PeopleofSuperintelligence

Researcher

Emanuele Rodolà

Emanuele Rodolà is an AI researcher whose work connects machine learning with 3D geometry and model reuse. He earned a computer systems engineering degree in 2008, completed a computer science PhD at Ca' Foscari in 2011, and joined Sapienza in 2017 after research posts in Tokyo, Munich and Lugano. Anisotropic convolutional networks learned correspondences between deformable 3D shapes; later, Latent Functional Maps aligned neural representations and MERGE3 supported model merging. He received an ERC Starting Grant for SPECGEO in 2018 and, as of October 2026, is a full professor at Sapienza and leads GLADIA, a machine learning and applied AI group.

Links
Google Scholar

Timeline

  1. 2008–2011

    A computer science PhD

    He graduates in Computer System Engineering at the University of Rome Tor Vergata in 2008, then earns a PhD in the Department of Computer Science at Ca' Foscari in 2011 under Andrea Torsello.

  2. 2012–2017

    Research fellowships

    In 2012, he becomes a JSPS Research Fellow at the University of Tokyo's Intelligent Systems and Informatics Lab. At TU Munich, he joins the Computer Vision lab in 2013 and holds an Alexander von Humboldt postdoctoral fellowship starting in 2014; in 2016, he moves to USI Lugano with Prof. Michael Bronstein for a postdoctoral role through 2017.

  3. 2016

    An anisotropic convolutional network

    He coauthors a paper in 2016 that introduces an anisotropic convolutional neural network for deep learning on non-Euclidean domains. The ACNN learns dense correspondences between deformable shapes.

  4. 2017–2018

    Sapienza and an ERC grant

    At Sapienza's Department of Computer Science, he joins as a researcher in 2017. In 2018, he receives an ERC Starting Grant for SPECGEO, which develops spectral geometric methods with applications in computer vision, machine learning, social networks and computational biology.

  5. 2024

    Aligning neural representations

    In 2024, he coauthors Latent Functional Maps, a spectral geometry framework for comparing learned representation spaces, finding correspondences between them and transferring representations.

  6. 2025

    Evolutionary model merging

    In 2025, he coauthors MERGE3, an evolutionary model-merging framework that makes model merging feasible on a single GPU.

  7. As of October 2026

    Professor and GLADIA leader

    He is a Full Professor of Computer Science at Sapienza University of Rome and leads the GLADIA group for machine learning and applied AI. His research spans representation learning, model merging, graph and geometric deep learning, vision, language and audio.

  1. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI from 1961 to 2025, with a year-by-year chart.
  2. The United States employs an estimated 53% of people in our data with a known employment countryOur estimates of where AI careers are concentrated now and how countries’ shares of dated positions changed over time.
  3. 46% of living people in our data with known birth and employment countries work outside their birth countryOur estimate of cross-border careers in AI, from country of birth and the country of each current organization.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionOur estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Emanuele Rodolà yet. Send us a free-licensed photo.

  1. Computer Vision Group - Dr. Emanuele Rodolà vision.cs.tum.edu. Checked 19 October 2026.
  2. Emanuele Rodolà | GLADIA gladia.di.uniroma1.it. Checked 19 October 2026.
  3. Learning shape correspondence with anisotropic convolutional neural networks arxiv.org. Checked 19 October 2026.
  4. Sapienza ERC Principal Investigators uniroma1.it. Checked 19 October 2026.
  5. Latent Functional Maps: a spectral framework for representation alignment gladia.di.uniroma1.it. Checked 19 October 2026.
  6. MERGE3: Efficient Evolutionary Merging on Consumer-grade GPUs gladia.di.uniroma1.it. Checked 19 October 2026.