Computer vision · Machine learning · AI for health

J. Matias
Di Martino

  • Chief Scientific Officer, Centro Nacional de Inteligencia Artificial (UY)
  • Full Professor, Universidad Católica del Uruguay
  • Adjunct Research Professor, Duke University (US)
Email me Google Scholar LinkedIn CV (1 page) CV (full)

About

Researcher, educator, and consultant.

I am the Chief Scientific Officer of the Centro Nacional de Inteligencia Artificial (Uruguay), Full Professor at Universidad Católica del Uruguay, and an Adjunct Research Professor at Duke University. I love working on open-ended AI projects; my interests include computer vision, image processing, machine learning, remote sensing, and AI for health applications.

Over the last 15+ years I have researched, designed, and deployed machine learning solutions — from classical pattern recognition to modern deep learning — taught undergraduate and graduate courses, led large-scale research projects, and published over 80 peer-reviewed papers in top journals and conferences. As key personnel and PI, I contributed to securing over $15M in research funds and mentored several MSc and PhD students. I also work as a consultant, supporting private and public companies in tackling complex technical problems.

Research

Computer vision, machine learning, and image processing.

My recent focus is on computer vision for autism screening and behavioral phenotyping — work highlighted by NIH as one of the top five scientific discoveries in human health of 2023 — alongside NLP to better understand ARFID, computer vision to improve transcranial magnetic stimulation, novel imaging paradigms for face recognition and anti-spoofing, remote sensing, and signal optimization for fraud detection in power networks.

1,800+ citations · h-index 21 · 80+ peer-reviewed papers · $15M+ in funded research — Google Scholar

AI for health & behavioral phenotyping

Computer vision for autism screening and behavioral analysis of young children — highlighted by NIH as a top-5 2023 discovery in human health — and NLP to better understand ARFID.

3D face analysis

Novel imaging paradigms to improve face recognition, prevent spoofing attacks, and guide trackerless neuronavigation for transcranial magnetic stimulation.

Image processing

Variational methods and PDE-based formulations — in particular Poisson equations — with a strong emphasis on reproducible research (IPOL).

Applied optics & structured light

Phase retrieval and one-shot 3D sensing with active light: the fringe-projection techniques behind my PhD and a dozen optics papers.

Energy & fraud detection

Detecting non-technical losses and modeling power-consumption patterns with UTE, Uruguay's national power company — learning from highly imbalanced data.

Smart agriculture

Fruit detection and visual SLAM for agricultural environments, developed with collaborators at Universidad de la República.

Selected publications

Where the work appeared.

AI for Health

  • New England Journal of Medicine Krishnappababu et al., 2024
  • Nature Medicine Perochon et al., 2023
  • Nature npj Digital Medicine Perochon et al., 2023
  • Nature Scientific Reports Krishnappa et al., 2023
  • Computers in Biology and Medicine Schlesinger et al., 2024
  • JAMA Pediatrics Chang et al., 2021
  • IEEE Trans. on Affective Computing Krishnappababu et al., 2021
  • IJCARS Chaudhary et al., 2022
  • Int. Journal of Eating Disorders Kim et al., 2021
  • JCPP Perochon et al., 2020

Computer Vision & AI

  • ACL Findings Schlesinger et al., 2026
  • CVPR Findings Schlesinger et al., 2026
  • TMLR Yuzhou et al., 2025 · Kim et al., 2024
  • Int. Journal of Robotics Research Marzoa et al., 2024
  • IEEE Trans. on Image Processing Di Martino et al., 2020
  • IEEE TPAMI Di Martino et al., 2020
  • ICCP Di Martino et al., 2020
  • IPOL Di Martino et al., 2018 · 2016
  • ICASSP Achddou et al., 2021
  • ICPR Rodriguez et al., 2015 · Fiori et al., 2016
  • Journal of Pattern Recognition Di Martino et al., 2013

