Mathematics of
data and imaging
I study the mathematical foundations of recovering information from noisy, incomplete, and transformed data.
I am an Associate Professor in the Faculty of Electrical and Computer Engineering at the Technion – Israel Institute of Technology.
Research interests
Mathematics of data science · Signal processing · Optimization · Statistics · Cryo-electron microscopy · Phase retrieval · Super-resolution
From information to reconstruction
Questions that guide
our research
Our work connects applied mathematics, statistics, and computation through questions like these.
What can be learned when the signal is almost completely buried in noise?
Which features of an unknown object survive random transformations, projections, or the loss of phase?
When do statistically optimal estimators admit efficient algorithms, and when is computation the fundamental bottleneck?
Can we reconstruct complex structures directly from raw data, without first detecting or aligning their individual occurrences?
Why do standard learning algorithms discover convincing but false structure in weak-signal regimes, and how can mathematics prevent it?
For prospective students
Enjoy thinking about questions like these?
I am looking for excellent research students to join my group. If you enjoy mathematical questions and want to work across theory, algorithms, and imaging, I would be glad to hear from you.
For a starting point, read our overview of the mathematical theory and computational challenges of cryo-EM.
Further reading
Selected papers
- A transversality theorem for semi-algebraic sets with application to signal recovery from the second moment and cryo-EM
- Object detection under the linear subspace model with application to cryo-EM images
- Autocorrelation analysis for cryo-EM with sparsity constraints: Improved sample complexity and projection-based algorithms
- Algebraic theory of phase retrieval
- Single-particle cryo-electron microscopy: Mathematical theory, computational challenges, and opportunities
- Fourier Phase Retrieval: Uniqueness and Algorithms