|Office ||MS 3975
|Office Hours ||N/A
|E-Mail ||bjarman (at) math (dot) ucla (dot) edu
- About Me
- I am a fourth year PhD student 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
- 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.
- 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.
- Self-supervised learning for computer vision.
- Preprints & Publications
- "Online Signal Recovery via Heavy Ball Kaczmarz" by B. Jarman, Y. Yaniv, D. Needell. Submitted, 2022.
- "Quantile Averaged Block Kaczmarz for Corrupted Systems of Linear Equations" by L. Cheng, B. Jarman, D. Needell, L. Rebrova. In preparation, 2022.
- "Persistent Homology for Resource Coverage: A Case Study of Access to Polling Sites" by A. Hickok, B. Jarman, M. Johnson, J. Luo, M. Porter. Preprint, 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 2022, 15(5), 136. arXiv
- "Paving the Way for Consensus: Convergence of Block Gossip Algorithms" by J. Haddock, B. Jarman, C. Yap. Submitted, 2021. arXiv
- "Randomized Extended Kaczmarz is a Limit Point of Sketch-and-Project" by B. Jarman, N. Mankovich, J.D. Moorman. Submitted, 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