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Seminar: High-Performance Big Data Analytics

Hang Liu

Stevens Institute of Technology

Wednesday, December 7, 2022
10:45 - 11:45 AM
via Zoom


Big data analytics can claim a large share of credit for tackling many grand challenges of our time, such as understanding the spread of pandemics, designing extremely large-scale integrated circuits, and discovering life-saving medicines, among many others.  In the High-Performance Data Analytics (HPDA) lab at Stevens, we develop High-Performance algorithms and systems to analyze big real-world data, understand the contextual and casual relationships within entities and events, and deliver actionable knowledge to stakeholders in an acceptable time envelope.  In this talk, I will share our experiences designing and developing high-performance systems for random walk and graph sampling on large dynamic graphs (featured at SC '20 and Eurosys '23).  Subsequently, I will present three ongoing and future projects: (i) training and online inference of temporal Graph Neural Networks (GNNs) at scale; (ii) using machine learning to accelerate microarchitecture simulations (featured at Sigmetrics '22 and SC '22) and (iii) offloading the Department of Energy (DoE) flagship direct solvers (i.e., SuperLU) on the FPGA clusters (featured at TPDS).


Dr. Hang Liu is currently a Presidential Fellowship Assistant Professor in the Department of Electrical and Computer Engineering at Stevens Institute of Technology.  He received his Ph.D. from George Washington University in 2017 and B.E. from Huazhong University of Science and Technology in 2011. Hang Liu's research interests include exploiting emerging hardware to build high-performance systems for graph analytics, machine learning, data compression, and numerical simulation.  Dr. Liu has received the prestigious NSF Career award, IEEE CS TCHPC Early Career Researcher Award for Excellence in High Performance Computing - 2022, NSF CRII Award, 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.