Publications

Here is a link to my arXiv» page and a sortable list of articles on Google Scholar».

All my publications are sorted into the following categories:

• Machine Learning, Quantum Information, and Mathematical Physics
• Physics Education Research and Social Studies
• Media and Impact
• Other publications

Machine Learning, Quantum Information, and Mathematical Physics

1. Amilson R. Fritsch, Shangjie Guo, Sophia M. Koh, I. B. Spielman, and Justyna P. Zwolak, Dark Solitons in Bose-Einstein Condensates: A Dataset for Many-body Physics Research. arXiv:2205.09114 (2022).
2. Justyna P. Zwolak and Jacob M. Taylor, Colloquium: Advances in automation of quantum dot devices control. arXiv:2112.09362 (2021).
3. Shangjie Guo, Sophia M. Koh, Amilson R. Fritsch, I. B. Spielman, and Justyna P. Zwolak, Combining machine learning with physics: A framework for tracking and sorting multiple dark solitons. Phys. Rev. Research 4(2): 023163 (2022).
4. Joshua Ziegler, Thomas McJunkin, E. S. Joseph, Sandesh S. Kalantre, Benjamin Harpt, D. E. Savage, M. G. Lagally, M. A. Eriksson, Jacob M. Taylor, and Justyna P. Zwolak, Toward Robust Autotuning of Noisy Quantum Dot Devices. Phys. Rev. Applied 17(2): 024069 (2022).
5. Brian J. Weber, Sandesh S. Kalantre, Thomas McJunkin, Jacob M. Taylor, and Justyna P. Zwolak, Theoretical bounds on data requirements for the ray-based classification. SN Comp. Sci. 3(1): 57 (2022).
6. Justyna P. Zwolak, Thomas McJunkin, Sandesh S. Kalantre, Samuel F. Neyens, E. R. MacQuarrie, Mark A. Eriksson, and Jacob M. Taylor, Ray-based framework for state identification in quantum dot devices. PRX Quantum 2(2): 020335 (2021).
7. Shangjie Guo, Amilson R. Fritsch, Craig Greenberg, Ian Spielman, and Justyna P. Zwolak, Machine-learning enhanced dark soliton detection in Bose-Einstein condensates. Mach. Learn.: Sci. Technol. 2(3): 035020 (2021).
8. Justyna P. Zwolak, Sandesh S. Kalantre, Thomas McJunkin, Brian J. Weber, and Jacob M. Taylor, Ray-based classification framework for high-dimensional data. Proceedings of Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada [December 11, 2020] (2020).
9. Justyna P. Zwolak, Thomas McJunkin, Sandesh S. Kalantre, J. P. Dodson, E. R. MacQuarrie, D. E. Savage, M. G. Lagally, S. N. Coppersmith, Mark A. Eriksson, and Jacob M. Taylor, Auto-tuning of double dot devices in situ with machine learning. Phys. Rev. Applied 13(3): 034075 (2020).
   • Featured as Editors’ Suggestion in Physical Review Applied.
   • Featured in NIST News: To Tune Up Your Quantum Computer, Better Call an AI Mechanic: New paradigm for “auto-tuning” quantum bits could overcome major engineering hurdle. (2020).
10. Sandesh S. Kalantre, Justyna P. Zwolak, Stephen Ragole, Xingyao Wu, Neil M. Zimmerman, M. D. Stewart, and Jacob M. Taylor, Machine Learning techniques for state recognition and auto-tuning in quantum dots. npj Quantum Information 5(6): 1–10 (2019).
11. Justyna P. Zwolak, Sandesh S. Kalantre, Xingyao Wu, Stephen Ragole, and Jacob M. Taylor, QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments. PLoS ONE 13 (10): e0205844 (2018).
    • Data is available at data.gov.
    • QFlow lite is available at github.com.
12. Justyna P. Zwolak and Dariusz Chruściński, Recurrent construction of optimal entanglement witnesses for 2N qubit systems. Phys. Rev. A 89 (5): 052314 (2014).
13. Justyna P. Zwolak and Dariusz Chruściński, New tools for investigating positive maps in matrix algebras. Rep. Math. Phys. 71 (2): 163–175 (2013).
14. Spyridon Michalakis and Justyna Pytel, Stability of frustration-free systems. Comm. Math. Phys. 322 (2): 277–302 (2013).
15. Dariusz Chruściński and Justyna Pytel, Optimal entanglement witnesses from generalized reduction and Robertson maps. J. Phys. A: Math. Theor. 44 (16): 165304 (2011).
16. Dariusz Chruściński and Justyna Pytel, Constructing optimal entanglement witnesses. II. Witnessing entanglement in 4N x 4N systems. Phys. Rev. A 82 (5): 052310 (2010).
17. Dariusz Chruściński, Justyna Pytel, and Gniewomir Sarbicki, Constructing new optimal entanglement witnesses. Phys. Rev. A 80 (6): 062314 (2009).

