Teaching Assistant at Caltech
ACM/IDS 154: Inverse Problems and Data Assimilation
Models in applied mathematics often have input parameters that are uncertain; observed data can be used to learn about these parameters and thereby to improve predictive capability. The purpose of this course is to describe the mathematical and algorithmic principles of this area. The topic lies at the intersection of fields including inverse problems, differential equations, machine learning and uncertainty quantification.
ACM/CMS 107: Introduction to Linear Analysis with Applications
Covers the basic algebraic, geometric, and topological properties of normed linear spaces, inner-product spaces, and linear maps. Emphasis is placed both on rigorous mathematical development and on applications to control theory, data analysis, and partial differential equations.
Instructor at NVIDIA Deep Learning Institute
Fundamentals of Deep Learning
This course introduces students to the fundamentals of deep learning, including how to train models using modern frameworks such as PyTorch, work with common data types and architectures, and improve performance through data augmentation and transfer learning. The course covers core topics including convolutional neural networks, natural language processing, and practical strategies for building accurate models with limited data and computation.