Short Bio.
Zhengchun Liu focuses his Research and Development around Data, Artificial intelligence and High Performance Computing system. Currently, he is on sabbatical leave from Argonne National Laboratory and works as a Senior Machine Learning Scientist at AWS on AI-powered Scaling and Optimization for Amazon Redshift Serverless. Before this, he was a Computer Scientist at the Data Science and Learning division of Argonne National Laboratory, and a Scientist At-Large of the Consortium for Advanced Science and Engineering at The University of Chicago worked on AI for science and HPC system. For more details and contact info, please see his resume, cv.pdf.
Research Highlights
Zhengchun Liu works at the intersection of AI-for-Science(AI4S) and computer systems including HPC, distributed computing(e.g., geographically distributed science workflow) and AI systems(e.g., Cerebras, Graphcore, SambaNova etc) for AI4S apps. More specifically, on one side he focuses his effort on applying advanced data science and machine learning techniques for solving and/or accelerating domain scientific applications such as synchrotron X-ray imaging and climate (i.e., AI for Science). On the other side, he also works on building frameworks to facilitate the AI model training and inference process in the context of AI for Science (i.e., system for AI).
Research Interests:
- Performance modeling and optimization for large scale systems;
- Data Science and Machine Learning for scientific applications;
- High Performance Computing and Artificial Intelligence;
- Edge Computing and AI at Edge.
Professional Memberships and Activities:
- Professional Membership: Association for Computing Machinery (ACM).
- Co-Chair: 17th SRMPDS 2021;16th SRMPDS 2020;15th SRMPDS 2019; 14th SRMPDS 2018; 13th SRMPDS 2017; 2nd AI-Science 2019;
- Editorship: Journal of Future Generation Computer Systems, FGCS, 2020-2023. Special Issue on “High-Performance Computing for Autonomous Cloud” of Cluster Computing.
- Technical Program Committee: IARIA SIMUL’16; IARIA SIMUL’15; DAAC’17; DAAC’18; IARIA ICDS’19; AI-Science 2019;DLS’19;ICDCS’20; SC’20; AI for Scientific App.; HPCC’20; HPCC’21; IPDPS’23.
- Conference Reviewer: COMPUTATION TOOLS’15; Euro-Par’17; CLUSTER 2017; HiPC’17; WoWS’17; IPDPS’18; HiPC; ICACCP; ICDS’19
- Journal Reviewer: Algorithms-MDPI; Sustainability–MDPI; Sensors; FGCS(Outstanding Reviewer); AJCSIT–iMedPub (Outstanding Reviewer); IEEE-Access; JOCS(Rec.); IEEE-TPDS.
Honors, Awards:
- [
2022/07] The IMPACT ARGONNE award for notable achievement in Innovation [Award Plaque]. - [
2021/11] Best paper awarded at the 3rd Annual Workshop on Extreme-Scale Experiment-in-the-Loop Computing. - [
2021/10] Future Generation Computer Systems 2021 Best Paper Award. - [
2020/05] The IMPACT ARGONNE award for notable achievement in Innovation [Award Plaque]. - [
2019/12] Best paper award at the 2019 International Conference on Machine Learning for Networking (MLN’19). - [
2019/11] Our solution: “Real-Time Analysis of Streaming Synchrotron Data over High-Speed WAN using Deep Learning and HPC” won the Top Recognition Award in the Technical Challenge at SC’19 by SciNet, for an Exemplary Blend of Network, Computing, and Storage [Recognition Ceremony, Award, Photo] . - [
2019/03] The 2019 Data Science and Learning Devision Pacesetter Award by the Argonne National Laboratory, photo. - [
2018/12] Best paper awarded at the 13th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks & Communities (TRIDENTCOM’18). - [
2018/11] Best paper awarded at the 2018 International Conference on Machine Learning for Networking (MLN’18). - [
2018/06]The Extraordinary Doctorate Award by the Universitat Autònoma de Barcelona, certificate. - [
2015/10] The 10th Marathon of Parallel Programming Contest, The 1st Place, Results. - [
2015/09] The 5th Spanish Parallel Progamming Contest, The 1st Place, Results. - [
2014/09] The IV Spanish Parallel Progamming Contest , The 2nd Place, Results.
