Hang Liu

Assistant Professor

School: School of Engineering and Science

Department: Electrical and Computer Engineering

Building: Burchard

Room: 307B

Phone: (201) 2168103

Email: hliu77@stevens.edu

Website

Research

High-Performance Computing
Graph Computing
Machine Learning
Data Privacy

Experience

Assistant Professor University of Massachusetts Lowell 2017 - 2019

Institutional Service
  • Faculty hiring Member
  • Faculty hiring Member
Professional Service
  • SC '20 Program Committee Member
  • NSF Panelist
  • NSF Panelist
  • HPDC '20 Committee Member
Honors and Awards

NSF CAREER Award 2021

NSF CRII Award 2018

DOE SRP Fellowship 2018

DARPA/MIT/Amazon Graph Challenge Champion 2018, 2019 [News]

Best Dissertation Award 2018 @ ECE of GWU [News]

No.1 most energy efficient graph traversal (small graph category)

Professional Societies
  • ACM – Association for Computing Machinery Member
  • Institute of Electrical and Electronics Engineers – IEEE Member
  • USENIX – Usenix Association Member
Grants, Contracts, and Funds

NSF CAREER '21

NSF CRII '18

DOE SRP '19

Selected Publications
Conference Proceeding
  1. Wang, S.; Li, D.; Yu, H.; Liu, H. (2020). ELDA: LDA made efficient via algorithm-system codesign submission. Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (pp. 407--408).
  2. Wang, L.; Wu, W.; Zhang, J.; Liu, H.; Bosilca, G.; Herlihy, M.; Fonseca, R. (2020). FFT-based Gradient Sparsification for the Distributed Training of Deep Neural Networks. Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing (pp. 113--124).
  3. Li, B.; Pandey, S.; Fang, H.; Lyv, Y.; Li, J.; Chen, J.; Xie, M.; Wan, L.; Liu, H.; Ding, C. (2020). FTRANS: energy-efficient acceleration of transformers using FPGA. Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design (pp. 175--180).
  4. Shi, R.; Ding, Y.; Wei, X.; Liu, H.; So, H.; Ding, C. (2020). FTDL: An FPGA-tailored Architecture for Deep Learning Systems. The 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (pp. 320--320).
  5. Gaihre, A.; Pandey, S.; Liu, H. (2019). Deanonymizing Cryptocurrency With Graph Learning: The Promises and Challenges. 2019 IEEE Conference on Communications and Network Security (CNS) (pp. 1--3).
  6. Finnerty, E.; Sherer, Z.; Liu, H.; Luo, Y. (2019). Dr. BFS: Data Centric Breadth-First Search on FPGAs. Proceedings of the 56th Annual Design Automation Conference 2019 (pp. 208).
  7. Bhattarai, B.; Liu, H.; Huang, H. H. (2019). Ceci: Compact embedding cluster index for scalable subgraph matching. Proceedings of the 2019 International Conference on Management of Data (pp. 1447--1462).
  8. Pandey, S.; Li, X. S.; Buluc, A.; Xu, J.; Liu, H. (2019). H-INDEX: Hash-Indexing for Parallel Triangle Counting on GPUs. 2019 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1--7).
  9. Liu, H.; Huang, H. H. (2019). Simd-x: Programming and processing of graph algorithms on gpus. 2019 $\{$USENIX$\}$ Annual Technical Conference ($\{$USENIX$\}$$\{$ATC$\}$ 19) (pp. 411--428).
  10. Sherer, Z.; Finnerty, E.; Luo, Y.; Liu, H. (2019). Software Hardware Co-Optimized BFS on FPGAs. Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (pp. 190--190).
  11. Gaihre, A.; Wu, Z.; Yao, F.; Liu, H. (2019). XBFS: eXploring Runtime Optimizations for Breadth-First Search on GPUs. Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing (pp. 121--131).
