Since October 2016, I am a Rudolf Mößbauer Tenure Track Assistant Professor at the Technical University of Munich (TUM) and a Fellow at the TUM Institute for Advanced Study (TUM IAS). I am heading the "Coding for Communications and Data Storage" (COD) group.

From November 2013 to August 2016, I was a Postdoctoral Researcher at the Computer Science Department of the Technion---Israel Institute of Technology in the group of Tuvi Etzion, Ronny Roth, and Eitan Yaakobi.

From October 2009 to October 2013, I was a PhD student and teaching assistant under the co-supervision of Martin Bossert at the Institute of Communications Engineering, Ulm University, Germany, and Pierre Loidreau at the Institut de Recherche Mathématique de Rennes (IRMAR), Université de Rennes 1, Rennes, France. You can download my PhD thesis "Decoding of Block and Convolutional Codes in Rank Metric" here.

My main research interests are coding theory, post-quantum cryptography (code- and lattice-based), efficient algorithms, security, and applying error-correcting codes to communications, security, and storage in general, in particular to DNA storage, network coding, non-volatile memories, distributed data storage, and physical unclonable functions.

Selected Grants and Awards

• NVMW Memorable Paper Award 2019
• ERC Starting Grant 2018 (ERC Press Release | TUM Press Release)
• DFG Heinz Maier-Leibnitz-Preis 2018, (DFG Press Release | TUM Press Release)
• DFG Emmy Noether Program (2016)
• Marie Skłodowska-Curie Individual Fellowship, Horizon 2020, European Commission (2015--2016)
• Minerva Postdoctoral Fellowship(2013--2015)
• Dissertation Award of "Ulmer Universitätsgesellschaft" (2013)
• 2nd Prix de Thèse, Édition 2013, Fondation Rennes 1 (2013)

(Co-)Organized Workshops

• Munich Workshop on Coding and Cryptography 2019 (MWCC 2019)
• Dagstuhl Seminar 18511 - Algebraic Coding Theory for Networks, Storage, and Security 2018
• Munich Workshop on Coding and Cryptography 2018 (MWCC 2018)
• Munich Workshop on Coding and Applications 2017 (MWCA 2017)

Lectures

• Nachrichtentechnik (Mandatory module for Bachelor students), Summer 2020
• Channel Coding (Core module for Master students), Winters 2016/17, 2017/18, 2019/20, Summer 2020
• Coding Theory for Storage and Networks (Elective module for Master students), Summers 2017, 2018, 2019
• Security in Communications and Storage (Elective module for Master students), Winters 2017/18, 2019/20

Supervision of PhD Students

• Sabine Pircher (with Hensoldt Cyber), Post-quantum cryptography, since 02/2020
• Marvin Xhemrishi, Coded computation, since 01/2020
• Thomas Jerkovits (with DLR), Post-quantum cryptography
• Lorenz Welter, Insertions/deletions & DNA storage, since 03/2019
• Hedongliang Liu, Coding for storage and networks, since 03/2019
• Georg Maringer, Post-quantum cryptography, since 12/2018
• Haider Al Kim, Coding for memories, since 10/2018
• Julian Renner, Code-based cryptography, since 11/2017
• Lukas Holzbaur, Coding for distributed data storage & Private information retrieval, since 03/2017
• Andreas Lenz, Insertions/deletions & coding for DNA storage, since 12/2016

Postdocs in my Group

• Rawad Bitar, since 02/2020
• Alessandro Neri, since 01/2020
• Nikita Polianskii, since 11/2019
• Sven Puchinger, 05/2018--09/2019, now postdoc at DTU
• Ragnar Freij-Hollanti, 11/2017--08/2018, now lecturer at Aalto University

Post-Quantum Cryptography

The recent developments of a quantum supercomputers has motivated research on post-quantum cryptography. Assuming an attack of a sufficiently large quantum computer, several classical public-key algorithms as RSA and elliptic curve cryptography become insecure since computationally intensive mathematical problems become easy-to-solve. It is therefore necessary to design techniques which are secure against an attack of a quantum computer. One approach to achieve this security is code-based cryptography, where encryption and decryption is based on encoding and decoding an algebraic code; another one is based on hard problems in lattices. We investigate and improve post-quantum cryptographic schemes.