Physics Education Research and Social Studies

1. Robert P. Dalka and Justyna P. Zwolak, Restoring the structure: A modular analysis of ego-driven organizational networks. arXiv:2201.01290 (2022).
2. Robert P. Dalka, Diana Sachmpazidi, Charles Henderson, and Justyna P. Zwolak, Network analysis approach to Likert-style surveys. Phys. Rev. Phys. Educ. Res. 18 (2): 020113 (2022).
3. Eric A. Williams, Justyna P. Zwolak, Remy Dou, and Eric Brewe, Linking engagement and performance: The social network analysis perspective. Phys. Rev. Phys. Educ. Res. 15 (2): 020150 (2019).
4. Remy Dou and Justyna P. Zwolak, Practitioner’s guide to social network analysis: Examining physics anxiety. Phys. Rev. Phys. Educ. Res. 15 (2): 020105 (2019).
   • An invited article within the Quantitative Methods in PER: A Critical Examination Focused Collection.
5. Emily M. Smith, Justyna P. Zwolak, and Corinne A. Manogue, Isolating approaches: How middle-division physics students coordinate forms and representations in complex algebra. Phys. Rev. Phys. Educ. Res. 15 (1): 010138 (2019).
6. C. A. Hass, Florian Genz, Mary Bridget Kustusch, Pierre-P. A. Ouimet, Katarzyna Pomian, Eleanor C. Sayre, and Justyna P. Zwolak, Studying community development: A network analytical approach. Proceedings of the Physics Education Research Conference 2018, Washington, DC [August 1-2, 2018], pp. 1–4 (2019).
7. Remy Dou, Eric Brewe, Geoff Potvin, Justyna P. Zwolak, and Zahra Hazari, Understanding the development of interest and self-efficacy in active-learning undergraduate physics courses. Int. J. Sci. Educ. 40 (13): 1587–1605 (2018).
8. Justyna P. Zwolak, Michael Zwolak, and Eric Brewe, Educational commitment and social networking: The power of informal networks. Phys. Rev. Phys. Educ. Res. 14 (1): 010131 (2018).
   • Featured as Editors’ Suggestion in Physical Review Physics Education Research.
   • Featured as a Research Highlight in Nature Physics: “Friendly persistence”, 14: 528 (2018).
9. Justyna P. Zwolak, Remy Dou, and Eric Brewe, Student perceptions of the value of out-of-class interactions: Attitudes vs. Practice. Proceedings of the Physics Education Research Conference 2017, Cincinnati, OH [July 26-27, 2017], pp. 480–483 (2018).
10. Eric Williams, Justyna P. Zwolak and Eric Brewe, Physics Major Engagement and Persistence: A Phenomenography Interview Study. Proceedings of the Physics Education Research Conference 2017, Cincinnati, OH [July 26-27, 2017], pp. 436–439 (2018).
11. Katarzyna Pomian, Justyna P. Zwolak, Eleanor Sayre, Scott Franklin, and Mary Bridgett Kustusch, Using Social Network Analysis on classroom video data. Proceedings of the Physics Education Research Conference 2017, Cincinnati, OH [July 26-27, 2017], pp. 316–319 (2018).
12. Justyna P. Zwolak, Remy Dou, Eric A. Williams, and Eric Brewe, Students’ network integration as a predictor of persistence in introductory physics courses. Phys. Rev. Phys. Educ. Res. 13 (1): 010113 (2017).
    • Featured as Editor’s Choice in Science: “The physics of social butterflies”, 356: 282 (2017).
    • Featured in FIU News: “To keep students interested in physics, have them interact” (May 15, 2017).
13. Remy Dou, Eric Brewe, Justyna P. Zwolak, Geoff Potvin, Eric A. Williams, and Laird H. Kramer, Beyond performance metrics: Examining a decrease in students’ physics self-efficacy through a social networks lens. Phys. Rev. Phys. Educ. Res. 12 (2): 020124 (2016).
14. Justyna P. Zwolak and Eric Brewe, The impact of social integration on student persistence in introductory Modeling Instruction courses. 2015 Physics Education Research Conference Proceedings, College Park, MD [July 29-30, 2015], pp. 395–398 (2015).
15. Eric Williams, Eric Brewe, Justyna P. Zwolak, and Remy Dou, Understanding centrality: Investigating student outcomes within a classroom social network. 2015 Physics Education Research Conference Proceedings, College Park, MD [July 29-30, 2015], pp. 375–378 (2015).
16. Emily M. Smith, Justyna P. Zwolak, and Corinne A. Manogue, Student difficulties with complex numbers. 2015 Physics Education Research Conference Proceedings, College Park, MD [July 29-30, 2015], pp. 311–314 (2015).
17. Justyna P. Zwolak and Corinne A. Manogue, Assessing student reasoning in upper-division electricity and magnetism at Oregon State University. Phys. Rev. ST Phys. Educ. Res. 11 (2): 020125 (2015).
    • An invited article within the PER in Upper Division Physics Courses Focused Collection.
18. Justyna P. Zwolak and Corinne A. Manogue, Revealing Differences Between Curricula Using the Colorado Upper-Division Electrostatics Diagnostic. 2014 Physics Education Research Conference Proceedings, Minneapolis, MN [July 30-31, 2014], pp. 295–298 (2015).
19. Justyna P. Zwolak, Mary Bridget Kustusch, and Corinne A. Manogue, Re-thinking the Rubric for Grading the CUE. The Superposition Principle. 2013 Physics Education Research Conference Proceedings, Portland, OR [July 17-18, 2013], pp. 385–388 (2014).

