My expertise lies at the intersection of Probabilistic Machine Learning, Deep Learning, and Data Driven Scientific Computing. In particular, I have been actively involved in the design of learning machines that leverage the underlying physical laws and/or governing equations to extract patterns from high-dimensional data generated from experiments.
keywords
probabilistic machine learning, deep learning, data driven scientific computing
APPM 4720 - Open Topics in Applied Mathematics
Primary Instructor
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Fall 2020 / Spring 2021 / Fall 2021 / Spring 2022 / Fall 2022 / Spring 2023
Provides a vehicle for the development and presentation of new topics that may be incorporated into the core courses in applied mathematics. Department enforced prerequisite: variable, depending on the topic, see instructor. May be repeated up to 15 total credit hours. Same as APPM 5720.
APPM 5720 - Open Topics in Applied Mathematics
Primary Instructor
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Fall 2020 / Spring 2021 / Fall 2021 / Spring 2022 / Fall 2022 / Spring 2023
Provides a vehicle for the development and presentation of new topics that may be incorporated into the core courses in applied mathematics. Department enforced prerequisite: variable, depending on the topic, see instructor. May be repeated up to 6 total credit hours. Same as APPM 4720.
STAT 2600 - Introduction to Data Science
Primary Instructor
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Spring 2020 / Fall 2020
Introduces students to importing, tidying, exploring, visualizing, summarizing, and modeling data and then communicating the results of these analyses to answer relevant questions and make decisions. Students will learn how to program in R using reproducible workflows. During weekly lab sessions students will collaborate with their teammates to pose and answer questions using real-world datasets.
