91 citations · 122 across the 8 of their papers we have counts for
13 papers
Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space
Jason Qin, Hans-Hermann Wessels, Carlos Fernandez-Granda +1
The advancement of novel combinatorial CRISPR screening technologies enables the identification of synergistic gene combinations on a large scale. This is crucial for developing no…
Sweetwater: An interpretable and adaptive autoencoder for efficient tissue deconvolution
Jesus de la Fuente, Naroa Legarra, Guillermo Serrano +8
Single-cell RNA-sequencing (scRNA-seq) stands as a powerful tool for deciphering cellular heterogeneity and exploring gene expression profiles at high resolution. However, its high…
Adaptive Test Allocation for Outbreak Detection and Tracking in Social Contact Networks
Pau Batlle, Joan Bruna, Carlos Fernandez-Granda +1
We present a general framework for adaptive allocation of viral tests in social contact networks. We pose and solve several complementary problems. First, we consider the design of…
A Sampling Theorem for Deconvolution in Two Dimensions
Joseph McDonald, Brett Bernstein, Carlos Fernandez-Granda
This work studies the problem of estimating a two-dimensional superposition of point sources or spikes from samples of their convolution with a Gaussian kernel. Our results show th…
Data-driven Estimation of Sinusoid Frequencies
Gautier Izacard, Sreyas Mohan, Carlos Fernandez-Granda
Frequency estimation is a fundamental problem in signal processing, with applications in radar imaging, underwater acoustics, seismic imaging, and spectroscopy. The goal is to esti…
Sparse Recovery Beyond Compressed Sensing: Separable Nonlinear Inverse Problems
Brett Bernstein, Sheng Liu, Chrysa Papadaniil +1
Extracting information from nonlinear measurements is a fundamental challenge in data analysis. In this work, we consider separable inverse problems, where the data are modeled as…
A Learning-Based Framework for Line-Spectra Super-resolution
Gautier Izacard, Brett Bernstein, Carlos Fernandez-Granda
We propose a learning-based approach for estimating the spectrum of a multisinusoidal signal from a finite number of samples. A neural-network is trained to approximate the spectra…
Deconvolution of Point Sources: A Sampling Theorem and Robustness Guarantees
Brett Bernstein, Carlos Fernandez-Granda
In this work we analyze a convex-programming method for estimating superpositions of point sources or spikes from nonuniform samples of their convolution with a known kernel. We co…
Demixing Sines and Spikes: Robust Spectral Super-resolution in the Presence of Outliers
Carlos Fernandez-Granda, Gongguo Tang, Xiaodong Wang +1
We consider the problem of super-resolving the line spectrum of a multisinusoidal signal from a finite number of samples, some of which may be completely corrupted. Measurements of…
Super-Resolution of Point Sources via Convex Programming
Carlos Fernandez-Granda
We consider the problem of recovering a signal consisting of a superposition of point sources from low-resolution data with a cut-off frequency f. If the distance between the sourc…
Support detection in super-resolution
Carlos Fernandez-Granda
We study the problem of super-resolving a superposition of point sources from noisy low-pass data with a cut-off frequency f. Solving a tractable convex program is shown to locate…
Super-Resolution from Noisy Data
Emmanuel Candes, Carlos Fernandez-Granda
This paper studies the recovery of a superposition of point sources from noisy bandlimited data. In the fewest possible words, we only have information about the spectrum of an obj…
Towards a Mathematical Theory of Super-Resolution
Emmanuel Candes, Carlos Fernandez-Granda
This paper develops a mathematical theory of super-resolution. Broadly speaking, super-resolution is the problem of recovering the fine details of an object---the high end of its s…