• LIGA: A Cryptosystem Based on the Hardness of Rank-Metric List and Interleaved Decoding
• Interleaving Loidreau's Rank-Metric Cryptosystem
• Randomized Decoding of Gabidulin Codes Beyond the Unique Decoding Radius
• A Cryptosystem based on Interleaved Goppa Codes
• A Rank-Metric Based Cryptosystem of Small Key Size
• Twisted Gabidulin Codes in the McEliece System

Coding for Insertions/Deletions and DNA Storage

The Levenshtein distance, also known as the edit distance, is a measure of similarity between two strings evaluated based on the minimum number of insertion/deletion operations required to transform one string into the other. Codes for correcting insertions and deletions were first proposed for communication applications in the presence of synchronization or sampling errors. However, insertions, deletions, and duplications have particular importance also for numerous applications in bioinformatics such as DNA storage. DNA storage is recently attracting attention as a dense and robust concept for archival storage. The data is written on DNA strands by synthesis and read by DNA sequencing. DNA storage has to cope with sequence losses, loss of ordering of the sequences, and symbol errors such as insertions, deletions and subtitutions.

• Coding for Efficient DNA Synthesis
• Criss-Cross Deletion Correcting Codes
• Single-Deletion Single-Insertion Correcting Codes
• Capacity of the DNA Storage Channel
• Coding over Sets for DNA Storage
• Clustering-Correcting Codes
• Duplication-Correcting Codes
• List Decoding of Insertions and Deletions
• Codes Correcting a Burst of Deletions or Insertions

Coding for Storage

Data storage media like flash memories (used in USB flash drives or solid state drives) suffer from manufacturing imperfections, wearout, and fluctuating read/write errors. In distributed data storage (necessary for cloud storage systems), the most common problem are failures of servers and the task is to reconstruct the lost data efficiently. It is therefore necessary to design coding solutions tailored to these storage applications.

• Secrecy and Accessibility in Distributed Storage
• Partial MDS Codes with Local Regeneration
• List Decoding of Locally Repairable Codes
• Codes for Partially Stuck-at Memory Cells
• Masking Trapped Charge in Flash Memories
• Erasure List Decoding of Locally Repairable Codes

 Network Coding and Rank-Metric Codes

The principle of network coding has been attracting growing attention in the recent years as a technique to disseminate information in data networks. The reason for this interest is that network coding achieves higher throughput than routing. In routing, the packets are simply forwarded at each node of the network, whereas in network coding, the nodes perform linear combinations of all packets received so far. Fundamental questions in the area of network coding include assigning appropriate linear combinations to the nodes and choosing a suitable error-correcting code for the case when erroneous packets occur or packets are lost. Rank-metric codes are thereby of relevance, not only for network coding but also for space time codes and code-based cryptography.

• Low-Rank Parity-Check Codes over the Ring of Integers Modulo a Prime Power
• Randomized Decoding of Gabidulin Codes Beyond the Unique Decoding Radius
• Scalar and Vector Solutions of Generalized Combination Networks
• Vector Network Coding Outperforms Scalar Linear Network Coding
• Optimal Ferrers Diagram Rank-Metric Codes
• Convolutional Codes in Rank Metric with Application to Random Network Coding
• Decoding High-Order Interleaved Rank-Metric Codes

List Decoding

List decoding is a major technique for increasing the error correcting capability of codes. The basic idea of list decoding is to return not only a unique decoding result (which is only possible up to half the minimum distance of the corresponding code), but to return all codewords in a certain radius around the received word. This enables us to decode more errors at the cost of a usually small list. Practically, list decoding is used in concatenated coding schemes. Apart from explicit list decoding algorithms, a fundamental question is whether list decoding in a certain metric up to a certain radius can be done in polynomial time.

