Applied Mathematics PhD Candidate - Data Science & ML Intern Candidate

Chi-An Chen

Profile

PhD Student, Applied Mathematics, Illinois Institute of Technology

********@*****.*** GitHub: https://github.com/er10ic24-ui Chicago, IL Summary

PhD student in Applied Mathematics with strong experience in machine learning, optimization, and data-driven modeling. Hands-on experience with regression, clustering, and neural-network–based models using Python and PyTorch. Interested in Applied Scientist, Research Scientist, and Data Scientist internship roles involving modeling, experimentation, and data analysis.Education

Technical Skills

• Programming: Python, PyTorch, NumPy, SciPy, Matplotlib, Seaborn

• Machine Learning:Regression, PCA, K-means clustering, Neural Networks, Physics-Informed Neural Networks

• Data & Modeling: Statistical learning, model evaluation, numerical simulation

• Optimization : Gradient-Based Optimization, Convex Optimization, Adaptive Loss Balancing, Research Experience

Research Assistant — National Center for Theoretical Sciences (NCTS), Taiwan 2021–2022

• Modeled Poisson–Nernst–Planck equations with steric effects.

• Solved PDEs using spectral numerical methods and analyzed solution behavior. PhD Research — Illinois Institute of Technology

2022–present

• Developed particle-based models for deterministic and stochastic Keller–Segel systems.

• Learned interaction kernels from trajectory data and validated reconstructed dynamics.

• Conducted numerical experiments and performance comparisons across models. Education

PhD in Applied Mathematics — Illinois Institute of Technology, Chicago, IL M.S. in Applied Mathematics — National Taiwan University B.S. in Materials Science and Engineering — National Taiwan UniversityPhD in Applied Mathematics Illinois Institute of Technology, Chicago, IL — 2022—present Publications (selected)

• Chen, C.-A., Liu, C., Zhong, M. Unified Learning of the Profile Function in Discrete Keller–Segel Models, arXiv:2510.23381.

• Lu, Y., Chen, C.-A., Li, X., Liu, C. Finite Difference Approximation with ADI Scheme for Two-Dimensional Keller–Segel Equations, Comm. Comput. Phys., 2023.

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