Hang Liu

I am a Presidential Fellowship Assistant Professor of Electrical and Computer Engineering at Stevens Institute of Technology, where I lead the HPDA lab. My research exploits emerging hardware — such as Graphics Processing Unit (GPU), Field-Programmable Gate Array (FPGA), high-end CPU and Solid-State Drive (SSD) — to build high-performance systems for graph computing, machine learning, computational omics, numerical simulation and cloud computing. I have received the prestigious NSF CAREER award, IEEE CS TCHPC Early Career Researchers Award for Excellence in High Performance Computing – 2022, the Best Dissertation Award of Electrical and Computer Engineering from the George Washington University, the Champion of GraphChallenge 2018 and 2019, the Lawrence Berkeley National Laboratory SRP fellowship 2019 and 2021, One of the best papers in VLDB '20, and Provost Early Career Award for Research Excellence 2022.

Prior to joining Stevens, I was an assistant professor at the University of Massachusetts Lowell. I received the Ph.D. degree from the George Washington University in 2017 (advisor: Prof. H. Howie Huang), and B.E. from Huazhong University of Science and Technology in 2011.

Some simple and actionable research advice: here [GitHub link].

I’m always looking for Ph.D. students with full scholarship. Please contact me with your CV at hliu77@stevens.edu if you are interested!

Recent News

• Oct 2022
  I feel extremely honored to receive the IEEE CS TCHPC Early Career Researchers Award for Excellence in High Performance Computing – 2022! Thanks to the IEEE CS TCHPC, the award committee, David, Sherry, Ali, Duane, Rajiv, Adolfy, Guang, Shuaiwen, Dingwen and many other friends and collaborators for their rock solid supports and insightful guidance!
• Sept 2022
  Welcome Lang to join our group!
• Sept 2022
  I am thrilled to receive the Presidential Fellowship Award. Thanks to the school leadership for the recognition, and Jean and Min for the nomination!
• Aug 2022
  Tea is accepted to Eurosys '23. Congrats Chengying and Santosh.
• July 2022
  One paper is accepted to SC '22. Congrats Santosh!
• June 2022
  Our NSF medium proposal is funded (Co-PI, PI: Prof. Zhuo Feng). Thanks NSF!
• May 2022
  I am honored to receive the 2022 Early Career Award for Research Excellence!
• April 2022
  One paper is accepted to ICS '22. Congratulations to the team!
• Mar 2022
  SIMNET is accepted to SIGMETRICS '22. We started this collaboration from 2019. What a ride!
• Feb 2022
  One paper accepted to DAC '22. Congrats to Hongwu, Shaoyi, Shiyang and all the collaborators!
• Feb 2022
  One paper accepted to ACL '22. Congrats to Shaoyi and all the collaborators!
• Jan 2022
  I am very happy to see that my Google Scholar citation count has surpassed 1,000! Hopefully, those projects that cite our work "learned" something by standing on our "shoulder". :)
• Nov 2021
  TeGraph accepted to ICDE '22.
• June 2021
  Dr. Top-k and E.T. are accepted to SC '21, congratulations to all of us! Dr. Top-k can find top-k elements from a gigantic unsorted array lightning fast! and E.T. is faster than the state of the art enterprise solutions, such as, FasterTransformer and TensorRT!
• May 2021
  GSoFa is accepted to TPDS, congratulations to Anil!
• May 2021
  I am honored to receive the 2021 ECE Outstanding Research Award! Thanks!
• April 2021
  My CPE 360 class receives top student evaluation score (3.94 out of 4)! Thank the students for this positive evaluation!
• Mar 2021
  Our group is selected to visit Lawrence Berkeley National Lab in the summer! Thanks SRP and our host Dr. Xiaoye S. Li.
• Feb 2021
  TRUST is accepted to IEEE Transactions on Parallel and Distributed Systems (TPDS)! Congrats Santosh and Zhibin! TRUST achieves unprecedentedly 1 trillion TEPS performance rate with 1,024 GPUs! Check it out here.
• Feb 2021
  I am also sharing my experience about NSF CAREER proposal writing at here [GitHub link]. Hope it will help you!
• Feb 2021
  My NSF CAREER Award has been funded! Thanks NSF!
• Jan 2021
  One paper accepted to Eurosys'21!
• Jan 2021
  Welcome Zhanfu to join our group!