• List Decoding of Locally Repairable Codes
• List Decoding of Insertions and Deletions
• List Decoding of Crisscross Errors
• Some Gabidulin Codes Cannot be List Decoded Efficiently at any Radius
• Bounds on List Decoding of Rank-Metric Codes
• Interpolation-Based Decoding of Interleaved Gabidulin Codes
• Efficient Interpolation-Based Decoding of Interleaved Subspace Codes

Private Information Retrieval and Private Computation

Private information retrieval (PIR) studies the problem when a user wants to retrieve a file from a storage system without revealing the identity of the file in question to the storage servers. Private information retrieval (PIR) studies the problem when a user wants to retrieve a file from a storage system without revealing the identity of the file in question to the storage servers. The files that are stored on the servers might be coded (e.g., to prevent server failures) and some servers might be communicating with each other. We investigate PIR schemes that guarantee either information-theoretic privacy or computational privacy. We also investigate how distributed computations such as matrix multiplication can be done while guaranteeing privacy constraints.

• Rateless Codes for Private Distributed Matrix-Matrix Multiplication
• Computational Code-Based Single-Server Private Information Retrieval
• Private Information Retrieval over Networks
• Private Streaming with Convolutional Codes

Fast Algorithms

Fast or efficient algorithms for performing certain polynomial and matrix operations are important in several applications, including coding theory. Our studies include fast algorithms for decoding Gabidulin codes, including fast algorithms for performing certain operations with linearized polynomials.

• Fast Operations on Linearized Polynomials
• Sub-quadratic Decoding of Gabidulin Codes
• A Fast Linearized Euclidean Algorithm
• Fast Multi-Sequence Shift-Register Synthesis with the Euclidean Algorithm

Physical Unclonable Functions

A Physical Unclonable Function (PUF) is a digital circuit that possesses an intrinsic randomness resulting from process variations. This randomness is exploited to generate random keys for cryptographic applications which can be reproduced on demand. Thus, no embedded physically secure non-volatile memory is needed. However, the regeneration of a key is not perfect due to environmental factors such as temperature variations and aging effects of the digital circuit. These variations can be seen as an erroneous channel and therefore error-correcting codes increase the reliability of key regenerations.

• Timing Attack Resilient Decoding Algorithms for Physical Unclonable Functions
• Variable-Length Bit Mapping and Error-Correcting Codes for Higher-Order Alphabet PUFs

Coding for Communications, Reed--Solomon, Cyclic Codes

In communications, error-correcting codes are an indispensable means to cope with errors which happen during the transmission. This includes classical error-correcting codes like Reed--Solomon and BCH codes, but also e.g., staircase codes which are used in optical communication systems.

• Improved Decoding of Staircase Codes for FEC in Optical Communications 
• Decoding Interleaved Reed-Solomon Codes
• Unambiguous Decoding of Reed-Solomon Codes beyond Half the Minimum Distance
• Generalizing Bounds on the Minimum Distance of Cyclic Codes
• Decoding Cyclic Codes up to a New Bound on the Minimum Distance

Convolutional Codes

• Convolutional Codes in Rank Metric with Application to Random Network Coding
• On (Partial) Unit Memory Codes Based on Gabidulin Codes
• Decoding of Partial Unit Memory Codes of Arbitrary Rate

Journal Publications

[29] H. Liu, N. Polianskii, I. Vorobyev, and A. Wachter-Zeh, “Almost Affinely Disjoint Subspaces,” (submitted to) Finite Fields and Their Applications, 2020.

[28] H. Liu, H. Wei, S. Puchinger, A. Wachter-Zeh, and M. Schwartz, “On the Gap between Scalar and Vector Solutions of Generalized Combination Networks,” (submitted to) IEEE Trans. Inform. Theory, 2020.

[27] J. Renner, S. Puchinger, and A. Wachter-Zeh, “LIGA: A Cryptosystem Based on the Hardness of Rank-Metric List and Interleaved Decoding,” (submitted to) Designs, Codes, and Cryptography, 2020.

[26] S. Puchinger, J. Renner, and A. Wachter-Zeh, “Decoding High-Order Interleaved Rank-Metric Codes,” (submitted to) IEEE Trans. Inform. Theory, 2019.

[25] L. Holzbaur, S. Puchinger, and A. Wachter-Zeh, “Error Decoding of Locally Repairable and Partial MDS Codes,” (in revision for) IEEE Trans. Inform. Theory, 2020.

