Department of Scientific Computing

Data assimilation methods combine numerical models and observations to arrive at the best possible representation of a physical system. This course aims to build a robust theoretical foundation in the subject and explore some of the computational challenges in large scientific and engineering applications. Students will gain hands-on experience by implementing their own algorithms and will complete a final project on a preferred research topic.

Last Offered Spring 2024
Schedule & Location: M W F 10:40-11:30, 499 DSL

Have you ever wondered why fires spread and grow in size so quickly or how smoke plumes can travel thousands of kilometers? These behaviors are governed by fuel properties, atmospheric conditions, topography, and more. This course introduces physics-based and data-driven models in fire science, and investigates the sensitivity and uncertainty of these models. We will discuss computational tools including cellular automata, level set methods, and data-driven methods for discovering equations from measured data. Finally, we will explore techniques for analyzing both simulated and measured fire data.

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Graduate Level (ISC 5307)

This course will focus on the applied data science pipeline of data acquisition, data processing and integration, data modeling and analysis, and validation and delivery, commonly used in the Health industry. Topics include data normalization, scientific visualization, multivariate regression, and Artificial Neural Networks (dense, convolutional, recurrent, and adversarial). The examples and projects of this course contain 1D to 4D health data of electrocardiogram sequences, X-ray, Magnetic resonance imaging (MRI), and functional MRI images.

Graduate Level (ISC 5935)

ISC 4933/ISC 5935. Computational Probabilistic Modeling (3). Prerequisites: MAC 2312 - Calculus II, MAS 3105 – Applied Linear Algebra, and STA 4442/5440: Introduction To Probability or STA 4321/5323: Introduction to Mathematical Statistics, or the permission of the instructor. In this course, students are introduced to probabilistic programming and modeling for modern data science and machine learning applications. Algorithms for predictive inference are covered from a theoretical and practical viewpoint with an emphasis on implementation in Python. Topics include an introduction to probability and learning theory, graph-based methods, machine learning with neural networks, dimensionality reduction, and algorithms for big data. [source]