Justin Wozniak joined Argonne as a postdoc in 2008 and has been a member of the Data Science and Learning Division since its founding in 2018. He has had a joint appointment at the University of Chicago since 2009, and is currently a Scientist-at-Large.
Wozniak designs and implements workflow systems for scientific applications and deep learning workloads, combining techniques from high-performance computing and distributed computing. He has extensive experience integrating advanced computing techniques in collaboration with the experimental and simulation sciences.
- Systems architecture for deep learning and scientific computing
- High-level programming models for concurrency at large scale
- Distributed computing, fault tolerance, and recovery
- Workflow systems for simulation, experiment, and learning
- Integrating computing with databases, provenance, and analysis
- Ph.D., Computer Science and Engineering, University of Notre Dame, 2008.
- MMath, University of Waterloo, ON, 2003.
- B.S., Mathematics and Computer Science, University of Illinois at Urbana Champaign, 2000,
minors in Chemistry and Latin.
- R&D 100 Winner, CANDLE, 2023.
- Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research Finalist, 2020
- R&D 100 Winner, Swift/T, 2018.
- Best poster, University of Chicago MindBytes Posters 2017.
- Best paper, Cancer Workshop at SC 2016.
- Best paper, ScienceCloud 2014.
- Arthur J. Schmitt Leadership Fellowship, 2004-2008.
- Co-Chair, XLOOP: Workshop on Large-scale Experiment-in-the-Loop Computing @ SC
- Editorial Review Board member, Cancer Informatics
- Co-Organizer, ERROR: Workshop on E-science ReseaRch leading tO negative Results @ eScience
- Co-Organizer, WoWoHa: DOE Workflow Workshop and Hackathon
- An automation framework for comparison of cancer response models across configurations
Justin M. Wozniak, Rajeev Jain, Andreas Wilke, Rylie Weaver, Alexander Partin, Thomas Brettin, and Rick Stevens.
Proc. eScience 2023.
- Developing distributed high-performance computing capabilities of an open science platform for robust epidemic analysis
Nicholson Collier, Justin M. Wozniak, Abby Stevens, Yadu Babuji, Mickael Binois, Arindam Fadikar, Alexandra Wurth, Kyle Chard, and Jonathan Ozik.
Proc. ParSocial 2023.
- A population data-driven workflow for COVID-19 modeling and learning
Jonathan Ozik, Justin M. Wozniak, Nicholson Collier, Charles M. Macal, and Mickaël Binois, International Journal of High Performance Computing Applications, 2021.
- CANDLE/Supervisor: A workflow framework for machine learning applied to cancer research
Justin M. Wozniak, Rajeev Jain, Prasanna Balaprakash, Jonathan Ozik, Nicholson Collier, John Bauer, Fangfang Xia, Thomas Brettin, Rick Stevens, Jamaludin Mohd-Yusof, Cristina Garcia Cardona, Brian Van Essen, and Matthew Baughman. BMC Bioinformatics, 2018.
- Turbine: A distributed-memory dataflow engine for high performance many-task applications
Justin M. Wozniak, Timothy G. Armstrong, Ketan Maheshwari, Ewing L. Lusk, Daniel S. Katz, Michael Wilde, and Ian T. Foster. Fundamenta Informaticae 28(3), 2013.
A more complete list is maintained here: https://web.cels.anl.gov/~woz/papers.html