(April 2017) New paper to appear in the Proceedings of the IEEE

Prof. Lara Dolecek’s comprehensive survey on channel coding methods for non-volatile memories will appear in an upcoming issue of the Proceedings of the IEEE. Paper is

L. Dolecek and Y. Cassuto, “Coding for non-volatile memories: recent advances and new opportunities”.


Proceedings of the IEEE is  the most highly-cited general interest journal in electrical engineering and computer science: link here


(March 2017) Ahmed and Shahroze present at NVMW

We had 2 oral presentations at the Non-Volatile Memories Workshop:

Ahmed Hareedy presented the work on “Non-Binary LDPC Code Optimization for Modern Storage Systems,” by A. Hareedy, C. Lanka, and L. Dolecek.

Shahroze Kabir presented the work on “Modeling the effects of radiation induced soft errors on LDPC decoding,” by F. Sala, C. Schoeny, S. Kabir, D. Divsalar, and L. Dolecek.

(November 2016) 2 papers as Asilomar; one on noisy decoding in outer space, one on spatially coupled codes in HDDs

LORIS has two papers at Asilomar Conference on Signals, Systems, and Computers. The first paper is an invited contribution on statistical inference on noisy hardware. It discusses LDPC decoders in Flash memories used in high radiation environments. The second paper is on combinatorial characterization of finite length spatially coupled codes with applications to magnetic recording applications.


  1. F. Sala, C. Schoeny, S. Kabir, D. Divsalar, and L. Dolecek, “Flash Memories in High Radiation Environments: LDPC Decoder Study,” in Proc. IEEE Asilomar Conference on Signals, Systems, and Computers, Nov. 2016 (invited).
  2. H. Esfahanizadeh, A. Hareedy, and L. Dolecek, “Spatially Coupled LDPC Codes Optimized for 1-D Magnetic Recording Channels,” in Proc. IEEE Asilomar Conference on Signals, Systems, and Computers, Nov. 2016.

(Oct 2016) New paper at Allerton conference on a novel use of channel coding methods in machine learning applications

In collaboration with Prof. Van den Broeck (CS, UCLA), we have an exciting new research on using coding methods to make ML algorithms more robust. Initial results from this work were presented at 2016 Allerton Conference.


Robust Channel Coding Strategies for Machine Learning Data (I)
Mazooji, Kayvon UCLA
Sala, Frederic UCLA
Van den Broeck, Guy UCLA
Dolecek, Lara Univ. of California, Los Angeles
Keywords: Coding TheoryData AnalyticsMachine Learning and Learning Theory
Abstract: Two important recent trends are the proliferation of learning algorithms along with the massive increase of data stored on unreliable storage mediums. These trends impact each other; noisy data can have an undesirable effect on the results provided by learning algorithms. Although traditional tools exist to improve the reliability of data storage devices, these tools operate at a different abstraction level and therefore ignore the data application, leading to an inefficient use of resources. In this paper we propose taking the operation of learning algorithms into account when deciding how to best protect data. Specifically, we examine several learning algorithms that operate on data that is stored on noisy mediums and protected by error-correcting codes with a limited budget of redundancy; we develop a principled way to allocate resources so that the harm on the output of the learning algorithm is minimized.