Ph.D., Industrial and Systems Engineering
Mississippi State University
M.Eng., Industrial Engineering
Arizona State University
MBA,
City University
BS, Aeronautical Engineering
Embry Riddle Aeronautical University
Topics of research and focus:
-
Computational Intelligence
-
Machine Learning
-
Data Analytics
-
Statistical Methods in Engineering
-
Quality Engineering
-
Process Improvement
-
Operations Research
Programming languages I use: R, Python
I've trained engineers, technologists and business teams in Singapore, Malaysia, China, Taiwan, India, Mexico, Germany, UK & USA.
Connect with me on LinkedIn
Why Computational Labs?
Hello fellow learners! My name is Michael Carter, and welcome to Computational Labs.
As a student, I experienced a common pattern: classes would begin accessibly, then abruptly shift to dense mathematical expressions filled with unfamiliar symbols and notation. These elements were necessary, but often obscured understanding rather than enhancing it.
I spent hours outside class researching fundamental concepts that had eluded me in lectures. Each time, I discovered the same thing - a clear foundational explanation would have eliminated virtually all confusion from the start.
This realization shaped my teaching philosophy. When I started teaching, I committed to introducing concepts progressively, which led to the 5-part pedagogy used throughout all Computational Labs courses:
-
Foundational Overview
-
By Hand
-
In Excel
-
In Python (or R)
-
Adapting code using LLMs
The results speak for them selves: in post training survey's, 91% of students report increased confidence in applying their training to real world problems.