Office | MS 7630 |

Office Hours | Weds 10-11am, Thurs 2-4pm |

bjarman (at) math (dot) ucla (dot) edu |

- About Me
- 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