Joyce A. Chew

Joyce A. Chew is an Assistant Professor of Mathematics & Statistics at Calvin University. She holds a PhD in Mathematics from UCLA (2025) and dual bachelor’s degrees in Mathematics and Chemistry from Calvin University (2020). Her research focuses on geometric deep learning, low-rank matrix/tensor approximation, and mitigating bias in machine learning. She has contributed to manifold-based data analysis, scattering transforms, and non-negative matrix factorization techniques. Her work bridges mathematics, data science, and applications in single-cell genomics and chemometrics. Chew has received prestigious awards including the NSF Graduate Research Fellowship and the UCLA Raymond Redheffer Prize. She actively mentors undergraduate researchers and participates in initiatives like UCLA’s Collaborative Undergraduate Research Experience.

Education:

• PhD in Mathematics, UCLA, 2025 (Thesis: Topics in Geometric Deep Learning and Learning on Manifolds)
• C.Phil. & MA in Mathematics, UCLA, 2023 & 2022
• B.S. (Honors) Mathematics & B.A. Chemistry, Calvin University, 2020

Research Interests:

Her work emphasizes geometric structures in data science, including manifold learning, neural network architectures for non-Euclidean data, and algorithmic fairness. She develops methods for analyzing high-dimensional datasets (e.g., point clouds) using diffusion maps and scattering transforms. Recent projects address bias in word embeddings and robust uncertainty quantification in spectroscopic analysis.

Awards:

• Pacific Journal of Mathematics Dissertation Award (2025)
• SIAM Student Travel Award (2024)
• UCLA Raymond Redheffer Prize (2022)
• NSF Graduate Research Fellowship (2020)

Mentoring & Grants:

Chew leads UCLA/Los Angeles Pierce College Collaborative Undergraduate Research Experience (2023-2024) and directs the UCLA CAM REU (2021-2022). She has advised projects in geometric deep learning and chemometric modeling.

Labs/Teams:

Collaborates with groups at UCLA (Deanna Needell’s lab), Stanford (Smita Krishnaswamy), and institutions globally on manifold-based data science initiatives.