Dingwen Tao

Registered user since Thu 7 Jan 2021

Name:Dingwen Tao

Dingwen Tao is an associate professor in the Department of Intelligent Systems Engineering at Indiana University, where he leads his research group, High-Performance Data Analytics and Computing (HiPDAC) Lab. Before joining IU, he worked as an assistant professor at Washington State University and University of Alabama between 2018 and 2022. Prior to that, he worked in the Computational Science Initiative (CSI) at Brookhaven National Laboratory, the Mathematics and Computer Science (MCS) Division at Argonne National Laboratory, and the High-Performance Computing (HPC) Group at Pacific Northwest National Laboratory. He is the recipient of various awards including NSF CAREER Award (2023), Amazon Research Award (2022), Meta Research Award (2022), R&D100 Awards Winner (2021), IEEE Computer Society TCHPC Early Career Researchers Award for Excellence in High Performance Computing (2020), NSF CRII Award (2020), IEEE CLUSTER Best Paper Award (2018). He is serving on the Technical Review Board (TRB) of IEEE Transactions on Parallel and Distributed Systems (TPDS). He served as the Program Co-chair of 2021 IEEE International Conference on Scalable Computing and Communications and International Workshops on Big Data Reduction. He is also a reviewer, program committee member, or session chair of major HPC venues, such as SC, HPDC, ICS, IPDPS, CLUSTER, ICPP, CCGrid, HiPC, NPC. He has published in the top-tier HPC and big data conferences and journals, including SC, ICS, HPDC, PPoPP, DAC, PACT, IPDPS, CLUSTER, ICPP, BigData, IEEE TC, IEEE TPDS, etc. His research has been supported by NSF, DOE, NOAA, AMD, Meta, and Xilinx.

Country:United States
Affiliation:Indiana University Bloomington
Research interests:High performance computing, parallel and distributed system, distributed deep learning, scientific data management


PPoPP 2021 Author of POSTER: An Efficient Uncertain Graph Processing Framework for Heterogeneous Architectures within the Main Conference-track
Author of POSTER: A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression within the Main Conference-track