Bio:
Gonzalo Navarro completed his PhD in Computer Science in 1998 at the University of Chile, where he is currently full professor. His areas of interest include algorithms and data structures, compression, graph databases, and text searching. He has directed the Millennium Nucleus Center for Web Research, and projects funded by Yahoo! Research and Google. He currently participates in the Center for Biotecnology and Bioengineering (CeBiB) and the Millennium Institute for Foundational Research on Data (IMFD).
He has been PC (co-)chair of several conferences, including SPIRE 2001, SPIRE 2005, SIGIR 2005 Posters, SISAP 2008, SISAP 2012, LATIN 2016, SPIRE 2018, CPM 2018, ESA B 2022, and ALENEX 2023. He co-created SISAP on 2008, and was Steering Committee member of SPIRE, LATIN, SISAP, and ESA (present). He is the Editor in Chief of the ACM Journal of Experimental Algorithmics and a member of the Editorial Board of the ACM Transactions on Algorithms. He has been guest editor of special issues in ACM SIGSPATIAL, Journal of Discrete Algorithmics, Information Systems, Information and Computation, and Algorithmica. He has given around 50 invited talks in several universities and international conferences, including 15 plenary talks and 5 tutorials in international conferences. He created in 2005 the Workshop on Compression, Text, and Algorithms, which has become a permanent satellite of SPIRE.
He has coauthored two books published by Cambridge University Press, about 25 book chapters, 11 proceedings of international conferences (editor), 200 papers in international journals, and around 275 in international conferences. He is one of the most prolific and highly cited authors in Latin America.
He has received 7 Best Paper Awards in conferences, 4 Google Research awards, a Highest Cited Paper Award from Elsevier, and an Award Scopus Chile. He is an ACM Fellow.
He has advised 8 postdocs, 22 PhDs, 17 MScs, and 29 undergraduate theses.
Available Lectures
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Compact Data Structures
The sharp growth in the amount of data applications are handling, plus the increasing gap in the memory hierarchy, gives rise to several approaches to efficiently handling large...
- Compact Data Structures meet Databases
We describe two success stories on the application of compact data structures (cds) to solve the problem of the excessively redundant space requirements posed by...
- On the Compressibility of Highly Repetitive Sequences
Compressed indexes for highly repetitive text collections can reduce the data size by orders of magnitude while still supporting efficient searches. Compression of this...
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- Compact Data Structures meet Databases