Reinhard Heckel
Reinhard Heckel
Technical University of Munich and Rice University
Verified email at - Homepage
Cited by
Cited by
Robust chemical preservation of digital information on DNA in silica with error‐correcting codes
RN Grass, R Heckel, M Puddu, D Paunescu, WJ Stark
Angewandte Chemie International Edition 54 (8), 2552-2555, 2015
DiffuserCam: lensless single-exposure 3D imaging
N Antipa, G Kuo, R Heckel, B Mildenhall, E Bostan, R Ng, L Waller
Optica 5 (1), 1-9, 2018
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks
R Heckel, P Hand
International Conference on Learning Representations (ICLR), 2019
A characterization of the DNA data storage channel
R Heckel, G Mikutis, RN Grass
Scientific reports 9 (1), 9663, 2019
Robust subspace clustering via thresholding
R Heckel, H Bölcskei
IEEE Transactions on Information Theory 61 (11), 6320-6342, 2015
Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network
E Bostan, R Heckel, M Chen, M Kellman, L Waller
Optica 7 (6), 559-562, 2020
Active Ranking from Pairwise Comparisons and when Parametric Assumptions Don't Help
R Heckel, NB Shah, K Ramchandran, MJ Wainwright
The Annals of Statistics, 2019
Fundamental Limits of DNA Storage Systems
R Heckel, I Shomorony, K Ramchandran, DNC Tse
IEEE International Symposium on Information Theory (ISIT), 2017
Reading and writing digital data in DNA
LC Meiser, PL Antkowiak, J Koch, WD Chen, AX Kohll, WJ Stark, ...
Nature protocols 15 (1), 86-101, 2020
Accelerated MRI with un-trained neural networks
MZ Darestani, R Heckel
IEEE Transactions on Computational Imaging 7, 724-733, 2021
Super-resolution radar
R Heckel, VI Morgenshtern, M Soltanolkotabi
Information and Inference: A Journal of the IMA 5 (1), 22-75, 2016
Low cost DNA data storage using photolithographic synthesis and advanced information reconstruction and error correction
PL Antkowiak, J Lietard, MZ Darestani, MM Somoza, WJ Stark, R Heckel, ...
Nature communications 11 (1), 5345, 2020
Unsupervised learning with Stein's unbiased risk estimator
CA Metzler, A Mousavi, R Heckel, RG Baraniuk
arXiv preprint arXiv:1805.10531, 2018
Combining data longevity with high storage capacity—layer‐by‐layer DNA encapsulated in magnetic nanoparticles
WD Chen, AX Kohll, BH Nguyen, J Koch, R Heckel, WJ Stark, L Ceze, ...
Advanced Functional Materials 29 (28), 1901672, 2019
Measuring Robustness in Deep Learning Based Compressive Sensing
M Zalbagi Darestani, A Chaudhari, R Heckel
International Conference on Machine Learning (ICML), 2021
Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators
R Heckel, M Soltanolkotabi
International Conference on Learning Representations (ICLR), 2020
Scalable and interpretable product recommendations via overlapping co-clustering
R Heckel, M Vlachos, T Parnell, C Dünner
IEEE International Conference on Data Engineering (ICDE), 1033-1044, 2017
Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation
R Heckel, M Soltanolkotabi
International Conference on Machine Learning (ICML), 2020
Emerging approaches to DNA data storage: Challenges and prospects
A Doricchi, CM Platnich, A Gimpel, F Horn, M Earle, G Lanzavecchia, ...
ACS nano 16 (11), 17552-17571, 2022
Super-resolution radar imaging via convex optimization
R Heckel
Chapter in: Compressed Sensing in Radar Signal Processing, 193, 2019
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