Hello! Welcome to my ENP 162 Portfolio. ENP 162 is a Tufts University course named “Human-Machine System Design.”
My name is Blake Williams, and I am a master’s student studying human factors engineering. I’m from Florida, but I have lived in a host of places in the past: Los Angeles, San Francisco, Washington DC, and Madrid. In my free time, I like to stay active through a host of activities: soccer, tennis, running, gymnastics, CrossFit, and I’ve been part of two dance groups while I’ve been at Tufts.
I graduated from Tufts University this past year with a degree in Economics and certificate in nutrition and food policy. During the end of my undergraduate years, I was exposed to the field of human factors and was fascinated by finding user-centered solutions to a wide range of problems. In the future, I would like to combine my interests for analyzing business processes with creating the best possible user-experience.
This website will show my projects and blog posts from ENP 162. I hope that this website will serve as a public way to present my work throughout my time in ENP 162. In addition, I previously used this blog to post about the wide range of careers in human factors. I have kept these blog posts visible on the website so that others can still learn about the endless amount of possibilities in the field of human factors.
In the course, we learn frameworks and methods to analyze human-machine system designs. Because there are so many different human-machine systems (look around, how many can you count right in front of you?), we get to explore many different types of systems in the course. Whether it be IoT, automated services, or medical device systems, the class will give me a deeper understanding of the mechanics behind the machines I interact with daily.
I think the class is an exciting opportunity to learn more about how I can better design complex systems; for example, a central question to human-machine systems is whether something should be automated and the factors that we must take into consideration. For example, if a specific feature is automated, will it result in more or less human errors? What is the effect on the older population’s mental health due to decreased social contact? Even if a task has the capacity to be automated, it does not necessarily mean that the task should be automated. By looking at systems through different lenses (e.g. physical, ethical, and cognitive), we are able to better design human-machine systems.
Whether we like it or not, automation and the complexity of human-machine systems will continue to grow increasingly fast in the future, which is why it is so imperative that the field of human factors be studied.