Hang Liu (hliu77)

Hang Liu

Assistant Professor

Research

High-Performance Computing
Graph Computing
Machine Learning
Data Privacy

Experience

Assistant Professor University of Massachusetts Lowell 2017 - 2019

Institutional Service

  • Graduate Student Recruiting Committee Member
  • Research computing committee Member
  • Department award committee Chair
  • Strategic Plan Committee Member
  • Graduate Student Recruiting Committee Member
  • Faculty hiring @ ECE Member
  • Faculty hiring at CS of Stevens Member
  • Faculty hiring Member
  • Faculty hiring Member

Professional Service

  • The workshop on Graph Techniques for Adversarial Activity Analytics 2021 Program Co-Chair
  • IEEE Transactions on Computers Reviewer
  • Journal of BigData: Theory and Practice Associate editor
  • IPDPS '21 Program Committee Member
  • SC '21 Program Committee Member
  • HPDC '21 Program Committee Member
  • IEEE Transactions on Parallel and Distributed Systems Reviewer
  • PPoPP '22 Program Committee Member
  • NSF Panelist
  • SC '20 Program Committee Member
  • NSF Panelist
  • NSF Panelist
  • HPDC '20 Committee Member

Consulting Service

Jabil Inc.

Honors and Awards

NSF CAREER Award 2021

NSF CRII Award 2019

DOE SRP Fellowship 2019

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 '19

DOE SRP '19

Selected Publications

Conference Proceeding

  1. Huang, S.; Xu, D.; Yen, I.; Wang, Y.; Chang, S.; Li, B.; Chen, S.; Xie, M.; Rajasekaran, S.; Liu, H.; Ding, C. (2022). Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm. Proceeding of 60th Annual Meeting of the Association for Computational Linguistics (ACL).
  2. Zhang, H.; Li, L.; Liu, H.; Zhuang, D.; Liu, R.; Huan, C.; Song, S.; Tao, D.; Liu, Y.; He, C.; Wu, Y.; Song, S. (2022). Bring Orders into Uncertainty: Enabling Efficient Uncertain Graph Processing via Novel Path Sampling on Multi-Accelerator Systems. Proceedings of the 36th ACM International Conference on Supercomputing (ICS).
  3. Li, L.; , S. P.; Liu, H.; Hoisie, A. (2022). SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning. Proceedings of the ACM on Measurement and Analysis of Computing Systems (SIGMETRICS).
  4. Yuan, G.; Behnam, P.; Li, Z.; Shafiee, A.; Lin, S.; Ma, X.; Liu, H.; Qian, X.; Bojnordi, M. N.; Wang, Y.; others (2021). FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator. The International Symposium on Computer Architecture.
  5. Xie, Z.; Dong, W.; Liu, J.; Liu, H.; Li, D. (2021). Tahoe: tree structure-aware high performance inference engine for decision tree ensemble on GPU. Proceedings of the Sixteenth European Conference on Computer Systems (pp. 426--440).
  6. Gaihre, A.; Zheng, D.; Liu, H. (2021). Dr. Top-k: Delegate-Centric Top-kon GPUs. SC21: International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1--15).
  7. Chen, S.; Huang, S.; Gao, G.; Ding, C.; Liu, H. (2021). E.T.: Re-thinking Self-Attention for Transformer Models on GPUs. SC21: International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1--15).
  8. Peng, H.; Huang, S.; Geng, T.; Li, A.; Jiang, W.; Liu, H.; Wang, S.; Ding, C. (2021). Accelerating Transformer-based Deep Learning Models on FPGAs using Column Balanced Block Pruning.. International Symposium on Quality Electronic Design.
  9. Chowdhuryy, M. H.; Liu, H.; Yao, F. (2020). BranchSpec: Information leakage attacks exploiting speculative branch instruction executions. 2020 IEEE 38th International Conference on Computer Design (ICCD) (pp. 529--536).
  10. Pandey, S.; Li, L.; Hoisie, A.; Li, X. S.; Liu, H. (2020). C-SAW: A framework for graph sampling and random walk on GPUs. SC20: International Conference for High Performance Computing, Networking, Storage and Analysis (pp. 1--15).
  11. 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).
  12. 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).
  13. 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).
  14. 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).
  15. Ji, Y.; Liu, H.; Huang, H. H. (2020). Swarmgraph: Analyzing large-scale in-memory graphs on gpus. 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) (pp. 52--59).
  16. 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).
  17. 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).
  18. 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).
  19. 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.
  20. 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).
  21. 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).
  22. 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).
  23. 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).
  24. 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).
  25. 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).
  26. 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).
  27. 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).
  28. 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).
  29. 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).
  30. 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).
  31. 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).
  32. 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).
  33. 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).
  34. 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).
  35. 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).
  36. 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. Jiao, M.; Wan, G.; Guo, Y.; Wang, D.; Liu, H.; Xiang, J.; Liu, F. (2022). A Graph Fourier Transform Based Bidirectional Long Short-Term Memory Neural Network for Electrophysiological Source Imaging. Frontiers in Neuroscience (vol. 16). Frontiers Media SA.
  2. Gaihre, A.; Liu, H.; Li, X. (2021). GSOFA: Scalable Sparse LU Symbolic Factorization on GPUs. IEEE Transactions on Parallel and Distributed Systems. IEEE.
  3. Zheng, B.; Zhao, X.; Weng, L.; Nguyen, Q. V.; Liu, H.; Jensen, C. S. (2021). PM-LSH: a fast and accurate in-memory framework for high-dimensional approximate NN and closest pair search. The VLDB Journal (pp. 1--25). Springer Berlin Heidelberg.
  4. Deng, J.; Wang, Y.; Li, J.; Shang, C.; Liu, H.; Rajasekaran, S.; Ding, C. (2021). TAG: Transformer Attack from Gradient. arXiv preprint arXiv:2103.06819.
  5. Pandey, S.; Wang, Z.; Zhong, S.; Tian, C.; Zheng, B.; Li, X.; Li, L.; Hoisie, A.; Ding, C.; Li, D.; others (2021). TRUST: Triangle Counting Reloaded on GPUs. IEEE Transactions on Parallel and Distributed Systems (11 ed., vol. 32, pp. 2646--2660). IEEE.
  6. Aloufi, A.; Hu, P.; Liu, H.; Chow, S. S.; Choo, K. R. (2021). Universal location referencing and homomorphic evaluation of geospatial query. Computers \& Security (vol. 102, pp. 102137). Elsevier Advanced Technology.
  7. Xiang, Y.; Liu, H.; Lan, T.; Huang, H.; Subramaniam, S. (2020). Optimizing job reliability through contention-free, distributed checkpoint scheduling. IEEE Transactions on Network and Service Management. IEEE.
  8. 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.
  9. Giger, D.; Liu, H. (2019). An Efficient Parallel Algorithm for Dominator Detection.
  10. Liu, H.; Liao, C. (2019). Actionable Guidance for Junior HPC Researchers.
  11. 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.
  12. Yan, D.; Liu, H. (2018). Parallel graph processing. Encyclopedia of Big Data Technologies (pp. 1--8). Springer International Publishing.
  13. 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.
  14. 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.
  15. 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.
  16. 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).
  17. 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).
  18. 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).
  19. 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).
  20. 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).
  21. 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).

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.

Courses

CPE 360;
CPE 517;
CPE 517-WS;