Name Title: Professor  Hongwei Huo

Department: Computer Science

Address:

Email: hwhuo@mail.xidian.edu.cn

Tel:

Hongwei Huo received the B.S. degree in mathematics from Northwest University, China, and the M.S. degree in computer science and the Ph.D. degree in electronic engineering from Xidian University.
Hongwei Huo is a Professor in School of Computer Science and Technology at Xidian University. She is the director of Lab of Algorithm Theory and Applications for Big Data (ATA).
Her research interests involve all aspects of algorithms, spanning from the design and analysis of efficient algorithms and data structures for the storage, compression, indexing, and retrieving information for big data like textual collections, larger-scale graph databases, and genomic sequences to the algorithm engineering. She has published more than 100 peer-reviewed papers in peer-reviewed journals abd leading conferences, including IEEE TKDE, Information and Computation (IandC), Bioinformatics, IEEE/ACM TCBB,  and IEEE Data Compression Conference (DCC), ACM-SIAM Algorithm Engineering and Experiments (ALENEX), String Processing and Information Retrieval (SPIRE).

• Design and analysis of algorithms, String algorithms, Graph algorithms
• Compressed data structure, Compressed indexes and retrieval
• Succinct indexing for graph databases, Compressed graph indexing
• Pan-genome indexing, Genome compression and pattern search
• Parallel and distributed algorithms, External memory algorithms, Algorithm engineering

Featured software

GeCSA: Practical High-order Entropy-compressed Text Self-indexing

(Hongwei Huo, Peng Long & Jeffrey Scott Vitter)

Featured publications:

• H. Huo, P. Long, J. S. Vitter, Practical high-order entropy-compressed text self-indexing, IEEE Transactions on Knowledge and Data Engineering (TKDE), 35(3): 2943–2960, 2023. Source code published in Code Ocean. PDF. 
• X.Chen, H.Huo, J.Huan, J.S.Vitter, W.Zheng, and L.Zou, MSQ-Index: A succinct index for fast graph similarity search, IEEE Transactions on Knowledge and Data Engineering (TKDE), 33(6):2654-2668, 2021. PDF. 
• Z. Li, J. Li, and H. Huo, Optimal in-place suffix sorting, Information and Computation (IandC), 285(Part B):104818, May 2022. PDF.
• H. Huo, P. Liu, C. Wang , H. Jiang and J. S.Vitter, CIndex: compressed indexes for fast retrieval of FASTQ files, Bioinformatics,  38(2):335-343, 2022. PDF.
• X.Chen, H.Huo, J.Huan, and J.S.Vitter, An efficient algorithm for graph edit distance computation, Knowl. Based Syst., 163(2019): 762–775, 2019.  PDF.
• H.Huo, X.Chen, X.Guo, J.S.Vitter, Efficient compression and indexing for highly repetitive DNA sequence collections, IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), 18(6): 2394-2408, 2021. PDF.
• Z. Sun, H. Huo, J. Huan, and J. S. Vitter, Feature reduction based on semantic similarity for graph classification, Neurocomputing, February 2020. PDF.
• H.Huo, X.Chen, Y.Zhao, X. Zhu, and J.S.Vitter, Practical succinct text indexes in external memory, Proceedings of the 2018 IEEE Data Compression Conference (DCC\'18), Snowbird, USA, 2018.
• Z.Li, J.Li and H.Huo, Optimal in-place suffix sorting, Proceedings of the 25th International Symposium on String Processing and Information Retrieval (SPIRE), Lima, Peru, 2018.
• H.Huo, Z.Sun, S.Li, J.S.Vitter, et al., CS2A: a compressed suffix array-based method for short read alignment, Proceedings of the 2016 IEEE Data Compression Conference (DCC\'16), Snowbird, USA, 2016.
• H.Huo, L.Chen, H.Zhao, J S.Vitter, et al., A data-aware FM-index, ACM-SIAM Proceedings of the 17th Meeting on Algorithm Engineering and Experiments (ALENEX), San Diego, California, USA, 2015.
• H.Huo, L.Chen, J.S.Vitter, and Y.Nekrich, A practical implementation of compressed suffix arrays with applications to self-indexing, Proceedings of the 2014 IEEE Data Compression Conference (DCC\'14), Snowbird, USA, 2014.

Software:
The software developed by her Lab can be accessed at the following website of GitHub:

Graduates:
Chinese Academy of Sciences, Universities, Research Institutes, Baidu, Tencent, Ali, Huawei, ZTE, Samsung, Meituan, SPSS, IBM, Intel, ByteDance