  12. Zhang, J.; Zhuo, X.; Moon, A.; Liu, H.; Son, S. W. (2019). Efficient Encoding and Reconstruction of HPC Datasets for Checkpoint/Restart. IEEE... Symposium on Mass Storage Systems and Technologies.
  13. Xia, N.; Tian, C.; Luo, Y.; Liu, H.; Wang, X. (2018). $\{$UKSM$\}$: Swift Memory Deduplication via Hierarchical and Adaptive Memory Region Distilling. 16th $\{$USENIX$\}$ Conference on File and Storage Technologies ($\{$FAST$\}$ 18) (pp. 325--340).
  14. Gaihre, A.; Luo, Y.; Liu, H. (2018). Do bitcoin users really care about anonymity? an analysis of the bitcoin transaction graph. 2018 IEEE International Conference on Big Data (Big Data) (pp. 1198--1207).
  15. Hu, Y.; Liu, H.; Huang, H. H. (2018). High-performance triangle counting on gpus. 2018 IEEE High Performance extreme Computing Conference (HPEC) (pp. 1--5).
  16. Ji, Y.; Liu, H.; Huang, H. H. (2018). ispan: Parallel identification of strongly connected components with spanning trees. SC18: International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 731--742).
  17. Hu, Y.; Liu, H.; Huang, H. H. (2018). Tricore: Parallel triangle counting on gpus. SC18: International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 171--182).
  18. Liu, H.; Huang, H. H. (2017). Graphene: Fine-Grained $\{$IO$\}$ Management for Graph Computing. 15th $\{$USENIX$\}$ Conference on File and Storage Technologies ($\{$FAST$\}$ 17) (pp. 285--300).
  19. Moon, A.; Kim, J.; Zhang, J.; Liu, H.; Son, S. W. (2017). Understanding the impact of lossy compressions on IoT smart farm analytics. 2017 IEEE International Conference on Big Data (Big Data) (pp. 4602--4611).
  20. Liu, H.; Huang, H. H.; Hu, Y. (2016). ibfs: Concurrent breadth-first search on gpus. Proceedings of the 2016 International Conference on Management of Data (pp. 403--416).
  21. Liu, H.; Huang, H. H. (2015). Enterprise: breadth-first graph traversal on GPUs. SC'15: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1--12).
  22. Huang, H. H.; Liu, H. (2014). Big data machine learning and graph analytics: Current state and future challenges. 2014 IEEE International Conference on Big Data (Big Data) (pp. 16--17).
  23. Xiang, Y.; Liu, H.; Lan, T.; Huang, H.; Subramaniam, S. (2014). Optimizing job reliability via contention-free, distributed scheduling of vm checkpointing. Proceedings of the 2014 ACM SIGCOMM workshop on Distributed cloud computing (pp. 59--64).
  24. Liu, H.; Seo, J.; Mittal, R.; Huang, H. H. (2013). GPU-accelerated scalable solver for banded linear systems. 2013 IEEE International Conference on Cluster Computing (CLUSTER) (pp. 1--8).
  25. Liu, H.; Seo, J.; Mittal, R.; Huang, H. H. (2012). Matrix decomposition based conjugate gradient solver for poisson equation. 2012 SC Companion: High Performance Computing, Networking Storage and Analysis (pp. 1499--1500).
Journal Article
  1. Zheng, B.; Zhao, X.; Weng, L.; Hung, N. Q.; Liu, H.; Jensen, C. S. (2020). PM-LSH: A fast and accurate LSH framework for high-dimensional approximate NN search. Proceedings of the VLDB Endowment (5 ed., vol. 13, pp. 643--655). VLDB Endowment.
  2. Giger, D.; Liu, H. (2019). An Efficient Parallel Algorithm for Dominator Detection.
  3. Liu, H.; Ding, Y.; Zheng, D.; Son, S. W.; Yan, D. (2018). Challenges Towards Deploying Data Intensive Scientific Applications on Extreme Heterogeneity Supercomputers. arXiv preprint arXiv:1804.09738.
  4. Yan, D.; Liu, H. (2018). Parallel graph processing. Encyclopedia of Big Data Technologies (pp. 1--8). Springer International Publishing.
