Mathematician
Mathematician

Biography

Bill Kay is a mathematician whose research focuses on graph theory, hypergraphs, probabilistic combinatorics, topology/geometry, and algorithmic design. He enjoys mathematics ("Theorem-Proof" results) and applications of math to domain science (particularly computer science, network science, and data science), and at Pacific Northwest National Laboratory, he has made it a goal to maintain a workload that is a healthy balance between the two.

His work in the applied space has been published in journals and featured at conferences and workshops, such as the following:

  • Nature: Scientific Reports (hypergraphs and information theory)
  • Nature: Computational Science (neuromorphic computing)
  • IEEE Big Data (critical infrastructure via network analysis)
  • ICML Subset Selection (subset selection via extreme points/discrete geometry—Spotlight Presentation)
  • UKCI (asynchronous evolutionary algorithms—Best Paper)

His background in combinatorial math has resulted in several publications, including in the following journals:

  • The Journal of Combinatorial Theory, Series B (graph coloring and graph homomorphisms)
  • Combinatorics, Probability, and Computing (extremal hypergraphs and probabilistic combinatorics)

Bill is a very collaborative researcher and always enjoys conversations in which new ideas are bounced around. He very much likes hearing about different research domains and talking about which parts of math theory come to mind in an effort to make new connections and steer empirical approaches with theoretical heuristics and underpinnings.

He received both a PhD in mathematics and an MS in computer science from Emory University under co-advisers Vojtech Rodl and Dwight Duffus. He held a visiting assistant professorship position at Emory University in 2017 to 2018 and served as a postdoctoral research fellow, hosted jointly by Ryerson University and The Fields Institute (adviser Anthony Bonato), during 2018 to 2019. From 2019 to 2021, he served as a data scientist (post doc, then staff) at Oak Ridge National Lab (supervisor Ramki Kanan), first in the Computational Data Analytics Research Group and then in the Conputational AI and ML Group. He is currently a mathematician in the Algorithms, Combinatorics, and Optimization team supervised by Stephen Young.

Disciplines and Skills

  • Algorithms
  • Artificial intelligence
  • Data reduction
  • Data science
  • Discrete geometry
  • Discrete mathematics
  • Graph theory
  • Hypergraph theory
  • Machine learning
  • Machine learning algorithms
  • Probability
  • Randomized algorithms
  • Spectral analysis
  • Theory of computation
  • Topology

Education

PhD in Mathematics, Emory University

MS in Computer Science, Emory University

MS in Mathematics, University of South Carolina

BS in Mathematics, University of South Carolina

Publications

2024

Verma, Miki E, et al. “A comprehensive guide to CAN IDS data and introduction of the ROAD dataset.” PloS one vol. 19,1 e0296879. 22 Jan. 2024, https://doi.org/10.1371/journal.pone.0296879.

Kritschgau, Jürgen, et al. "Community detection in hypergraphs via mutual information maximization." Scientific Reports, vol. 14, no. 1, Mar. 2024. https://doi.org/10.1038/s41598-024-55934-5.

2023

Nielson, F. F., Kay, B., Young, S. J., Colby, S. M., Renslow, R. S., & Metz, T. O. (2023). Correction: Nielson et al. Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics. Metabolites 2023, 13, 105. Metabolites, 13(11), 1158. https://doi.org/10.3390/metabo13111158.

A. Myers, et al., "Malicious Cyber Activity Detection using Zigzag Persistence," 2023 IEEE Conference on Dependable and Secure Computing (DSC), Tampa, FL, USA, 2023, pp. 1-8. https://doi.org/10.1109/DSC61021.2023.10354204.

Aksoy, Sinan G., et al. "Seven open problems in applied combinatorics." Journal of Combinatorics, vol. 14, no. 4, Apr. 2023. https://doi.org/10.4310/joc.2023.v14.n4.a8.

Nielson, Felicity F., et al. "Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics." Metabolites, vol. 13, no. 1, Jan. 2023. https://doi.org/10.3390/metabo13010105.

2022

Schuman, C.D., Kulkarni, S.R., Parsa, M. et al. Publisher Correction: Opportunities for neuromorphic computing algorithms and applications. Nat Comput Sci 2, 205 (2022). https://doi.org/10.1038/s43588-022-00223-2.

Aimone, James B., et al. "A review of non-cognitive applications for neuromorphic computing." Neuromorphic Computing and Engineering, vol. 2, no. 3, Sep. 2022. https://doi.org/10.1088/2634-4386/ac889c.

Scott, Eric O., et al. "Avoiding excess computation in asynchronous evolutionary algorithms." Expert Systems, vol. 40, no. 5, Aug. 2022. https://doi.org/10.1111/exsy.13100.

Godbole, Anant, et al. "Threshold progressions in covering and packing contexts." Journal of Combinatorics, vol. 13, no. 3, Mar. 2022. https://doi.org/10.4310/joc.2022.v13.n3.a1.

Aksoy, Sinan G., et al. "Models and Methods for Sparse (Hyper)Network Science in Business, Industry, and Government." Notices of the American Mathematical Society, vol. 69, no. 02, Feb. 2022. https://doi.org/10.1090/noti2424.

Schuman, Catherine D., et al. "Opportunities for neuromorphic computing algorithms and applications." Nature Computational Science, vol. 2, no. 1, Jan. 2022. https://doi.org/10.1038/s43588-021-00184-y.

2021

Bonato, Anthony, et al. "Improved Bounds for Burning Fence Graphs." Graphs and combinatorics, vol. 37, no. 6, Aug. 2021. https://doi.org/10.1007/s00373-021-02390-x.

2020

Bonato, Anthony, et al. "The iterated local model for social networks." Discrete Applied Mathematics, vol. 284, no. C, May. 2020. https://doi.org/10.1016/j.dam.2020.04.018.

2019

Arman, A.; Kay, B.; Rödl, V. “A note on weak delta systems.” Discrete Mathematics, vol. 342, no. 11, 2019. https://doi.org/10.1016/j.disc.2019.06.013.