Preprints

• Measuring Robustness in Deep Learning Based Compressive Sensing
  M. Zalbagi Darestani, Akshay Chaudhari, and R. Heckel, Feb. 2021, CODE.

• Accelerated MRI with Un-trained Neural Networks
  M. Zalbagi Darestani and R. Heckel, Jul. 2020, CODE.

• Image recognition from raw labels collected without annotators
  F. F. Yilmaz and R. Heckel, Oct. 2019, Feb. 2020, CODE.

Journal Papers

• A provably convergent scheme for compressive sensing under random generative priors
  W. Huang, P. Hand, R. Heckel, V. Voroninski
  Journal of Fourier Analysis and Applications, 2021.

• DNA-Based Storage: Models and Fundamental Limits
  I. Shomorony and R. Heckel
  IEEE Transactions on Information Theory, 2021.

• Low cost DNA data storage using photolithographic synthesis and advanced information reconstruction and error correction
  P. L. Antkowiak, J. Lietard, M. Z. Darestani, M. M. Somoza, W. J. Stark, R. Heckel & R. N. Grass
  Nature Communications, 2020.

• Rate-Optimal Denoising with Deep Neural Networks
  R. Heckel, W. Huang, P. Hand, and V. Voroninski
  Information and Inference: A Journal of the IMA, 2020, CODE.

• Deep Phase Decoder: Self-calibrating phase microscopy with an untrained deep neural network
  E. Bostan, R. Heckel, M. Chen, M. Kellman, L. Waller
  Optica, 2020.

• Genomic encryption of digital data stored in synthetic DNA
  R. N. Grass, R. Heckel, C. Dessimoz, and W. J. Stark
  Angewandte Chemie International Edition, 2020.

• Reading and writing digital data in DNA
  L. Meiser, P. Antkowiak, J. Koch, W. Chen, A. X. Kohll, W. J. Stark, R. Heckel, R. N. Grass
  Nature Protocols, 2019, Featured on the cover

• A characterization of the DNA data storage channel
  R. Heckel, G. Mikutis, and R. N. Grass
  Scientific Reports, 2019.

• Combining Data Longevity with High Storage Capacity—Layer‐by‐Layer DNA Encapsulated in Magnetic Nanoparticles
  W. D. Chen, A. X. Kohll, B. H. Nguyen, J. Koch, R. Heckel, W. J. Stark, L. Ceze, K. Strauss, R. N. Grass
  Advanced Functional Materials, 2019.

• Active ranking from pairwise comparisons and when parametric assumptions don’t help
  R. Heckel, N. B. Shah, K. Ramchandran, and M. J. Wainwright
  Annals of Statistics, 2019 CODE.

• An archive written in DNA
  R. Heckel
  Nature Biotechnology 2018.

• DiffuserCam: Lensless single-exposure 3D imaging
  N. Antipa, G. Kuo, R. Heckel, B. Mildenhall, E. Bostan, R. Ng, and L. Waller
  Optica, 2018.

• Generalized line spectral estimation via convex optimization
  R. Heckel and M. Soltanolkotabi
  IEEE Transactions on Information Theory, 2018.

• Addressing interpretability and cold-start in matrix factorization for recommender systems
  M. Vlachos, C. Duenner, R. Heckel, V. Vassiliadis, T. Parnell, K. Atasu
  IEEE Transactions on Knowledge and Data Engineering, 2018.

• Dimensionality-reduced subspace clustering
  R. Heckel, M. Tschannen, and H. Bölcskei
  Information and Inference: A Journal of the IMA, 2017, CODE

• Super-resolution radar
  R. Heckel, V. I. Morgenshtern, M. Soltanolkotabi
  Information and Inference: A Journal of the IMA, 2016, CODE.

• Robust subspace clustering via thresholding
  R. Heckel and H. Bölcskei
  IEEE Transactions on Information Theory, 2015.

• Detecting and number counting of single engineered nanoparticles by digital particle polymerase chain reaction
  D. Paunescu, C. A. Mora, L. Querci, R. Heckel, M. Puddu, B. Hattendorf, D. Günther, and R. N. Grass
  ACS Nano, 2015.

• Robust chemical preservation of digital information on DNA in silica with error-correcting codes
  R. N. Grass, R. Heckel, M. Puddu, D. Paunescu, and W. J. Stark
  Angewandte Chemie International Edition, 2015.
  Featured on the titlepage of Angewandte Chemie International Edition, as a research highlight in Nature, and received press coverage by CNN, NewScientist, Spektrum der Wissenschaft, the German-language edition of Scientific American, Neue Züricher Zeitung, and Tagesanzeiger, amongst others.

• Identification of sparse linear operators
  R. Heckel and H. Bölcskei
  IEEE Transactions on Information Theory, 2013.

• Bounds on the Average Sensitivity of Nested Canalizing Functions
  J. Klotz, R. Heckel, and S. Schober
  PLOS one, 2013.

• Harmonic analysis of Boolean networks: determinative power and perturbations
  R. Heckel, S. Schober, and M. Bossert
  EURASIP Journal on Bioinformatics and Systems Biology, 2013.

• Detecting controlling nodes of Boolean regulatory networks
  S. Schober, D. Kracht, R. Heckel, and M. Bossert
  EURASIP Journal on Bioinformatics and Systems Biology, 2011.

Long papers in selective confereneces

• Early Stopping in Deep Networks: Double Descent and How to Eliminate it
  R. Heckel and F. F. Yilmaz
  ICLR 2021 (International Conference on Learning Representations) CODE.

• Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation
  R. Heckel and M. Soltanolkotabi
  ICML 2020 (International Conference on Machine Learning) CODE

• Denoising and regularization via exploiting the structural bias of convolutional generators
  R. Heckel and M. Soltanolkotabi
  ICLR 2020 (International Conference on Learning Representations) CODE.

• Deep decoder: Concise image representations from untrained non-convolutional networks
  R. Heckel and P. Hand
  ICLR 2019 (International Conference on Learning Representations) CODE

• Adaptive Estimation for Approximate k-Nearest-Neighbor Computations
  D. LeJeune, R. Baraniuk, R. Heckel
  AISTATS 2019 (International Conference on Artificial Intelligence and Statistics)

• Approximate ranking from pairwise comparisons
  R. Heckel, M. Simchowitz, K. Ramchandran, and M. J. Wainwright
  AISTATS 2018 (International Conference on Artificial Intelligence and Statistics) CODE

• The sample complexity of online one-class collaborative filtering
  R. Heckel and K. Ramchandran
  ICML 2017 (International Conference on Machine Learning)

• Scalable and interpretable product recommendations via overlapping co-clustering
  R. Heckel, M. Vlachos, T. Parnell, and C. Dünner
  ICDE 2017 (IEEE International Conference on Data Engineering).

• Private and right-protected big data publication: An analysis
  R. Heckel and M. Vlachos
  SDM 2017 (SIAM International Conference on Data Mining)

Conference Papers

• Capacity of the Erasure Shuffling Channel
  S. Shin, R. Heckel, I. Shomorony
  ICASSP 2020 (IEEE International Conference on Acoustics, Speech, and Signal Processing)

• Regularizing linear inverse problems with convolutional neural networks
  R. Heckel
  NeurIPS 2019 Medical Imaging Workshop

• Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients
  Z. Dai and R. Heckel
  ICML 2019 Deep Phenomena Workshop

• Capacity results for the noisy shuffling channel
  I. Shomorony and R. Heckel
  ISIT 2019 (IEEE International Symposium on Information Theory)

• A fast and robust paradigm for Fourier compressed sensing based on coded sampling
  F. Ong, R. Heckel, and K. Ramchandran
  ICASSP 2019 (IEEE International Conference on Acoustics, Speech, and Signal Processing)

• Fundamental limits of DNA storage systems
  R. Heckel^, I. Shomorony^, K. Ramchandran, and D. Tse
  ISIT 2017 (IEEE International Symposium on Information Theory) (^=equal contribution)

• Super-resolution MIMO radar
  R. Heckel
  ISIT 2016 (IEEE International Symposium on Information Theory) CODE.

• Generalized line spectral estimation for radar and localization
  R. Heckel and M. Soltanolkotabi
  CoSeRa 2016 (Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing)

• Subspace clustering of dimensionality-reduced data
  R. Heckel, M. Tschannen, and H. Bölcskei
  ISIT 2014 (IEEE International Symposium on Information Theory)

• Compressive nonparametric graphical model selection for time series
  A. Jung, R. Heckel, H. Bölcskei, and F. Hlawatsch
  ICASSP 2014 (IEEE International Conference on Acoustics, Speech, and Signal Processing)

• Neighborhood selection for thresholding-based subspace clustering
  R. Heckel, E. Agustsson, and H. Bölcskei
  ICASSP 2014 (IEEE International Conference on Acoustics, Speech, and Signal Processing)

• Noisy subspace clustering via thresholding
  R. Heckel and H. Bölcskei
  ISIT 2013 (IEEE International Symposium on Information Theory)

• Subspace clustering via thresholding and spectral clustering
  R. Heckel and H. Bölcskei
  ICASSP 2013 (IEEE International Conference on Acoustics, Speech, and Signal Processing)

• Joint sparsity with different measurement matrices
  R. Heckel and H. Bölcskei
  Allerton 2012 (Allerton Conference on Communication, Control, and Computing)

• Determinative power and perturbations in Boolean networks
  R. Heckel, S. Schober, and M. Bossert
  WCSB 2012 (9th International Workshop on Computational Systems Biology) (best student paper award).

• Compressive identification of linear operators
  R. Heckel and H. Bölcskei
  ISIT 2011 (IEEE International Symposium on Information Theory)

• Spectral properties of a Boolean model of the E.coli genetic network and its implication on network inference
  S. Schober, R. Heckel, and D. Kracht
  WCSB 2010 (7th International Workshop on Computational Systems Biology)

• On random Boolean threshold networks
  R. Heckel, S. Schober, and M. Bossert
  SCC 2010 (International ITG Conference on Source and Channel Coding)

• A Boolean genetic regulatory network created by whole genome duplication
  R. Heckel and S. Schober
  WCSB 2009 (6th International Workshop on Computational Systems Biology)

Patents

• Method and system for identifying dependent components
  R. Heckel, V. Vasileiadis, and M. Vlachos, US 14/935476, filed Nov. 2015. - The obfuscation and protection of data rights
  R. Heckel and M. Vlachos, US 14/805514, filed July 2015.

Book chapters

• Super-resolution radar imaging via convex optimization
  R. Heckel, Chapter in ``Compressed sensing-based radar signal processing’’, 2018.

Thesis

• Sparse Signal Processing: Subspace Clustering and System Identification
  R. Heckel, PhD thesis, ETH Zurich, 2014, (awarded with the ETH Medal)