Selected Publications

(Full publication list at Google Scholar)

• [Eurosys '23] TEA: A General-Purpose Temporal Graph Random Walk Engine
• [SC '22] Scalable Deep Learning-based Microarchitecture Simulation on GPUs
• [ICS '22] Bring Orders into Uncertainty: Enabling Efficient Uncertain Graph Processing via Novel Path Sampling on Multi-Accelerator System
• [SIGMETRICS '22] SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning
• [DAC '22] A Length Adaptive Algorithm-Hardware Co-design of Transformer on FPGA Through Sparse Attention and Dynamic Pipelining
• [ACL '22] Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm
• [ICDE '22] TeGraph: A Novel General-Purpose Temporal Graph Computing Engine
• [SC '21] E.T.: Re-Thinking Self-Attention for Transformer Models on GPUs [Slides] [Source code]
• [SC '21] Dr.Top-k: Delegate-Centric Top-k Computation on GPUs [Slides] [Source code]
• [TPDS] GSOFA: Scalable Sparse Symbolic LU Factorization on GPUs
• [IJCAI '21] Against Membership Inference Attack: Pruning is All You Need
• [ISCA '21] FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator
• [TPDS] TRUST: Triangle Counting Reloaded on GPUs [Source code]
• [Eurosys '21] Tahoe: Tree Structure-Aware High Performance Inference Engine for Decision Tree Ensemble on GPU
• [SC '20] C-SAW: A Framework for Graph Sampling and Random Walk on GPUs [Source code]
• [EMNLP Findings '20] Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning
  
• [HPDC '20] FFT-based Gradient Sparsification for the Distributed Training of Deep Neural Network
• [DAC '20] FTDL: A Tailored FPGA-Overlay for Deep Learning with High Scalability
• [VLDB '20] PM-LSH: A Fast and Accurate LSH Framework for High-Dimensional Approximate NN Search [One of the best papers in VLDB '20]
  
• [GraphChallenge '19] H-INDEX: Hash-Indexing for Parallel Triangle Counting on GPUs [Awarded Champion]
  
• [HPDC '19] XBFS: eXploring Dynamic Optimizations for Breadth-First Search on GPUs [Source code]
• [USENIX ATC '19] SIMD-X: Programming and Processing of Graph Algorithms on GPUs [Source code] [Video]
• [DAC '19] Dr. BFS: Data Centric Breadth-First Search on FPGAs
• [SIGMOD '19] CECI: Compact Embedding Cluster Indexfor Scalable Subgraph Matching
• [SC '18] TriCore: Parallel Triangle Counting on GPUs
• [SC '18] iSpan: Parallel Identification of Strongly Connected Components with Spanning Trees
• [USENIX FAST '18] UKSM: Swift Memory Deduplication via Hierarchical and Adaptive Memory Region Distilling
• [USENIX FAST '17] Graphene: Fine-Grained IO Management for Graph Computing
• [Journal of Computational Physics '16] Computational Modeling of Cardiac Hemodynamics: Current Status and Future Outlook
• [SIGMOD '16] iBFS: Concurrent Breadth-First Search on GPUs
• [SC '15] Enterprise: Breadth-First Graph Traversal on GPUs

Students

I am very fortunate to work with the following people:

• PhD students
  • Anil Gaihre (Tribhuvan University, Nepal; SC, HPDC, TPDS, IEEE BigData)
  • Santosh Pandey (Tribhuvan University, Nepal; SC, TPDS, SC)
  • Shiyang Chen (Huazhong University of Science and Technology, China; SC)
  • Zhanfu Yang (BS: Sun Yet-Sen University, China; MS: Purdue University)
  • Lang Zhu (BS: CUHK, MS: Stevens)
• Masters students
  • Scott Weitze
  • Luca Pasquariello
  • Yufeng Liu
  • Zehui Xie
• Undergraduate students
  • Pridhvi Myneni
  • Chris Waldt
  • Jared Kantor
  • Yohanes Gunarso
  • Daniel Giger
• Alumni
  • Shilong Wang (Ph.D., co-advised w/ Prof. Hengyong Yu). First employment: Cadence.
  • Zhenlin Wu (MS)
  • Hans-Edward Hoene (BS)
  • Andy MacGregor (BS)
  • Noah Saffie (BS)
  • Zach Sherer (BS)
  • Eric Finnerty (BS)
  • Alexander Main (BS)
  • Femi Ogunseye (Middlesex High School)

Professional Service

• Program Committee:
  • 2021: SC, HPDC, IPDPS
  • 2020: SC, HPDC, ICDCS
  • 2019: HPDC, IEEE BigData, NAS, Chapel@PLDI
  • 2018: HPDC, IPDPS, NAS, ASPLOS shadow PC
  • 2017: Eurosys shadow PC
• Editorial board: Journal of BigData: Theory and Practice
• Journal reviewer: TPDS 2016, 2017, 2018, 2019, 2020; TC 2018, 2019
• NSF panel: 2018, 2019, 2020, 2021

Teaching

• CPE 517 Computer and Digital Systems Architecture (Fall 2019, Spring 2020, Spring 2021)
• CPE 360 Computational Data Structures & Algorithms (Fall 2020)
• EECE4810/5730 Operating Systems (Fall 2018)
• EECE 7110 High-Performance Computing on GPUs (Spring 2018, Spring 2019)

Research Advice