[24] H. Cai, J. Chrisnata, T. Etzion, M. Schwartz, and A. Wachter-Zeh, “Network-Coding Solutions for Minimal Combination Networks and Their Subnetworks,” (accepted for) IEEE Trans. Inform. Theory, 2020.

[23] A. Lenz, P. Siegel, A. Wachter-Zeh, and E. Yaakobi, “Coding over Sets for DNA Storage,” IEEE Trans. Inform. Theory, vol. 66, no. 4, pp. 2331–2351, Apr. 2020.

[22] L. Holzbaur, R. Freij-Hollanti, A. Wachter-Zeh, and C. Hollanti, “Private Streaming with Convolutional Codes,” IEEE Trans. Inform. Theory, vol. 66, no. 4, pp. 2331–2351, Apr. 2020.

[21] R. Tajeddine, A. Wachter-Zeh, and C. Hollanti, “Private Information Retrieval over Random Linear Networks,” IEEE Trans. on Information Forensics and Security, vol. 15, pp. 790--799, Jul. 2019.

[20] V. Immler, M. Hiller, L. Qinzhi, A. Lenz, and A. Wachter-Zeh, “Variable-Length Bit Mapping and Error- Correcting Codes for Higher-Order Alphabet PUFs,” Journal of Hardware and Systems Security, vol. 3, pp. 78–93, Mar. 2019.

[19] L. Holzbaur, H. Bartz, and A. Wachter-Zeh, “Improved Decoding and Error Floor Analysis of Staircase Codes,” Designs, Codes and Cryptography, vol. 2-3, Jan. 2019.

[18] A. Lenz, A. Wachter-Zeh, and E. Yaakobi, “Duplication-Correcting Codes,” Designs, Codes and Cryptogra- phy, vol. 2-3, Jan. 2019.

[17] H. Bartz and A. Wachter-Zeh, “Efficient List Decoding of Interleaved Subspace and Gabidulin Codes Using Gröbner Bases,” Advances in Mathematics of Communications, vol. 12, no. 4, pp. 773–804, Nov. 2018.

[16] S. Puchinger and A. Wachter-Zeh, “Fast Operations on Linearized Polynomials and their Applications in Coding Theory,” Journal of Symbolic Computation, vol. 89, pp. 194–215, Nov. 2018.

[15] A. Wachter-Zeh, “List Decoding of Insertions and Deletions,” IEEE Trans. Inform. Theory, vol. 64, no. 9, pp. 6297–6304, Sep. 2018.

[14] T. Etzion and A. Wachter-Zeh, “Vector Network Coding based on Subspace Codes Outperforms Scalar Linear Network Coding,” IEEE Trans. Inform. Theory, vol. 64, no. 4, pp. 2460–2473, Apr. 2018.

[13] C. Schoeny, A. Wachter-Zeh, R. Gabrys, and E. Yaakobi, “Codes Correcting a Burst of Deletions or Insertions,” IEEE Trans. Inform. Theory, vol. 63, no. 4, pp. 1971–1985, Apr. 2017.

[12] A. Wachter-Zeh, “List Decoding of Crisscross Errors,” IEEE Trans. Inform. Theory, vol. 63, no. 1, pp. 142–149, Jan. 2017.

[11] N. Raviv and A. Wachter-Zeh, “Some Gabidulin Codes Cannot be List Decoded Efficiently at any Radius,” IEEE Trans. Inform. Theory, vol. 62, no. 4, pp. 1605–1615, Apr. 2016.

[10] T. Etzion, E. Gorla, A. Ravagnani, and A. Wachter-Zeh, “Optimal Ferrers Diagram Rank-Metric Codes,” IEEE Trans. Inform. Theory, vol. 62, no. 4, pp. 1616–1630, Apr. 2016. 1/6

[9] A. Wachter-Zeh and E. Yaakobi, “Codes for Partially Stuck-at Memory Cells,” IEEE Trans. Inform. Theory, vol. 62, no. 2, pp. 639–654, Feb. 2016.

