Dr. Nathan B. Gaw, Assistant Professor of Data Science

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  PhD Dissertations Advised MS Thesis Advised Undergrad Research Students Courses
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Courses Taught

Summary: I have taught 13 four-credit courses with at least 3 individuals enrolled and have received an average of 4.58 rating on my instruction. A summary table of the courses and ratings is provided below.

• OPER 679: Empirical Modeling (AFIT) (2022-present), primary instructor, introduction to linear regression analysis
• OPER 685: Machine Learning (AFIT) (2022-present), primary instructor, introduction to machine learning techniques
• OPER 688: Operational Experimentation (AFIT) (2022-present), primary instructor, design of experiments
• OPER 683: Response Surface Methods (AFIT) (2022-present), primary instructor, overview of response surface methodology
• OPER 684: Time Series Forecasting (AFIT) (2024-present), primary instructor, analysis of time series data patterns and introduction of major forecasting techniques
• IEE 474: Quality Control (ASU) (2018-2019), Served as a teaching assistant (TA) for on-campus sections; pioneered a new lab curriculum, wrote and graded quizzes/tests, helped teach classes, and held review sessions
• IEE 570: Advanced Quality Control (ASU) (2019), Served as a teaching assistant (TA) for both on-campus and online sections; wrote and graded quizzes/tests and held review sessions

I have additionally taught other full courses/independent studies on the following topics:

Full Courses:

• OPER 655: Text Mining (AFIT) (2023-present), primary instructor, introductory course to machine learning techniques to perform analytics and inference on text data

Independent Studies:

• OPER 899: Advanced Federated Learning (AFIT) (2023-present)
• OPER 699: Ensembling Approaches (AFIT) (2023-present)
• OPER 899: Machine Learning Feature Selection Techniques (AFIT) (2022-present)

The following are new courses I have developed:

• OPER/STAT 699: ML for Temporal/Dynamic Modeling (AFIT) (2023-present), primary instructor, topical course recent machine learning techniques for temporal/dynamic data
• OPER 699: Neural Networks Specialization (AFIT) (2023-present), primary instructor, introduction course on neural networks
• OPER/STAT 699: Federated Learning (AFIT) (2022-present), primary instructor, introduction to federated learning (FL)
  • Developed project involving actual federated learning using Raspberry Pi (RPi) edge devices to train a model on human detection.
  • Developed a handbook and lecture materials that cover various fundamental methods in FL.

The following are short courses I have taught:

• Docker Tutorial, AFIT Department of Operational Sciences, Fall Short Course (2023)
• Data Manipulation, AFIT Department of Operational Sciences, Fall Short Course (2022)
• Introduction to Artificial Intelligence, U.S. Joint Artificial Intelligence Center (2022)