Paolo Conti

Principal Research Scientist

Autodesk Research

I am a Principal Research Scientist in the Physics-Informed AI team at Autodesk Research in London. I am fascinated by the immense world that arises from blending core numerical methods with machine learning, bringin together when that combination serves engineering, design, and reliable prediction of physical systems. Lifelong learning is my attitude, and working day-by-day for long-term goals is my methodology. Mathematical engineer by formation, aerobic gymnast by passion.

I am fascinated by the immense world that arises from blending core numerical methods with artificial intelligence learning techniques, which is what I am focusing on in my research activities. Lifelong learning is my attitude, and working day-by-day for long-term goals is my methodology. Mathematical engineer by formation, aerobic gymnast by passion.

Interests

Scientific Machine Learning
Reduced Order Modeling
Multi-fidelity data fusion

Education

PhD in Scientific Machine Learning
Politecnico di Milano, University of Washington
MSc in Mathematical Engineering, 2021
Politecnico di Milano, Sorbonne University
BSc in Mathematical Engineering, 2018
Politecnico di Milano

Experience

Principal Research Scientist

Autodesk Research

April 2025 – Present London, UK

Principal Research Scientist in the Physics-Informed AI team, advancing physics-informed AI at the intersection of advanced computation, engineering expertise, and design innovation.

Research Associate

The Alan Turing Institute

March 2025 – April 2026 London, UK

Research Associate in Fundamental Research in AI for physical systems, with the aim of developing the next generation of foundational methods, tools and theory to enable modelling, prediction and control of physical systems.

Visting Researcher

Imperial College London

December 2023 – January 2024 London, UK

Led a research project to develop generative AI/ML frameworks for data-driven, reduced-order modeling under uncertainty.

Conceptualized and implemented a data-driven framework based on variational reduced-order modeling with variational dynamics identification for scientific discovery in the presence of model and measurement uncertainties.

Visting Researcher

SimTech - Cluster of Excellence

October 2023 – November 2023 University of Stuttgart, Germany

Developed the Python package VINDy to perform data-driven modeling of dynamical systems with generative AI.

Visiting Research Scientist

Artificial Intelligence Institute in Dynamic Systems

October 2022 – June 2023 University of Washington, Seattle (US)

Developed ML algorithms for dimensionality reduction and system identification in complex, high-dimensional scenarios in engineering sciences.

Designed and constructed physics-informed models for Micro-Electrical Mechanical Systems (MEMS) devices. Application and validation on MEMS micromirrors and resonator devices.
Developed a multi-fidelity method to recover and predict high-quality solutions from multi-modal, low-fidelity data sources.

Exchange program

Applied Mathematics, Sorbonne University

September 2019 – July 2020 Paris, France

Study abroad coursework in the departments of Applied Mathematics and Sorbonne Polytech.

Professional Athlete

International Gymnastics Federation

January 2010 – December 2021 Bergamo, Italy

Member of the National Team of Aerobic Gymnastics from 2010 to 2021.
World medallist and European champion.
Experiences in coaching and coreographing in Italy, France, Finland, Hungary, Lithuania and United States.