Energy

  • IEEE Trans. on Smart Grid Massaferro et al., 2022
  • IEEE Trans. on Power Systems Massaferro et al., 2019
  • ISGT Massaferro et al., 2021
  • PESGM Mariño et al., 2023 · Massaferro et al., 2018

Signal Processing & Optics

  • Applied Optics Casaballe et al., 2020 · Ayubi et al., 2016, 2014, 2011 · Flores et al., 2013 · Di Martino et al., 2012
  • Optics and Lasers in Engineering Di Martino et al., 2018, 2015, 2014
  • Optics Letters Ayubi et al., 2012, 2011 · Flores et al., 2012 · Di Martino et al., 2013
  • Optics Communications Di Martino et al., 2013

Full list on Google Scholar

Experience

Appointments.

  1. 2026–present Chief Scientific Officer
    Centro Nacional de Inteligencia Artificial, Uruguay
  2. 2024–present Full Professor, Computer Science
    Universidad Católica del Uruguay
  3. 2024–present Adjunct Research Professor
    Duke University, US
  4. 2024–present Visiting Research Scholar
    Princeton University, US
  5. 2025–2026 Department Chair, Computer Science
    Universidad Católica del Uruguay
  6. 2017–present Consultant
    Details upon request
  7. 2019–2024 Assistant Research Professor
    Duke University, US
  8. 2016–2022 Associate Professor
    Universidad de la República, Uruguay
  9. 2018–2019 Postdoctoral Associate
    Duke University, US
  10. 2015–2016 Chercheur Associé
    École Normale Supérieure, France
  11. 2011–2016 Teaching and Research Assistant
    Universidad de la República, Uruguay

Education

  1. 2011–2015 PhD in Engineering and Signal Processing
    Universidad de la República, Uruguay
  2. 2005–2011 Electrical Engineering (ranked 1st of 164)
    Universidad de la República, Uruguay

Fellowships & service

Researcher level II of the SNI (Sistema Nacional de Investigadores, Uruguay) and level 4 of PEDECIBA in Physics and Computer Science. Member of the IPOL Editorial Board (2018–2023). Reviewer for top AI venues and journals, including AAAI, CVPR, Pattern Recognition, and Nature.

Grants

Funded research.

  • 2026–2029 Monitoreo eficiente del territorio mediante la super-resolución de imágenes satelitales
    ANII Fondo María Viñas — role: PI (Co-PI: Dr. Lara Raad)
  • 2023–2027 Accurate, low-cost, trackerless neuronavigation for transcranial magnetic stimulation
    NIH 1R01-MH129733-01 — role: co-PI
  • 2021–2024 Feeling and Body Investigators (FBI) — ARFID Division: sensory and somatic exposure for children with avoidant restrictive food intake
    NIH R33-MH-121549 — role: co-I (PIs: Drs. Zucker and Sapiro)
  • 2020–2021 Accurate, affordable, and easy-to-use navigation for transcranial magnetic stimulation
    Duke Institute for Brain Sciences, Research Germinator Award — role: co-I (PIs: Drs. Peterchev, Sapiro, Goetz, and Turner)
  • 2019–2023 Scalable computational platform for active closed-loop behavioral coding in autism spectrum disorder
    NIH 1R01-MH120093-02 — role: co-I (PIs: Drs. Dawson and Sapiro)
  • 2021–2023 Power disaggregation and identification via multi-scale information fusion
    UTE–UDELAR — role: co-PI
  • 2018–2020 Decoupling domestic electricity consumption
    UTE–CSIC — role: co-PI
  • 2017–2019 3D reconstruction with structured light
    CSIC I+D — role: co-PI

Teaching & mentoring

Fifteen years in the classroom.

I have taught Physics, Electrical Engineering, Computer Vision, and AI at UCU, Duke, and Universidad de la República — coordinating courses with over 1,000 students — and mentored Master's and PhD students throughout.