About

Biography

I am a Computer Scientist in the AI/ML Group at Argonne National Laboratory (ALCF). My research interests include agentic AI systems for autonomous scientific discovery, large-scale distributed training, and data management for AI. I apply high-performance computing and deep learning to domain sciences including physics, chemistry, and materials science. I co-lead the MLPerf Storage Benchmarking group, developing benchmark suites for evaluating the performance of storage systems for AI applications.

My current projects include: (1) ExaIO/ExaHDF5 — developing advanced features in the HDF5 library for efficient parallel I/O, including caching and pre-staging on node-local storage and topology-aware collective I/O; (2) data management and I/O optimization for AI workloads; (3) scalable image reconstruction algorithm development; and (4) agentic AI workflows for autonomous scientific discovery across DOE leadership computing facilities.

I received my Ph.D. in Physics from the University of Illinois at Urbana-Champaign in 2016, with a strong background in condensed matter physics, density functional theory, and Quantum Monte Carlo. I joined ALCF as a Theta Early Science postdoc in 2016, working on developing and scaling density functional theory and many-body perturbation theory. In 2018, I joined the AI/ML Group, expanding my expertise in data science and machine learning.

Education

  • Ph.D. in Physics — University of Illinois at Urbana-Champaign, 2016
  • M.Phil. in Physics — Hong Kong University of Science and Technology, 2010
  • B.Sc. in Physics — Tsinghua University, 2008

Personal

I grew up in a small village in southern China, where both of my parents are farmers. My curiosity about nature led me to pursue physics through a Ph.D.; deeper questions about meaning eventually led me to faith. I became a Christian in Hong Kong in 2010, and I find that work, family, and personal life are all means to grow and find purpose.

I live in Glen Ellyn, IL with my wife and two children. Outside of work I enjoy hiking, walking in nature preserves, and distance running — I have completed seven full marathons.

Talks & Invited Lectures

2026
May 2026 • Washington D.C.
Agentic AI for Autonomous Scientific Workflows at Exascale
DOE ASCR Workshop on Autonomous Scientific Discovery — Invited
2025
Nov 2025 • St. Louis, MO
AskHPC: A ChatBot for High Performance Computing User Support
SC’25 Workshops — Paper presentation
Jun 2025 • Zurich, Switzerland
HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights
PASC 2025 Conference — Paper presentation
2025
Data Management and I/O Benchmarking for Exascale AI Workloads
MLCommons MLPerf Storage Working Group — Community talk
2024
Nov 2024 • Atlanta, GA
DFTracer: An Analysis-Friendly Data Flow Tracer for AI-Driven Workflows
SC24 — Paper presentation
Nov 2024 • Atlanta, GA
MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein Design
SC24 — Paper presentation
2022
May 2022
HDF5 Cache VOL: Efficient and Scalable Parallel I/O through Caching on Node-local Storage
IEEE CCGrid 2022 — Paper presentation
May 2022
Stimulus: Accelerate Data Management for Scientific AI Applications in HPC
IEEE CCGrid 2022 — Paper presentation
2021
May 2021
DLIO: A Data-Centric Benchmark for Scientific Deep Learning Applications
IEEE/ACM CCGrid 2021 — Paper presentation

Teaching & Mentoring

Tutorials & Short Courses

Student & Postdoc Mentoring

I mentor students and postdocs through ALCF’s summer internship and postdoctoral programs. Past and current mentees have worked on topics including parallel I/O optimization, benchmarking AI workloads, deep learning for materials science, and agentic workflow systems.

Prospective Collaborators

I am always interested in collaborations at the intersection of HPC, scientific AI, and autonomous workflows. If you are a student, postdoc, or external researcher with a project connecting to my research areas, I would be happy to hear from you.

  • Students & postdocs: ALCF offers several programs for student and postdoc appointments. Email huihuo.zheng@anl.gov with a brief description of your background and interests, and attach your CV.
  • External researchers: If you are interested in using ALCF systems, DLIO/h5bench/MLPerf Storage benchmarks, or in joint research on agentic AI for science, please reach out.