c.curionodeThe Fields Archive

2018 · Fields Medal

Alessio Figalli

Optimal transport compares distributions by moving mass between them, turning a question about cheapest routes into deep information about geometric regularity.

Birth location
Rome, Italy
Country labels
Italy
At the award
ETH Zurich
Subject
Analysis & PDEs
Read the award story ↗
Illustrated portrait of Alessio Figalli
Original AI-generated illustration by Curionode. Heidelberg Laureate Forum Foundation; named institutional laureate profile · Likeness reference ↗

01 · A life in mathematics

The person behind the work

Alessio Figalli was born in Rome and trained in Italy and France. He was a professor at ETH Zurich, which he joined in 2016, when awarded the 2018 Fields Medal. His research centers on optimal transport, the mathematical study of moving one distribution of mass into another at minimum cost. He uses this framework to study nonlinear partial differential equations, geometric inequalities and the regularity of solutions. A central achievement is understanding when transport maps and the equations they satisfy remain well behaved despite rough data or singularities. His work connects abstract optimization with the fine structure of shapes and evolving systems.

The contribution, explained

Imagine reshaping one pile of sand into another while minimizing the total transportation cost. Optimal transport turns this picture into a precise problem for probability measures or mass distributions. A cheapest transport plan may exist, but existence alone does not tell us whether the movement is smooth or has sudden discontinuities. Figalli developed tools to understand such regularity questions and the nonlinear equations behind them, notably the Monge–Ampère equation. He also connected transport with sharp geometric inequalities and their stability: if a shape almost achieves an optimal bound, how close must it be to an ideal shape?

Why it matters

Transport becomes a versatile method for studying nonlinear equations, geometric shapes and probability distributions. Figalli’s results clarify when optimal behavior has stable, regular structure and when singularities or nearly optimal configurations require subtler analysis.

Sources & further reading

  1. Official IMU citation ↗www.mathunion.org
  2. Heidelberg Laureate Forum ↗www.heidelberg-laureate-forum.org
  3. ETH Zurich: Alessio Figalli wins the Fields Medal, 1 August 2018 ↗ethz.ch
  4. IMU: Figalli detailed citation ↗www.mathunion.org