I'm a professor at the University of Bordeaux, Institute of Mathematics. My research lies at the intersection of mathematics and perceptual science and could be described as "mathematical psychophysics". It focuses on modeling color perception through the broad variety of mathematical techniques used in mathematical physics. I feel lucky to share this research interest with Michel Berthier (University of La Rochelle, France) and our PhD students and collaborators.
You can take a look at my CV for more details.
My current research focuses on understanding and mathematically modeling how humans perceive colors, at the intersection of mathematical physics, information theory, and vision science. It is well known that the classical CIE model of color reproduction, while satisfactory for reproduction purposes, is not suited for the analysis of human color perception. Despite this, very few researchers dare to propose theoretical frameworks that move beyond the CIE paradigm and offer a deeper understanding of perceptual color phenomena.
This lack of alternative models has important practical consequences for signal and image processing. Extending grayscale algorithms to color images leads to a proliferation of color spaces, often chosen through heuristic and suboptimal artefact-control strategies, and long-standing problems still lack universally accepted solutions.
The model that Michel Berthier and I are developing proposes a radically novel framework based on quantum information theory: perceived colors are modeled as outcomes of quantum measurements performed on quantum chromatic states. This paradigm naturally reconciles trichromacy with Hering’s opponency theory and allows perceptual attributes such as hue, saturation, and brightness to be rigorously defined, among many other implications.
Beyond its intrinsic scientific interest, I consider this topic timely in view of the convergence between artificial intelligence and robotics. A deeper understanding of visual perception will be crucial for autonomous systems to interact with their environment in a robust and adaptive manner. Approaching the efficiency, flexibility, and contextual awareness of human perception remains a major challenge for AI and robotics, and progress in perceptual modeling is expected to play a key role in addressing it.
These are the courses that I've taught in several universities in France, Italy, Spain and Cuba (more details can be found in my CV).
For the courses of Linear Algebra, Probability and Functional Analysis I practice the flipped classroom with video support. You can find the videos of Linear Algebra and Probability (in English) on my YouTube page: www.youtube.com/@edoardo_provenzi
The certificate of teaching efficiency of the UPF is here (Evaluation)
I update my publications in my CV.
Here are a couple of books I wrote.
As it is well-known, the contemporary academic system increasingly operates under the logic of publish and perish. Research activity is often evaluated through simplified quantitative metrics, as the infamous H-index, that are supposed to measure scientific quality and impact.
This has gradually produced a form of intellectual dependence on numerical indicators. Careers, reputations, and even institutional strategies are often shaped by numbers that attempt to compress the complexity of scientific activity into a single score. Yet there is arguably nothing easier, and nothing more superficial, than judging a researcher or an institution through a single number.
Such practices resemble what Daniel Kahneman famously described as fast thinking: quick, automatic judgments that give the comforting illusion of objectivity while bypassing deeper and more careful evaluation. Scientific research, however, belongs to the realm of slow thinking. It requires time, nuance, context, and intellectual judgment—qualities that cannot be captured by a single bibliometric indicator.
The reliance on poorly defined quantitative criteria risks transforming academic evaluation into a mechanical exercise. When numbers become the primary lens through which research is assessed, the richness of scientific work, embodied by its originality, conceptual depth, and long-term influence, tends to disappear behind simplified rankings and metrics.
Science has always progressed through creativity, cooperation, and intellectual curiosity. Reducing researchers to numerical profiles undermines precisely those qualities that make scientific inquiry meaningful and productive.
IMB Institute de Mathématiques de Bordeaux UMR 5251, Université de Bordeaux
351, cours de la Libération, 33405 Talence, France
email: name dot last name @ math dot u dash bordeaux dot fr