I am a fifth year PhD candidate in the Department of Mathematics, advised by Professor Deanna Needell. I hold BA, MA, and MMath degrees from the University of Cambridge (Trinity College), and MA and CPhil degrees from UCLA. I'm British, and outside of math I'm a huge fan of fitness, the great outdoors, and the Los Angeles Dodgers.
Current Teaching
Winter 2023: Math 170E with Prof. Justin Forlano
All Teaching
I love teaching math and I strive to create an engaging, inclusive, and supportive classroom. You can see a sample of my evaluations here.
I was honored to receive one of the inaugural Liggett Teaching Fellow awards in 2021.
Winter 2023: Math 170E with Prof. Justin Forlano
Fall 2022: Math 118 with Prof. Lara Kassab
Spring 2022: Math 170E with Prof. Tyler Arant
Winter 2022: Math 170E with Prof. Dmitrii Pedchenko
Fall 2021: Math 118 with Prof. Daniel McKenzie
Spring 2021: Math 170S with Prof. Tyler Arant
Winter 2021: Math 170E with Prof. Sangchul Lee, Math 170S with Prof. Hanbaek Lyu
Fall 2020: Math 31B with Prof. Adam Moreno, Math 33A with Prof. Rose Morris-Wright
Summer 2020: Math 3C with Prof. Paige Greene
Spring 2020: Math 3B with Prof. Paige Greene, Math 33A with Robbie Housden
Winter 2020: Math 3C with Prof. March Boedihardjo, Math 33A with Prof. Oleg Gleizer
Fall 2019: Math 3A with Prof. Marcus Roper, Math 3C with Prof. March Boedihardjo
Summer 2019: Math 170B with Prof. Hanbaek Lyu
Spring 2019: Math 3B with Prof. Paige Greene
Winter 2019: Math 170A with Prof. Allen Gehret
Fall 2018: Math 3B with Prof. Noah White
Research Interests
Broadly, I am interested in the mathematics of data theory and machine learning. I have a strong background in probability theory and analysis, and enjoy finding applications of these in data science. Some topics I have researched include:
Variants of the randomized Kaczmarz method for corrupted, noisy linear systems.
Kaczmarz methods with random or streamed measurement data.
Gossip algorithms for the average consensus problem and their connection to the block randomized Kaczmarz method.
Topological data analysis for network converage problems.
ML tasks for modewise-compressed tensor data.
Preprints & Publications
"Online Signal Recovery via Heavy Ball Kaczmarz" by B. Jarman, Y. Yaniv, D. Needell. Proc. 56th Asilomar Conf. on Signals, Systems and Computers, 2022. arXiv
"On Block Accelerations of Quantile Randomized Kaczmarz for Corrupted Systems of Linear Equations" by L. Cheng, B. Jarman, D. Needell, L. Rebrova. Inverse Problems 39(2), 2022. arXiv
"Persistent Homology for Resource Coverage: A Case Study of Access to Polling Sites" by A. Hickok, B. Jarman, M. Johnson, J. Luo, M. Porter. Submitted, 2022. arXiv
"Guided Semi-Supervised Non-negative Matrix Factorization" by P. Li, C. Tseng, Y.Zheng, J.A. Chew, L. Huang, B. Jarman, D. Needell. Algorithms 15(5), 2022. arXiv
"Paving the Way for Consensus: Convergence of Block Gossip Algorithms" by J. Haddock, B. Jarman, C. Yap. IEEE Transactions on Information Theory 68(11), 2022. arXiv
"Randomized Extended Kaczmarz is a Limit Point of Sketch-and-Project" by B. Jarman, N. Mankovich, J.D. Moorman. Preprint, 2021. arXiv
"QuantileRK: Solving large-scale linear systems with corrupted, noisy measurement data" by B. Jarman, D. Needell. Proc. 55th Asilomar Conf. on Signals, Systems and Computers, 2021. arXiv
