Benjamin Leinwand (bleinwan)

Benjamin Leinwand

Assistant Professor

Charles V. Schaefer, Jr. School of Engineering and Science

Department of Mathematical Sciences

North Building 327

Education

  • PhD (2022) University of North Carolina at Chapel Hill (Statistics and Operations Research)
  • BA (2013) Cornell University (Statistical Science and Economics)

Research

My work lies at the interface of statistics and network science. Applications include neuroscience, social networks, politics.

Institutional Service

  • Department of Mathematical Sciences Faculty Candidate Interviewer Member

Professional Societies

  • Network Science Society Member

Selected Publications

Conference Proceeding

  1. Leinwand, B.; Wu, G.; Pipiras, V. (2020). Characterizing Frequency-Selective Network Vulnerability for Alzheimer's Disease by Identifying Critical Harmonic Patterns. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE.
    http://dx.doi.org/10.1109/isbi45749.2020.9098324.

Journal Article

  1. Leinwand, B.; Pipiras, V.. Block dense weighted networks with augmented degree correction. Network Science (pp. 1-21). Cambridge University Press (CUP).
    http://dx.doi.org/10.1017/nws.2022.23.
  2. Baek, C.; Leinwand, B. N.; Lindquist, K. A.; Jeong, S.; Hopfinger, J.; Gates, K. M.; Pipiras, V. (2023). Detecting Changes in Correlation Networks with Application to Functional Connectivity of fMRI Data. Psychometrika (pp. 1 - 20).
    https://link.springer.com/article/10.1007/s11336-023-09908-7.
  3. Leinwand, B.; Ge, P.; Kulkarni, V.; Smith, R. (2021). Winning an election, not a popularity contest. Significance (4 ed., vol. 18, pp. 24-29). Wiley.
    http://dx.doi.org/10.1111/1740-9713.01549.
  4. Baek, C.; Gates, K. M.; Leinwand, B.; Pipiras, V. (2021). Two sample tests for high-dimensional autocovariances. Computational Statistics & Data Analysis (vol. 153, pp. 107067). Elsevier BV.
    http://dx.doi.org/10.1016/j.csda.2020.107067.

Courses

MA 331: Intermediate Statistics
MA 540: Introduction to Probability Theory