[8] A. Wachter-Zeh, M. Stinner, and V. Sidorenko, “Convolutional Codes in Rank Metric with Application to Random Network Coding,” IEEE Trans. Inform. Theory, vol. 61, no. 6, pp. 3199–3213, Jun. 2015.

[7] A. Wachter-Zeh and A. Zeh, “List and Unique Error-Erasure Decoding of Interleaved Gabidulin Codes with Interpolation Techniques,” Des. Codes Cryptogr., vol. 73, no. 2, pp. 547–570, 2014.

[6] A. Wachter-Zeh, A. Zeh, and M. Bossert, “Decoding Interleaved Reed–Solomon Codes beyond their Joint Error-Correcting Capability,” Des. Codes Cryptogr., vol. 71, no. 2, pp. 261–281, Jul. 2014.

[5] A. Wachter-Zeh, “Bounds on List Decoding of Rank-Metric Codes,” IEEE Trans. Inform. Theory, vol. 59, no. 11, pp. 7268–7277, Nov. 2013.

[4] A. Wachter-Zeh, V. Afanassiev, and V. Sidorenko, “Fast Decoding of Gabidulin Codes,” Des. Codes Cryptogr., vol. 66, no. 1, pp. 57–73, Jan. 2013.

[3] A. Zeh, A. Wachter-Zeh, and S. Bezzateev, “Decoding Cyclic Codes up to a New Bound on the Minimum Distance,” IEEE Trans. Inform. Theory, vol. 58, no. 6, pp. 3951–3960, Jun. 2012.

[2] A. Zeh and A. Wachter, “Fast Multi-Sequence Shift-Register Synthesis with the Euclidean Algorithm,” Adv. Math. Commun., vol. 5, no. 4, pp. 667–680, Nov. 2011.

[1] A. Wachter, V. Sidorenko, M. Bossert, and V. Zyablov, “On (Partial) Unit Memory Codes Based on Gabidulin Codes,” Probl. Inf. Transm., vol. 47, no. 2, pp. 38–51, Jun. 2011.

Conference Publications

[57] R. Bitar, M. Xhemrishi, and A. Wachter-Zeh, “Rateless Codes for Private Distributed Matrix-Matrix Multiplication,” IEEE International Symposium on Information Theory and its Applications (ISITA), Oct. 2020, Hawaii, USA.

[56] R. Bitar, I. Smagloy, L. Welter, A. Wachter-Zeh, and E. Yaakobi, “Criss-Cross Deletion Correcting Codes,” IEEE International Symposium on Information Theory and its Applications (ISITA), Oct. 2020, Hawaii, USA.

[55] L. Holzbaur, S. Kruglik, A. Frolov, and A. Wachter-Zeh, “Secrecy and Accessibility in Distributed Storage,” (submitted to) IEEE Global Communications Conference (GLOBECOM), Dec. 2020, Taipei, Taiwan.

[54] L. Holzbaur, C. Hollanti, and A. Wachter-Zeh, “Computational Code-Based Single-Server Private Information Retrieval,” IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2020, Los Angeles, CA, USA.

[53] I. Smagloy, L. Welter, A. Wachter-Zeh, and E. Yaakobi, “Single-Deletion Single-Substitution Correcting Codes,” IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2020, Los Angeles, CA, USA.

[52] A. Lenz, Y. Liu, C. Rashtchian, P. Siegel, A. Wachter-Zeh, and E. Yaakobi, “Coding for Efficient DNA Synthesis,” IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2020, Los Angeles, CA, USA.

[51] H. Liu, H. Wei, S. Puchinger, A. Wachter-Zeh, and M. Schwartz, “On the Gap between Scalar and Vector Solutions of Generalized Combination Networks,” IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2020, Los Angeles, CA, USA.

[50] L. Holzbaur, S. Puchinger, E. Yaakobi, and A. Wachter-Zeh, “Partial MDS Codes with Local Regeneration,” IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2020, Los Angeles, CA, USA.

[49] J. Renner, S. Puchinger, A. Wachter-Zeh, C. Hollanti, and R. Freij-Hollanti, “Low-Rank Parity-Check Codes over the Ring of Integers Modulo a Prime Power,” IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2020, Los Angeles, CA, USA.

