Research Highlights

Error Correction Laboratory: Director. Coding theory and applications to quantum computing and communications, optical communications and flash memories.

LDPC Codes: Pioneering work on structured low-density parity check (LDPC) error correcting codes and iterative decoders. Designed codes and decoders with best error-floor performance known today.

Codelucida: Founder and Chief Scientific Officer of Codelucida, a company developing error correction coding solutions for flash memories.

Bell Labs: An inventor of the soft error-event decoding algorithm, and the key architect of a detector/decoder for Bell Labs data storage read channel chips which were regarded as the best in industry.

Honors and distinctions

IEEE Fellow - For contributions to coding theory and its application in data storage systems and optical communications

Kenneth Von Behren Chair da Vinci Fellow
Chair of the Data Storage Technical Group
- IEEE Communications Society

Serbian Academy of Sciences and Arts Scholarship

2019 Best of Show Award for the Most Innovative Flash Memory Technology for Codelucida - Flash Memory Summit

2018 I-dotd Startup of the Year for Codelucida - Tech Launch Arizona

Institute of Advanced Study Grant - Universite Paris Seine

2017 Arizona Innovation Challenge Award for Codelucida- Arizona Commerce Authority

Expert Panel Chair - Flash Memory Summit

About ECL

The research in the Error Correction Laboratory is in the general area of information theory, more specifically in channel coding. Our specialty is so called modern coding theory and includes low-density parity check codes and iterative decoding algorithms. We design and analyze classical and quantum codes and decoders for both communications and computing systems. Our error correction systems are used in variety of technologies such as flash memories and optical and wireless communications.

ECL Job Openings

NSF-CIF-2100013: Small: Learning To Correct Error
ECL Job Openings

ECL Job Openings

The Error Correction Laboratory is looking for multiple PhD candidates to work on the projects funded by NASA, Fermi National Accelerator Laboratory, and National Science Foundation. Topics or research is a design and optimization of algorithms for iterative decoding for error correction codes using machine learning.
Strong background in digital communications, coding theory, probability theory and algebra is a must. Master of Science in Electrical and Computer Engineering is required, but exceptional candidates with Bachelors of Science in Electrical Engineering who qualify for the University of Arizona direct Ph.D. program will be also considered.

NSF-CIF-2100013: Small: Learning To Correct Error
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