Physics-Informed Vision and Imaging

Our members

Physics-Informed Vision and Imaging is a research group focused on advancing the science of imaging through novel sensors, physics-aware computation, and machine learning. We push the limits of what can be seen — revealing new insights about our world and the cosmos.

Research Focus

Observational Tools

Instruments

Imaging systems across radio, optical, and time-resolved sensing.

Algorithms & Theory

Computation

Physics-informed machine learning and neural rendering for inverse problems, differentiable physics, dynamic inference, and scientific simulation.

Application Domains

Science

From particles to planets to black holes — discovery across all scales.

Recent Publications

View all publications

Revealing Fine Structure in Protoplanetary Disks with Physics Constrained Neural Fields

arXiv 2025 Levis, Luong et al.

Neural Dynamic Modes: Computational Imaging of Dynamical Systems from Sparse Observations

arXiv 2025 SaraerToosi, Tu et al.

3D Volumetric Tomography of Clouds Using Machine Learning for Climate Analysis

Scientific Reports 2025 Ronen et al.

Revealing the 3D Cosmic Web through Gravitationally Constrained Neural Fields

ICLR 2025 Zhao, Levis et al.

Effective Resistivity in Relativistic Reconnection: A Prescription Based on Fully Kinetic Simulations

The Astrophysical Journal Letters 2025 Moran et al.

Exoplanet Imaging via Differentiable Rendering

IEEE Trans. on Comp. Imaging 2025 Feng et al.

News & Highlights

Pinned highlights

Pinned Oct 10, 2025

Aviad Levis received a $200K award from the CFI John R. Evans Leaders Fund (JELF) to spearhead the project Physics-constrained neural fields for scientific imaging. Read more from U of T: 55 U of T scholars receive research infrastructure support.

Pinned Sep 1, 2025

Rohan Dahale and Kevin McKinnon were selected for the 2025 cohort of the Schmidt AI in Science Fellowship. Their fellowships kick off this September, supporting bold AI-driven approaches to scientific discovery.

Pinned Mar 3, 2025

Ontario’s Research Fund is investing $2M in Aviad Levis and collaborators to develop new physics-informed imaging methods that demystify black holes. Learn more in the CS department feature, “$2M from Ontario Research Fund to help scientists demystify black holes.”

Date	News
Sep 20, 2025	Supported by the Data Sciences Institute, Aviad Levis is co-leading Bridging the Gap: From Computational Physics, to Physics-informed Machine Learning, to Data-driven Scientific Discovery. The DSI Emerging Program unites four co-leads and 16 members across nine departments to connect first-principles simulations with data-driven discovery for high-impact domains—from sustainable design to astrophysics.
Jul 10, 2025	Aviad Levis received the 2024–25 Connaught New Researcher Award, supporting high-impact directions in physics-informed vision and imaging.
May 15, 2025	David Bromley was awarded the Ontario Graduate Scholarship (OGS) for 2025–2026, recognizing his outstanding research contributions.
Oct 16, 2024	U of T highlighted Aviad Levis in “In awarding Nobel Prize, committee cites collaborative black hole research including U of T computer scientist Aviad Levis,” recognizing his Event Horizon Telescope contributions to the discoveries celebrated by the Nobel Committee.
Mar 28, 2024	The Natural Sciences and Engineering Research Council of Canada awarded Aviad Levis an NSERC Discovery Grant to launch his research program in scientific imaging and physics-informed computation.