Wei-keng Liao
Scholar

Wei-keng Liao

Google Scholar ID: jZDI-L0AAAAJ
Northwestern University
Parallel I/OHigh-performance data mining
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Citations & Impact
All-time
Citations
5,048
 
H-index
32
 
i10-index
72
 
Publications
20
 
Co-authors
0
 
Contact
No contact links provided.
Publications
1 items
Multi-Resolution Model Fusion for Accelerating the Convolutional Neural Network Training
2025
Cited
0
Resume (English only)
Academic Achievements
  • - MPI-IO. Book Chapter 13 of High Performance Parallel I/O, Chapman and Hall/CRC, October 2014.
  • - Delegation-based I/O Mechanism for High Performance Computing Systems. In the IEEE Transactions on Parallel and Distributed Sytems, vol. 23, no. 2, pages 271-79, February 2012.
  • - Dynamically Adapting File Domain Partitioning Methods for Collective I/O Based on Underlying Parallel File System Locking Protocols. In the Proceedings of International Conference for High Performance Computing, Networking, Storage and Analysis, Austin, Texas, November 2008.
  • - Towards Autonomic Science Infrastructure: Architecture, Limitations, and Open Issues. In the International Workshop on Autonomous Infrastructure for Science, held in conjunction with the 27th International Symposium on High-Performance Parallel and Distributed Computing, June 2018.
  • - DTF: An I/O Arbitration Framework for Multi-Component Data processing Workflows. In the ISC High Performance Conference, June 2018.
  • - Deep Learning Approaches for Mining Structure-property Linkages in High Contrast Composites from Simulation Datasets. Computational Materials Science, 151:278-287, Springer, May 2018.
Research Experience
  • - 2016-2023: Data Libraries and Services Enabling Exascale Science, Position: co-principle investigator, Sponsor: DOE under the ECP program of the Office of Advanced Scientific Computing Research
  • - 2018-2021: PROTEUS: Machine Learning Driven Resilience for Extreme-scale Systems, Position: co-principle investigator, Sponsor: DOE under the Office of Advanced Scientific Computing Research
  • - 2017-2020: Machine Learning/Deep Learning and I/O Research, Development, and Deployment for RAPIDS, Position: principle investigator, Sponsor: RAPIDS, SciDAC Institute for Computer Science and Data, DOE
  • - 2018-2019: AMASE: Architect and Manage Autonomic Science Ecosystem, Position: co-principle investigator, Sponsor: Argonne National Laboratory
  • - 2015-2018: Scalable, In-situ Data Clustering Data Analysis for Extreme Scale Scientific Computing, Position: co-principle investigator, Sponsor: DOE under the Office of Advanced Scientific Computing Research
  • - 2014-2017: Dynamical Modeling of Dense Star Clusters with a Parallel Monte Carlo Code, Position: co-principle investigator, Sponsor: NASA
  • - 2014-2017: Scalable Algorithms for Spatio-temporal Data Analysis, Position: co-principle investigator, Sponsor: NSF under the program of Computing and Communication Foundations (CCF)
  • - 2013-2016: Black Holes in Dense Star Clusters, Position: co-principle investigator, Sponsor: NSF under the program of Computational and Data-Enabled Science and Engineering (CDS&E)
Background
  • - Research Interests: Parallel and distributed file I/O and storage system design, data mining algorithm design and their parallelization, data management for large-scale scientific applications, computational model design for large-scale applications on parallel and distributed environments
  • - Position: Research Professor
  • - Department: Department of Electrical Engineering and Computer Science, Northwestern University
Co-authors
0 total
Co-authors: 0 (list not available)