[48] J. Renner, T. Jerkovits, H. Bartz, S. Puchinger, P. Loidreau, and A. Wachter-Zeh, “Randomized Decoding of Gabidulin Codes Beyond the Unique Decoding Radius,” in International Conference on Post-Quantum Cryptography (PQCrypto), 2020, Paris, France.

[47] A. Lenz, P. Siegel, A. Wachter-Zeh, and E. Yaakobi, “Achieving the Capacity of the DNA Storage Channel (Invited Paper),” in International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2020, Barcelona, Spain.

[46] H. Al Kim, S. Puchinger, and A. Wachter-Zeh, “Error Correction for Partially Stuck Memory Cells,” in International Symposium on Problems of Redundancy in Information and Control Systems 2019, Oct. 2019, Moscow, Russia.

[45] J. Renner, S. Puchinger, and A. Wachter-Zeh, “Interleaving Loidreau’s Rank-Metric Cryptosystem,” in International Symposium on Problems of Redundancy in Information and Control Systems 2019, Oct. 2019, Moscow, Russia.

[44] L. Holzbaur, S. Puchinger, and A. Wachter-Zeh, “On Error Decoding of Locally Repairable and Partial MDS Codes,” in IEEE Information Theory Workshop (ITW), Aug. 2019, Visby, Sweden.

[43] A. Lenz, P. Siegel, A. Wachter-Zeh, and E. Yaakobi, “An Upper Bound on the Capacity of the DNA Storage Channel,” in IEEE Information Theory Workshop (ITW), Aug. 2019, Visby, Sweden.

[42] T. Shinkar, E. Yaakobi, A. Lenz, and A. Wachter-Zeh, “Clustering-Correcting Codes,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2019, Paris, France.

[41] A. Lenz, P. Siegel, A. Wachter-Zeh, and E. Yaakobi, “Anchor-Based Correction of Substitutions in Indexed Sets,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2019, Paris, France.

[40] L. Holzbaur, H. Liu, S. Puchinger, and A. Wachter-Zeh, “On Decoding and Applications of Interleaved Goppa Codes,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2019, Paris, France.

[39] H. Cai, T. Etzion, M. Schwartz, and A. Wachter-Zeh, “Network Coding Solutions for the Combination Network and its Subgraphs,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2019, Paris, France.

[38] A. Lenz, P. Siegel, A. Wachter-Zeh, and E. Yaakobi, “Coding over Sets for DNA Storage,” in Non-Volantile Memories Workshop (NVMW), Feb. 2019, San Diego, USA (NVMW Memorable Paper Award).

[37] L. Holzbaur, R. Freij-Hollanti, A. Wachter-Zeh, and C. Hollanti, “Private Streaming with Convolutional Codes,” in IEEE Information Theory Workshop (ITW), Nov. 2018, Guangzhou, China.

[36] S. Puchinger, J. Renner, and A. Wachter-Zeh, “Twisted Gabidulin Codes in the GPT Cryptosystem,” in Sixteenth International Workshop on Algebraic and Combinatorial Coding Theory (ACCT), Sep. 2018, Svetlogorsk, Russia. 2/6

[35] A. Lenz, N. Jünger, and A. Wachter-Zeh, “Bounds and Constructions for Multi-Symbol Duplication Error Correcting Codes,” in Sixteenth International Workshop on Algebraic and Combinatorial Coding Theory (ACCT), Sep. 2018, Svetlogorsk, Russia.

[34] H. Liu, L. Holzbaur, and A. Wachter-Zeh, “Locality in Crisscross Error Correction,” in Sixteenth In- ternational Workshop on Algebraic and Combinatorial Coding Theory (ACCT), Sep. 2018, Svetlogorsk, Russia.

[33] A. Wachter-Zeh, S. Puchinger, and J. Renner, “Repairing the Faure-Loidreau Public-Key Cryptosystem,” in IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2018, Vail, Colorado, USA.

[32] L. Holzbaur and A. Wachter-Zeh, “List Decoding of Locally Repairable Codes,” in IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2018, Vail, Colorado, USA.