  5. Wang, L.; Wu, W.; Zhao, Y.; Zhang, J.; Liu, H.; Bosilca, G.; Dongarra, J.; Herlihy, M.; Fonseca, R. (2018). SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks. arXiv preprint arXiv:1811.08596.
  6. Zhao, Y.; Jian, Y.; Liu, Z.; Liu, H.; Liu, Q.; Chen, C.; Li, Z.; Wang, L.; Huang, H. H.; Zeng, C. (2017). Network analysis reveals the recognition mechanism for dimer formation of bulb-type lectins. Scientific reports (1 ed., vol. 7, pp. 2876). Nature Publishing Group.
  7. Mittal, R.; Seo, J. H.; Vedula, V.; Choi, Y. J.; Liu, H.; Huang, H. H.; Jain, S.; Younes, L.; Abraham, T.; George, R. T. (2016). Computational modeling of cardiac hemodynamics: Current status and future outlook. Journal of Computational Physics (vol. 305, pp. 1065--1082). Academic Press.
  8. Chen, G.; Martini, R.; Park, S. W.; Bethea, C. G.; Chen, I. C.; Grant, P. D.; Dudek, R.; Liu, H. (2010). Optically induced fast wavelength modulation in a quantum cascade laser. Applied Physics Letters (1 ed., vol. 97).
    https://api.elsevier.com/content/abstract/scopus_id/77954753827.
  9. Chen, G.; Bethea, C. G.; Martini, R.; Grant, P. D.; Dudek, R.; Liu, H. (2009). High-speed all-optical modulation of a standard quantum cascade laser by front facet illumination. Applied Physics Letters (10 ed., vol. 95).
    https://api.elsevier.com/content/abstract/scopus_id/70249107460.
  10. Capasso, F.; Paiella, R.; Martini, R.; Colombelli, R.; Gmachl, C.; Myers, T. L.; Taubman, M. S.; Williams, R. M.; Bethea, C. G.; Unterrainer, K.; Hwang, H. Y.; Sivco, D. L.; Cho, A. Y.; Sergent, A. M.; Liu, H.; Whittaker, E. (2002). Quantum cascade lasers: Ultrahigh-speed operation, optical wireless communication, narrow linewidth, and far-infrared emission. IEEE Journal of Quantum Electronics (6 ed., vol. 38, pp. 511-532).
    https://api.elsevier.com/content/abstract/scopus_id/0036610062.
  11. Gmachl, C.; Ng, H. M.; Paiella, R.; Martini, R.; Hwang, H. Y.; Sivco, D. L.; Capasso, F.; Cho, A. Y.; Frolov, S. V.; George Chu, S. N.; Liu, H. (2002). Recent results in quantum cascade lasers and intersubband transitions in GaN/AlGaN multiple quantum wells. Physica E: Low-Dimensional Systems and Nanostructures (2-4 ed., vol. 13, pp. 823-828).
    https://api.elsevier.com/content/abstract/scopus_id/0036492693.
  12. Martini, R.; Paiella, R.; Gmachl, C.; Capasso, F.; Whittaker, E.; Liu, H.; Hwang, H. Y.; Sivco, D. L.; Baillargeon, J. N.; Cho, A. Y. (2001). High-speed digital data transmission using mid-infrared quantum cascade lasers. Electronics Letters (21 ed., vol. 37, pp. 1290-1292).
    https://api.elsevier.com/content/abstract/scopus_id/0035846055.
  13. Paiella, R.; Martini, R.; Capasso, F.; Gmachl, C.; Hwang, H. Y.; Sivco, D. L.; Baillargeon, J. N.; Cho, A. Y.; Whittaker, E.; Liu, H. (2001). High-frequency modulation without the relaxation oscillation resonance in quantum cascade lasers. Applied Physics Letters (16 ed., vol. 79, pp. 2526-2528).
    https://api.elsevier.com/content/abstract/scopus_id/0035886354.
  14. Liu, H.; Liao, C.. Actionable Guidance for Junior HPC Researchers.
Ph.D. Thesis
  1. Liu, H. (2017). Novel Techniques for Graph Algorithm Acceleration. The George Washington University.
Technical Report
  1. Xiang, Y.; Liu, H.; Lan, T.; Huang, H.; Subramaniam, S. (2013). Optimizing job reliability through contention-free, distributed checkpoint scheduling. Online technical report available at www. seas. gwu. edu/tlan/papers/ICAC. pdf.