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.
- High-level programming models for concurrency at large scale
- Distributed computing, fault tolerance, and recovery
- Workflow systems for simulation, experiment, and learning
- Simulation of computer systems
- 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.
- 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
- Co-Organizer, ERROR: Workshop on E-science ReseaRch leading tO negative Results @ eScience
- Co-Organizer, WoWoHa: DOE Workflow Workshop and Hackathon
- 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.
- High-bypass learning: Automated detection of tumor cells that significantly impact drug response
Justin M. Wozniak, Hyunseung Yoo, Jamaludin Mohd-Yusof, Bogdan Nicolae, Nicholson Collier, Jonathan Ozik, Thomas Brettin, and Rick Stevens
Proc. Machine Learning in HPC Environments @ SC 2020.
- MPI jobs within MPI jobs: A practical way of enabling task-level fault-tolerance in HPC workflows
Justin M. Wozniak, Matthieu Dorier, Robert Ross, Tong Shu, Tahsin Kurc, Li Tang, Norbert Podhorszki, and Matthew Wolf.
Future Generation Computing Systems 101, 2019.
- 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