[31] A. Lenz, P. Siegel, A. Wachter-Zeh, and E. Yaakobi, “Coding over Sets for DNA Storage,” in IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2018, Vail, Colorado, USA.

[30] V. Immler, M. Hiller, L. Qinzhi, A. Lenz, and A. Wachter-Zeh, “Variable-Length Bit Mapping and Error- Correcting Codes for Higher-Order Alphabet PUFs,” in Seventh International Conference on Security, Privacy, and Applied Cryptography Engineering (SPACE), Dec. 2017, Goa, India.

[29] L. Holzbaur, H. Bartz, and A. Wachter-Zeh, “Improved Decoding and Error Floor Analysis of Staircase Codes,” in International Workshop on Coding and Cryptography (WCC), Sep. 2017, St. Petersburg, Russia.

[28] A. Lenz, A. Wachter-Zeh, and E. Yaakobi, “Bounds on Codes Correcting Tandem and Palindromic Duplications,” in International Workshop on Coding and Cryptography (WCC), Sep. 2017, St. Petersburg, Russia.

[27] A. Wachter-Zeh, “Limits to List Decoding of Insertions and Deletions,” in IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2017, Aachen, Germany.

[26] V. Sidorenko, H. Bartz, and A. Wachter-Zeh, “Interleaved Subspace Codes in Fountain Mode,” in IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2017, Aachen, Germany.

[25] S. Puchinger, S. Müelich, M. Bossert, and A. Wachter-Zeh, “Timing Attack Resilient Decoding Algorithms for Physical Unclonable Functions,” in International ITG Conference on Systems, Communications and Coding (SCC), Feb. 2017, Hamburg, Germany.

[24] T. Etzion and A. Wachter-Zeh, “Vector Network Coding based on Subspace Codes Outperforms Scalar Linear Network Coding,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2016, Barcelona, Spain.

[23] S. Puchinger and A. Wachter-Zeh, “Sub-quadratic Decoding of Gabidulin Codes,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2016, Barcelona, Spain.

[22] C. Schoeny, A. Wachter-Zeh, R. Gabrys, and E. Yaakobi, “Codes Correcting a Burst of Deletions or Insertions,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2016, Barcelona, Spain.

[21] A. Zeh and A. Wachter-Zeh, “Improved Erasure List Decoding Locally Repairable Codes using Alphabet- Dependent List Recovery,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2016, Barcelona, Spain.

[20] A. Wachter-Zeh and E. Yaakobi, “Masking Trapped Charge in Flash Memories,” in 53nd Annual Allerton Conference on Communication, Control, and Computing, Oct. 2015, Monticello, IL, USA.

[19] N. Raviv and A. Wachter-Zeh, “Some Gabidulin Codes Cannot be List Decoded Efficiently at any Radius,” in IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2015, Hong Kong, China. 3/6

[18] A. Wachter-Zeh and E. Yaakobi, “Codes for Partially Stuck-at Memory Cells,” in Int. ITG Conf. on Systems, Communications and Coding (SCC), Feb. 2015, Hamburg, Germany.

[17] H. Bartz and A. Wachter-Zeh, “Efficient Interpolation-Based Decoding of Interleaved Subspace and Gabidulin Codes,” in 52nd Annual Allerton Conference on Communication, Control, and Computing, Oct. 2014, Monticello, IL, USA.

[16] A. Wachter-Zeh, “List Decoding of Crisscross Error Patterns,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2014, Honolulu, HI, USA.

[15] S. Puchinger, A. Wachter-Zeh, and M. Bossert, “Improved Decoding of Partial Unit Memory Codes Using List Decoding of Reed–Solomon Codes,” in Int. Zurich Sem. Comm. (IZS), Feb. 2014, Zurich, Switzerland.

[14] A. Wachter-Zeh, “Bounds on Polynomial-Time List Decoding of Rank Metric Codes,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2013, Istanbul, Turkey.

[13] A. Zeh, A. Wachter-Zeh, M. Gadouleau, and S. Bezzateev, “Generalizing Bounds on the Minimum Distance of Cyclic Codes Using Cyclic Product Codes,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2013, Istanbul, Turkey.

