A Convergence Rate for Manifold Neural Networks
Chew, J.A.; Perlmutter, M.; Needell, D. A Convergence Rate for Manifold Neural Networks. arXiv preprint, 2022.
CV
Click here to download full CV (last updated: September 2022).
Education
- Ph.D. in Applied Mathematics, University of California, Los Angeles, 2025 (expected)
- Area Exams (passed Spring 2021):
- Numerical Analysis
- Optimization/Numerical Linear Algebra
- Area Exams (passed Spring 2021):
- M.A. in Mathematics, University of California, Los Angeles, 2022
- B.S. in Mathematics (Honors), Calvin University, 2020
- B.A. in Chemistry, Calvin University, 2020
Research experience
- June 2021 - present: Graduate Student Researcher
- University of California, Los Angeles Department of Mathematics
- Supervisor: Prof. Deanna Needell
- Research interests:
- Numerical linear algebra
- Stochastic iterative methods
- Graph and manifold learning
- June 2019 - August 2019: NSF REU
- Cornell University Department of Mathematics
- Supervisor: Dr. Andy Borum
- Studied the equilibrium configurations of flexible helical springs using optimal control. Developed numerical methods to find unstable configurations and demonstrated the presence of saddle-node bifurcations in the equilibrium configurations of twisted springs.
- May 2018 - September 2020: Undergraduate Researcher
- Calvin University Department of Chemistry and Biochemistry
- Supervisor: Prof. Douglas A. Vander Griend
- Projects:
- Development of a parallelized, high-throughput website for equilibrium binding analysis: Applied several classes of optimization algorithms to the simultaneous determination of reaction stoichiometry and binding constants of equilibrium systems of host-guest binding. Evaluated speed, convergence, and accuracy to devise a new hybrid algorithm. Currently implementing this new methodology in a supercomputer software package to facilitate fast, high-throughput analysis via a website interface.
- Mathematical and computational analysis of 1:1 equilibrium binding: Wrote Monte Carlo simulations to determine the robustness of global analysis for determination of binding constants of 1:1 host-guest equilibrium binding. Numerically demonstrated the benefits of using mathematically-derived optimal parameters for the titration experiment.
- May 2017 - August 2017: Undergraduate Researcher
- Calvin University Department of Mathematics and Statistics
- Supervisor: Prof. James Turner
- Worked towards a new proof of the 3-variable Moreno-Socias conjecture to prove the generalized conjecture. Used computer algebra systems to compute Grobner bases of generic ideals. Formulated a conjecture characterizing generic Grobner bases and proved the 2-variable case.
Publications
Multi-scale Hybridized Topic Modeling: A Pipeline for Analyzing Unstructured Text Datasets using Topic Modeling
Cheng, K; Inzer, S.; Leung, A.; Shen, X.; Perlmutter, M.; Lindstrom, M.; Chew, J.A.; Presner, T.; Needell, D. Multi-scale Hybridized Topic Modeling: A Pipeline for Analyzing Unstructured Text Datasets using Topic Modeling. Submitted for publication, 2022.
Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data
Kazmierczak, N.P.; Chew, J.A.; Vander Griend, D.A. Bootstrap methods for quantifying the uncertainty of binding constants in the hard modeling of spectrophotometric titration data. Analytica Chimica Acta, 2022, 1227:339834.
Geometric Scattering on Measure Spaces
Chew, J.A.; Hirn, M.; Krishnaswamy, S.; Needell, D.; Perlmutter, M.; Steach, H.R.; Viswanath, S.; Wu, H.-T. Geometric Scattering on Measure Spaces. Submitted for publication, 2022.
The Manifold Scattering Transform for High-Dimensional Point Cloud Data
Chew, J.A.; Steach, H.R.; Viswanath, S.; Wu, H.-T.; Hirn, M.; Needell, D.; Krishnaswamy, S.; Perlmutter, M. The Manifold Scattering Transform for High-Dimensional Point Cloud Data. arXiv preprint, 2022.
A reliable algorithm for calculating stoichiometry parameters in the hard modeling of spectrophotometric titration data
Kazmierczak, N.P.; Chew, J.A.; Vander Griend, D.A. A reliable algorithm for calculating stoichiometry parameters in the hard modeling of spectrophotometric titration data. Journal of Chemometrics, 2022, 36:e3409.
Guided Semi-Supervised Non-Negative Matrix Factorization
Li, P.; Tseng, C.; Zheng, Y.; Chew, J.A.; Huang, L.; Jarman, B.; Needell, D. Guided Semi-Supervised Non-Negative Matrix Factorization. Algorithms 2022, 15:136
Sensitivity Limits for Determining 1:1 Binding Constants from Spectrophotometric Titrations via Global Analysis
Kazmierczak, N.P.; Chew, J.A.; Michmerhuizen, A.R.; Kim, S.E.; Drees, Z.D.; Rylaarsdam, A.; Thong, T.; Van Laar, L.; Vander Griend, D.A. Sensitivity Limits for Determining 1:1 Binding Constants from Spectrophotometric Titrations via Global Analysis. Journal of Chemometrics, 2019, 33:e3119.
Talks
Geometric Scattering on Non-Euclidean Data
Conference talk at SIAM MDS 2022, San Diego, CA
NSF GRFP Workshop
Workshop at UCLA Department of Mathematics, Los Angeles, CA
Tension Induced Instabilities of Twisted Springs
Talk at Cornell University Undergraduate Research Forum, Ithaca, NY
Defining the Relationship: Computer-Driven Characterization of the Binding of Host and Guest Molecules
Poster at Joint Mathematics Meetings, Baltimore, MD
The Tie that Binds: Optimal Design of Equilibrium Spectrophotometric Titrations
Poster at West Michigan Regional Undergraduate Science Conference, Grand Rapids, MI
Characterizing the Grobner Bases of Generic Ideals
Poster at West Michigan Regional Undergraduate Science Conference, Grand Rapids, MI
Cake and What I Learned From Cutting It
Talk at TEDx Valencia High School, Placentia, CA
Awards
- Raymond Redheffer Prize (recognizes graduate student going above and beyond who taught freshman calculus course), 2022
- NSF Graduate Research Fellowship, 2020
- NCWIT Collegiate Award Finalist, 2019
- Goldwater Scholarship Honorable Mention, 2018
- Calvin Student Research Fellowship, 2016
Mentoring
Click here for more information about each of these programs.
- UCLA CAM REU (2021, 2022)
- UCLA Women in Mathematics (2021-present)
- UCLA Directed Reading Program (2021-2022)