Media and Impact

1. Mary Theofanos and Justyna Zwolak (May 16, 2021), Diversity and Inclusivity at NIST. Women in Standards News.
2. Justyna P. Zwolak (June 2, 2020), Ebb and Flow: Creating Quantum Dots Automatically With AI. Taking Measure: Just a Standard Blog.

Other publications

1. Justyna P. Zwolak, Automation of Experimental Quantum Dot Control. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2021, pp. 24–27 (2022).
2. Judith Terrill, Justyna P. Zwolak, James Filliben, and Jeffrey Bullard, HydratiCA, In Situ Analysis, and Machine Learning. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2021, pp.82–83 (2022).
3. Justyna P. Zwolak, I. B. Spielman, Sophia M. Koh, Shangjie Guo, Amilson R. Fritsch, Combining Machine Learning with Physics: Enhanced Dark Soliton Detection in BECs. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2021, pp. 84–85 (2022).
4. Justyna P. Zwolak, Sandesh S. Kalantre, Thomas McJunkin, Brian J. Weber, and Jacob M. Taylor, Ray-based Classification Framework for Quantum Dot Devices. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2021, pp. 86–88 (2022).
5. Justyna P. Zwolak, Zachary J. Grey, Joshua Ziegler, and Brian J. Weber, Charge Field Decomposition and State Identification for Quantum Dot Data. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2021, pp. 88–90 (2022).
6. Joshua Ziegler, Justyna P. Zwolak, Jacob M. Taylor, Thomas McJunkin, Sandesh Kalantre, E. S. Joseph, Benjamin Harpt, D. E. Savage, M. G. Lagally, and Mark A. Eriksson, Noisy Quantum Dot Devices. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2021, pp. 90–92 (2022).
7. Joshua Ziegler and Justyna P. Zwolak, Physics-driven Tuning of Quantum Dot Charge States. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2021, pp. 92–93 (2022).
8. Justyna P. Zwolak, Mary F. Theofanos, Jasmine Evans, and Sandra Spickard Prettyman, Gender, Equity, and Inclusion Survey Study at NIST. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2021, pp. 134–135 (2022).
9. Justyna P. Zwolak, Laura Espinal, and Camila Young, Mapping and Analyzing Employee Networks through the NIST Interactions Survey. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2021, pp. 135–136 (2022).
10. Robert P. Dalka and Justyna P. Zwolak, Restoring Organizational Structure Using Projected Ego-Centric Networks. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2021, pp. 136–137 (2022).
11. Robert P. Dalka, Justyna P. Zwolak, Diana Sachmpazidi, and Charles Henderson, Physics Education Survey Validation Through a Network Analytic Approach. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2021, pp. 137–138 (2022).
12. Laura Espinal, Camila Young, and Justyna P. Zwolak, Mapping employee networks through the NIST Interactions Survey. Natl. Inst. Stand. Technol. Interag. Intern. Rep. 8375 (2021).
13. Judith Terrill, Justyna P. Zwolak, James Filliben, and Jeffrey Bullard, HydratiCA, In Situ Analysis and Machine Learning. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2020, pp. 78–79 (2021).