[12] A. Wachter-Zeh and A. Zeh, “Interpolation-Based Decoding of Interleaved Gabidulin Codes,” in Int. Workshop Coding Cryptogr. (WCC), Apr. 2013, Bergen, Norway.

[11] V. Sidorenko, A. Wachter-Zeh, and D. Chen, “On fast Decoding of Interleaved Gabidulin Codes,” in Int. Symp. Probl. Redundancy Inf. Control Systems, Sep. 2012, pp. 78–83, St. Petersburg, Russia.

[10] A. Wachter-Zeh, M. Stinner, and M. Bossert, “Efficient Decoding of Partial Unit Memory Codes of Arbitrary Rate,” in IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2012, pp. 2356–2360, Boston, MA, USA.

[9] A. Wachter-Zeh and V. Sidorenko, “Rank Metric Convolutional Codes for Random Linear Network Coding,” in IEEE Int. Symp. Network Coding (Netcod), Jul. 2012, Boston, MA, USA.

[8] A. Wachter-Zeh, “Bounds on List Decoding Gabidulin Codes,” in Int. Workshop Alg. Combin. Coding Theory (ACCT), Jun. 2012, pp. 329–334, Pomorie, Bulgaria.

[7] A. Zeh, A. Wachter-Zeh, and M. Bossert, “Unambiguous Decoding of Generalized Reed-Solomon Codes beyond Half the Minimum Distance,” in Int. Zurich Sem. Comm. (IZS), Feb. 2012, Zurich, Switzerland.

[6] A. Wachter, V. Sidorenko, M. Bossert, and V. Zyablov, “Partial Unit Memory Codes Based on Gabidulin Codes,” in IEEE Int. Symp. Inf. Theory (ISIT), Aug. 2011, pp. 2487–2491, St. Petersburg, Russia.

[5] A. Zeh, A. Wachter, and S. Bezzateev, “Efficient Decoding of Some Classes of Binary Cyclic Codes beyond the Hartmann–Tzeng Bound,” in IEEE Int. Symp. Inf. Theory (ISIT), Aug. 2011, pp. 1017–1021, St. Petersburg, Russia.

[4] A. Wachter, V. Afanassiev, and V. Sidorenko, “Fast Decoding of Gabidulin Codes,” in Int. Workshop Coding Cryptogr. (WCC), Apr. 2011, pp. 433–442, Paris, France.

[3] A. Wachter, V. Sidorenko, and M. Bossert, “A Fast Linearized Euclidean Algorithm for Decoding Gabidulin Codes,” in Int. Workshop Alg. Combin. Coding Theory (ACCT), Sep. 2010, pp. 298–303, Novosibirsk, Russia.

[2] A. Wachter, V. Sidorenko, and M. Bossert, “A Basis for all Solutions of the Key Equation for Gabidulin Codes,” in IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2010, pp. 1143–1147, Austin, TX, USA.

[1] S. Kampf, A. Wachter, and M. Bossert, “A Method for Soft-Decision Decoding of Reed-Solomon Codes Based on the Extended Euclidean Algorithm,” in ITG Conf. Source Channel Coding (SCC), Jan. 2010, pp. 1–6, Siegen, Germany.

Theses

[3] A. Wachter-Zeh, “Decoding of Block and Convolutional Codes in Rank Metric,” Ph.D. dissertation, Ulm University and University of Rennes 1, Ulm, Germany and Rennes, France, Oct. 2013.

[2] A. Wachter, “Soft-Decision Decoding of Reed–Solomon Codes Beyond Half the Minimum Distance with the Euclidean Algorithm,” Master’s thesis, Ulm University, Ulm, Germany, Oct. 2009.

[1] A. Wachter, “Signal Processing Algorithms for Air-Based Radar Systems,” Bachelor’s thesis, DHBW Ravensburg, Sep. 2007.

Book Chapter

[1] M. Bossert, V. Sidorenko, and A. Wachter-Zeh, “Coding Techniques for Transmitting Packets Through Complex Communication Networks,” in Communications in Interference Limited Networks, ser. Signals and Communication Technology, W. Utschick, Ed. Springer International Publishing, 2016, pp. 1–29.