CV

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Education

• B.S. in Electrical Engineering, University of Southern California, 2015
• M.S. in Electrical and Computer Engineering, University of Wisconsin–Madison, 2018
• Ph.D in Electrical and Computer Engineering, University of Wisconsin–Madison, 2020

Selected Research and Industry Experience

• September, 2021: Rice University
  • Role: Postdoctoral Research Associate
  • Supervisor: Richard Baraniuk
  • Projects (Ongoing):
    • Connecting membership inference to overparamterization
    • Active learning for knowledge graphs for intelligent textbooks
• August, 2015 - August 2021: University of Wisconsin–Madison
  • Role: Graduate Research Assistant
  • Advisor: Robert Nowak
  • Projects:
    • Bandit algorithms for level set estimation
    • Nearest neighbor algorithms with noisy data
    • Theoretical guatantees for low-dimensional metric learning
    • Metric learning for interactive online education
    • Fast algorithms for ordinal embedding
• June, 2018 - August, 2018: Amazon Music
  • Role: Applied scientist intern
  • Project:
    • Redesigning the song-prediction codebase to reduce size and computational burden
    • Developed protoype of new method to predict song preference, driving new artist discovery
• May, 2014 - May, 2015: The Aerospace Corporation
  • Role: Engineering Intern
  • Advisor: Adam Bushmaker
  • Project: Recording audio signals from carbon nanotube transistors

Skills

• Machine learning
  • Multi-armed bandits
  • Active learning
  • Metric learning
  • Level set estimation
• Python
  • NumPy
  • Pandas
  • SciPy
  • Pytorch
• Latex
• MatLab

Publications

Thermoacoustic Transduction in Individual Suspended Carbon Nanotubes

Mason, B. J., Chang, S. W., Chen, J., Cronin, S. B., & Bushmaker, A. W. (2015). Thermoacoustic transduction in individual suspended carbon nanotubes. ACS nano, 9(5), 5372-5376.

How to Model Implicit Knowledge? Similarity Learning Methods to Assess Perceptions of Visual Representations

Rau, M. A., Mason, B., & Nowak, R. (2016). How to Model Implicit Knowledge? Similarity Learning Methods to Assess Perceptions of Visual Representations. International Educational Data Mining Society.

Learning Low Dimensional Metrics

Mason, B., Jain, L., & Nowak, R. (2017). Learning low-dimensional metrics. Advances in neural information processing systems, 30.

For Teaching Perceptual Fluency, Machines Beat Human Experts.

Sen, A., Patel, P., Rau, M. A., Mason, B., Nowak, R., Rogers, T. T., & Zhu, J. (2018, January). For Teaching Perceptual Fluency, Machines Beat Human Experts. In CogSci

Socioeconomic Variability in Human Fecundity

Nobles, J., Hamoudi, A., Nowak, R., Landau, E., Baron, A., Brittingham, J., & Mason, B. (2018, April). Socioeconomic Variability in Human Fecundity. In PAA 2018 Annual Meeting. PAA.

Machine Beats Human at Sequencing Visuals for Perceptual-Fluency Practice.

Sen, A., Patel, P., Rau, M. A., Mason, B., Nowak, R., Rogers, T. T., & Zhu, X. (2018). Machine Beats Human at Sequencing Visuals for Perceptual-Fluency Practice. International Educational Data Mining Society.

Cognitive Task Analysis for Implicit Knowledge About Visual Representations With Similarity Learning Methods

Mason, B., Rau, M. A., & Nowak, R. (2019). Cognitive Task Analysis for Implicit Knowledge About Visual Representations With Similarity Learning Methods. Cognitive science, 43(9), e12744.

Learning nearest neighbor graphs from noisy distance samples

Mason, B., Tripathy, A., & Nowak, R. (2019). Learning nearest neighbor graphs from noisy distance samples. Advances in Neural Information Processing Systems, 32.

Finding All epsilon-Good Arms in Stochastic Bandits

Mason, B., Jain, L., Tripathy, A., & Nowak, R. (2020). Finding all $\epsilon $-good arms in stochastic bandits. Advances in Neural Information Processing Systems, 33, 20707-20718.

Improved confidence bounds for the linear logistic model and applications to bandits

Jun, K. S., Jain, L., Mason, B., & Nassif, H. (2021, July). Improved confidence bounds for the linear logistic model and applications to bandits. In International Conference on Machine Learning (pp. 5148-5157). PMLR.

NFT-K: Non-Fungible Tangent Kernels

Alemohammad, S., Babaei, H., Barberan, C. J., Liu, N., Luzi, L., Mason, B., & Baraniuk, R. G. (2021). NFT-K: Non-Fungible Tangent Kernels. arXiv preprint arXiv:2110.04945.

Nearest neighbor search under uncertainty

Mason, B., Tripathy, A., & Nowak, R. (2021, December). Nearest neighbor search under uncertainty. In Uncertainty in Artificial Intelligence (pp. 1777-1786). PMLR.

Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers

Katz-Samuels, J., Mason, B., Jamieson, K. G., & Nowak, R. (2021). Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers. Advances in Neural Information Processing Systems, 34.

Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference

Tan, J., Mason, B., Javadi, H., & Baraniuk, R. G. (2022). Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference. arXiv preprint arXiv:2202.01243.

Nearly Optimal Algorithms for Level Set Estimation

Mason, B., Camilleri, R., Mukherjee, S., Jamieson, K., Nowak, R., & Jain, L. (2021). Nearly Optimal Algorithms for Level Set Estimation. arXiv preprint arXiv:2111.01768. (To appear at AIStats, 2022)

Service and leadership

• September, 2017 - September 2018: GODDESSES seminar
  • Founded and led a graduate student only Optimization and Machine Learning speaker series where students can practice scientific communication skills and get feedback on research ideas.
• August, 2012 - May, 2015: Moneythink
  • I was the president of the chapter of Moneythink at the University of Southern California. Through Moneythink, I taught financial literacy in Los Angeles high schools and oversaw the teaching of over 400 students as president.