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AMS 691  Topics in Applied Mathematics

Varying topics selected from the list below if sufficient interest is shown. Several topics may be taught concurrently in different sections: Advanced Operational Methods in Applied Mathematics, Approximate Methods in Boundary Value Problems in Applied Mathematics, Control Theory and Optimization Foundations of Passive Systems Theory, Game Theory, Mixed Boundary Value Problems in Elasticity, Partial Differential Equations, Quantitative Genetics, Stochastic Modeling, Topics in Quantitative Finance.


AMS 691.01: Topics in Applied Mathematics
This course will focus on big data/data science and their applications to crucial
applications such as Blockchain, Metaverse, Smart Energy, Electric Vehicles where huge amount of data
exist. We will first cover the basics of big data and data science. Then we introduce the applications. If
time allows, we may cover some future technologies such as quantum computing which could eventually
give us the ultimate power to conquer the big data. The course will be self-contained as much as possible.
If you are unsure about your background, please send the instructor an email inquiry with your
3 credits
ABCF Grading
Topics (subject to change):
1. Data Science & Big Data Techniques
2. Blockchain and Metaverse
3. Smart Energy and Electric Vehicle
4. Future Technologies, e.g., Quantum Computing (if time allows)

Learning outcomes:
• Understand the basic concepts and common techniques in data science and big data
• Understand the basic concepts and popular applications of Blockchain and Metaverse
• Understand the challenges and opportunities in smart grid and electric vehicles
• Conduct data analysis using the large datasets in the applications discussed

Course Materials:

No required text.  Suggested readings will be recommended.