From theory to impact: rethinking how we train intelligent systems
Luiz Chamon joins Hi! PARIS as
a new Chair-holder, bringing a fresh perspective on the mathematical
foundations of AI, and what it takes to make machine learning trustworthy by
design.
Luiz Chamon’s career has taken him from São Paulo to Philadelphia, from Berkeley to Stuttgart, and now to École polytechnique and Hi! PARIS, where he’s at the frontier of AI and engineering. But his question has remained the same: how can we design intelligent systems that truly serve human needs?
“I’m interested in how systems learn from data — but also in how they do it,” Chamon explains. “We need learning methods that don’t just optimize accuracy, but meet strict requirements: fairness, robustness, consistency with science.” That goal calls for a deeper rethink of the foundations of AI. “Too often, constraints like safety or fairness are treated as afterthoughts. I believe they should be built in from the start.”
— Luiz Chamon, Hi! PARIS Chair Holder
Engineering intelligence, not just optimizing it
Chamon’s work looks under the hood of machine learning, using tools from control theory, optimization, and signal processing. His ambition is to shift the focus away from trial-and-error learning toward a model where requirements guide the design of intelligent systems, what he calls “requirement-driven learning.”
“It’s not about perfection. But it is about knowing what we want our systems to do, and making sure they do it. That’s engineering. That’s design.”
He frames this not just as a technical challenge, but as a necessary evolution of the field.
“Artificial intelligence today often means discovering patterns in data. But in many cases, we already know what our systems must respect, physical laws, ethical boundaries, domain constraints. Learning should start from there.”
The bigger picture: AI as infrastructure
At Hi! PARIS, Chamon is joining a community focused on interdisciplinary, responsible AI. His project fits into a broader ambition: to reimagine the role of AI in society, not as an opaque tool, but as a piece of critical infrastructure.
“AI is already shaping our world, sometimes in ways we understand, sometimes in ways we don’t,” he says. “To make it sustainable, we need more than technical performance. We need trust, traceability, clarity.”
That’s where his research comes in. “Mathematical foundations aren’t just abstract. They’re how we make sure AI works, and works for everyone.”
A place to connect
For Chamon, joining Hi! PARIS was also a question of context. “What attracted me was the environment. The chance to work across disciplines, connect with partners in science, engineering, business, all in one ecosystem.”
He sees this as essential for moving from theory to real-world impact. “The challenges we face, misinformation, bias, instability, aren’t just technical. They’re social, political, economic. And solving them requires teams that reflect that complexity.”
With that mindset, Chamon’s arrival marks more than a new Chair. It signals Hi! PARIS’ continued investment in building AI not just as a technology, but as a shared responsibility.
“I want to help shift the mindset, from artificial intelligence as something that ‘emerges’ from data to something we build, together, with intention.”