote responsible AI systems.
Hi, my name is Xavier Franch and I’m the scientific
coordinator of the GESSI Research Group
at the UPC,
the research group on Software
and Service Engineering.
The group has more than 30 years
now and we have been conducting
research and transfer of Technology
in different topics like Requirements
Engineering, Software Architecture and
More recently the interplay
Between Software Engineering
and Artificial Intelligence.
In this context,
we are currently conducting
several projects both
at the national and the European level
and in these projects
we are more than happy to onboard
students of different kinds and expertise, from
Undergraduate to master
to PhD students.
You are more than welcome to
join us! We can offer
different topics at your
convenience and also the advantage to travel around
with our collaborators and perform research
in other centers.