Programming: Python, C++, Bash, Git, open source development
Research: graph search, network analysis, mathematical optimization, numerical linear algebra, matrix factorization

• Developed top performing subgraph matching algorithms for the DARPA Modeling Adversarial Activity program.
• Extended subgraph matching algorithms to knowledge graphs and noisy/inexact data.
• Showed that adaptive variants of stochastic gradient descent (SGD) enjoy accelerated convergence.
• Created open-source Python packages for subgraph matching, optimization algorithms, and more.
• Led special interest groups in SGD, matrix and tensor factorization, and Python package development.
• Completed courses in numerical analysis, statistics, optimization, and machine learning.

• Created a calibration procedure for dynamic multi-sensor systems.
• Integrated the calibration procedure into a hands-off system of sensors.
• Established benchmark tests to reliably measure calibration accuracy.

• Developed search algorithms for NovaGuide, an automatic cerebral ultrasound robot.
• Created cerebral bloodflow simulations to reduce the need to physically scan people when testing algorithms.

• Built an interactive web application (Surveyor Web) to view full historical order book market data.
• Created an automatic outlier detection system to find anomalous market events.

• Developed algorithms to simultaneously identify and track acoustic sources in passive sonar applications.
• Wrote C++ and MATLAB simulations to generate benchmark datasets for the algorithms.

• Designed game mechanics and prototyped levels for a video game published on Steam.

• Jacob D. Moorman, Qinyi Chen, Thomas K. Tu, Xie He, Andrea L. Bertozzi, “Subgraph Matching on Multiplex Networks." IEEE Transactions on Network Science and Engineering, to Appear, 2021.
• Robert M. Gower, Denali Molitor, Jacob D. Moorman, Deanna Needell, “Adaptive Sketch-and-Project Methods for Solving Linear Systems." SIAM Journal on Matrix Analysis and Applications, to Appear, 2021.
• Jacob D. Moorman, Thomas K. Tu, Denali Molitor, Deanna Needell, “Randomized Kaczmarz with Averaging." BIT Numerical Mathematics 61, pp. 337–359, 2021.

• Thomas K. Tu, Jacob D. Moorman, Dominic Yang, Qinyi Chen, Andrea L. Bertozzi, “Inexact Attributed Subgraph Matching." 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, pp. 2575-2582, 2020.
• Zhaojun Nie, Michael O’Brien, Mina Ranjbaran, Jacob D. Moorman, Nic Canac, Shankar Radhakrishnan, Zsolt Garami, Robert Hamilton, “Neural Echo Simulator (NES) for Real-Time Simulation of Transcranial Doppler Ultrasound (TCD) Signal Responses of Cerebral Hemodynamics From High-Resolution 3D Imaging Head-Models." Proc. 24th Meeting of the Euro. Soc. of Neurosonology and Cerebral Hemodynamics, Linz, Austria, Apr. 2019.
• Jacob D. Moorman, Thomas K. Tu, Denali Molitor, Deanna Needell, “Randomized Kaczmarz with Averaging." Information Theory and Applications Workshop, La Jolla, CA, Feb. 2019.
• Jacob D. Moorman, Qinyi Chen, Thomas K. Tu, Zachary M. Boyd, Andrea L. Bertozzi, “Filtering Methods for Subgraph Matching on Multiplex Networks." 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, pp. 3980-3985, 2018.

• 2018-2019 MENTOR NSF Research Traineeship $34,000
• 2020-2021 UCLA Dissertation Year Fellowship $20,000

• Elsevier Applied Mathematics and Computation
• Elsevier Journal of Computational and Applied Mathematics
• Elsevier Linear Algebra and its Applications
• IEEE Big Data GTA³ 4.0 Workshop (Program Committee Member)
• Journal of Open Source Software
• Linear and Multilinear Algebra
• SIAM Journal on Matrix Analysis and Applications
• SIAM Journal on Scientific Computing
• Springer Calcolo
• Springer Numerical Algorithms

• Math 174E: Mathematics of Finance (S'18)
• Math 171: Stochastic Processes (S'18, W'18, F'17)
• Math 155: Mathematical Imaging (W'18)
• Math 142: Mathematical Modeling (F'17)
• Math 170B: Probability Theory (S'17)
• Math 170A: Probability Theory (F'16)