14. Justyna P. Zwolak, Sandesh S. Kalantre, Thomas McJunkin, Brian J. Weber, and Jacob M. Taylor, Ray-based classification framework for quantum dot devices. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2020, pp. 79–81 (2021).
15. Zachary J. Grey, Justyna P. Zwolak, Andrew M. Dienstfrey, Sandesh S. Kalantre, and Brian J. Weber, Ray-Tracing Active Subspace Computations (R-TASC) for Quantum Dot Decompositions. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2020, pp. 81–83 (2021).
16. Justyna P. Zwolak, Shangjie Guo, Amilson R. Fritsch, Craig Greenberg, and Ian B. Spielman, Machine learning enhanced dark solitons detection in Bose-Einstein condensates. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2020, pp. 83–84 (2021).
17. Joshua Ziegler, Justyna P. Zwolak, Jacob M. Taylor, Sandesh Kalantre, Thomas Mcjunkin, and Mark A. Eriksson, Towards Robust Autotuning of Noisy Quantum Dot Devices. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2020, pp. 101–102 (2021).
18. Justyna P. Zwolak, Mary F. Theofanos, Jasmine Evans, and Sandra Spickard Prettyman, Gender, Equity and Inclusion Survey Study at the National Institute of Standards and Technology. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2020, pp. 129–130 (2021).
19. Justyna P. Zwolak, Laura Espinal, and Camila Young, Mapping and analyzing employee networks through the NIST Interactions Survey. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2020, pp. 130–131 (2021).
20. Mary F. Theofanos, Jasmine Evans, Justyna P. Zwolak, and Sandra Spickard Prettyman, Survey on Gender, Equity and Inclusion. Natl. Inst. Stand. Technol. Interag. Intern. Rep. 8362 (2021).
21. Justyna P. Zwolak, Machine Learning for Experimental Quantum Dot Control. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2019, pp. 25–28 (2020).
22. Judith Terrill, Justyna P. Zwolak, James Filliben, and Jeffrey Bullard, HydratiCA, In Situ Analysis and Machine Learning. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2019, pp. 77–78 (2020).
23. Justyna P. Zwolak, Justin Elenewski, Amilson R. Fritsch, and Ian B. Spielman, Machine Learning Enhanced Dark Soliton Detection in Bose-Einstein Condensates. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2019, pp. 80–81 (2020).
24. Justyna P. Zwolak and Remy Dou, Practitioner’s Guide to Social Network Analysis for Education Researchers. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2019, pp. 119–120 (2020).
25. Justyna P. Zwolak, Judith Terrill, and Aleksandra Słapik, Computations in Physics: A Quest to Integrate Computer Methods in STEM Courses. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2019, pp. 120 (2020).
26. Justyna P. Zwolak, Machine Learning for Experimental Quantum Dot Control. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2018 », pp. 18–21 (2019).
27. Sandesh S. Kalantre, Justyna P. Zwolak, Stephen Ragole, Xingyao Wu, Neil M. Zimmerman, M. D. Stewart, and Jacob M. Taylor, Machine Learning Approach to Quantum Dot Experiments. Applied and Computational Mathematics Division: Summary of Activities for Fiscal Year 2017 », pp. 87–88 (2018).