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Jan Balewski

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Jan Balewski Ph.D.
Fax: (510) 486-6459
1 Cyclotron Road
Mailstop: 59R4010A
Berkeley, CA 94720 US

Biographical Sketch

Jan Balewski works at NERSC on quantum computing experiments at AQT/LBNL  as well as on machine learning HPC projects applied to cosmology and neuroscience data. Jan holds a Ph.D. in physics from Jagiellonian Uni, Cracow, Poland.  He worked as a researcher at MIT, LNS, on the dark matter detection experiment  DarkLight at JLAB and on polarized protons experiment STAR at RHIC/BNL. 


Journal Articles

Yilun Xu, Gang Huang, Jan Balewski, Ravi K. Naik, Alexis Morvan, Brad Mitchell, Kasra Nowrouzi, David I. Santiago, Irfan Siddiqi, "Automatic Qubit Characterization and Gate Optimization with QubiC", ACM Transactions on Quantum Computing, April 20, 2021,

William B Andreopoulos, Alexander M Geller, Miriam Lucke, Jan Balewski, Alicia Clum, Natalia Ivanova, Asaf Levy, "Deeplasmid: Deep learning accurately separates plasmids from bacterial chromosomes", Nucleic Acids Research, April 2, 2021,

Yilun Xu, Gang Huang, Jan Balewski, Ravi Naik, Alexis Morvan, Bradley Mitchell, Kasra Nowrouzi, David I. Santiago, Irfan Siddiqi, "QubiC: An open source FPGA-based control and measurement system for superconducting quantum information processors", IEEE Transactions on Quantum Engineering, December 1, 2020,

Yosuke Oyama, Jan Balewski, and more, "The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs With Hybrid Parallelism", IEEE Transactions on Parallel and Distributed Systems, December 1, 2020,

S. Lee, R. Corliss, I. Friščić, R. Alarcon, S. Aulenbacher, J. Balewski, S. Benson, J.C. Bernauer, J. Bessuille, J. Boyce, J. Coleman, D. Douglas, C.S. Epstein, P. Fisher, S. Frierson, M. Garçon, J. Grames, D. Hasell, C. Hernandez-Garcia, E. Ihloff, R. Johnston, K. Jordan, R. Kazimi, J. Kelsey, M. Kohl, A. Liyanage, M. McCaughan, R.G. Milner, P. Moran, J. Nazeer, D. Palumbo, M. Poelker, G. Randall, S.G. Steadman, C. Tennant, C. Tschalär, C. Vidal, C. Vogel, Y. Wang, S. Zhang,, "Design and operation of a windowless gas target internal to a solenoidal magnet for use with a megawatt electron beam", Nuclear Instruments and Methods in Physics Research Section A, September 21, 2019,

D Androić, J Balewski, and many more, "Precision measurement of the weak charge of the proton", Nature, May 9, 2018,

Conference Papers

Grzegorz Muszynski, Prabhat, Jan Balewski, Karthik Kashinath, Michael Wehner, Vitaliy Kurlin, "Atmospheric Blocking Pattern Recognition in Global Climate Model Simulation Data", publication descriptionICPR 2020, May 5, 2021,

M D Poat, J Lauret, J Porter, Jan Balewski, "STAR Data Production Workflow on HPC: Lessons Learned & Best Practices", 19th International Workshop on Advanced Computing and Analysis Techniques 2019, April 1, 2020,

Glenn K. Lockwood, Wucherl Yoo, Suren Byna, Nicholas J. Wright, Shane Snyder, Kevin Harms, Zachary Nault, Philip Carns, "UMAMI: a recipe for generating meaningful metrics through holistic I/O performance analysis", Proceedings of the 2nd Joint International Workshop on Parallel Data Storage & Data Intensive Scalable Computing Systems (PDSW-DISCS'17), Denver, CO, ACM, November 2017, 55-60, doi: 10.1145/3149393.3149395

I/O efficiency is essential to productivity in scientific computing, especially as many scientific domains become more data-intensive. Many characterization tools have been used to elucidate specific aspects of parallel I/O performance, but analyzing components of complex I/O subsystems in isolation fails to provide insight into critical questions: how do the I/O components interact, what are reasonable expectations for application performance, and what are the underlying causes of I/O performance problems? To address these questions while capitalizing on existing component-level characterization tools, we propose an approach that combines on-demand, modular synthesis of I/O characterization data into a unified monitoring and metrics interface (UMAMI) to provide a normalized, holistic view of I/O behavior.

We evaluate the feasibility of this approach by applying it to a month-long benchmarking study on two distinct large-scale computing platforms. We present three case studies that highlight the importance of analyzing application I/O performance in context with both contemporaneous and historical component metrics, and we provide new insights into the factors affecting I/O performance. By demonstrating the generality of our approach, we lay the groundwork for a production-grade framework for holistic I/O analysis.

Mustafa Mustafa, Jan Balewski, Jérôme Lauret, Jefferson Porter, Shane Canon, Lisa Gerhardt, Levente Hajdu2, Mark Lukascsyk, "STAR Data Reconstruction at NERSC/Cori, an adaptable Docker container approach for HPC", CHEP 2016, October 1, 2017,


Gang Huang,Yilun Xu,Jan Balewski, QubiC - Qubits Control Systems at LBNL, Supercomputing Conference, SC20, November 11, 2020,

Tutorial w/ handouts. use of Shifter w/ image of chos=sl64 from PDSF Download the slides at



Roy Ben-Shalom, Jan Balewski, Anand Siththaranjan, Vyassa Baratham, Henry Kyoung, Kyung Geun Kim, Kevin J. Bender, Kristofer E. Bouchard, "Inferring neuronal ionic conductances from membrane potentials using CNNs", August 6, 2019,

Glenn K. Lockwood, Damian Hazen, Quincey Koziol, Shane Canon, Katie Antypas, Jan Balewski, Nicholas Balthaser, Wahid Bhimji, James Botts, Jeff Broughton, Tina L. Butler, Gregory F. Butler, Ravi Cheema, Christopher Daley, Tina Declerck, Lisa Gerhardt, Wayne E. Hurlbert, Kristy A. Kallback-
Rose, Stephen Leak, Jason Lee, Rei Lee, Jialin Liu, Kirill Lozinskiy, David Paul, Prabhat, Cory Snavely, Jay Srinivasan, Tavia Stone Gibbins, Nicholas J. Wright,
"Storage 2020: A Vision for the Future of HPC Storage", October 20, 2017, LBNL LBNL-2001072,

As the DOE Office of Science's mission computing facility, NERSC will follow this roadmap and deploy these new storage technologies to continue delivering storage resources that meet the needs of its broad user community. NERSC's diversity of workflows encompass significant portions of open science workloads as well, and the findings presented in this report are also intended to be a blueprint for how the evolving storage landscape can be best utilized by the greater HPC community. Executing the strategy presented here will ensure that emerging I/O technologies will be both applicable to and effective in enabling scientific discovery through extreme-scale simulation and data analysis in the coming decade.