What personal characteristics do you think are important for a successful career in tech?
In my field, one should be curious, math-inclined, and resilient. To start, curiosity supports the evaluation process. In Data Science, stopping at the first solution will, in slightly exaggerated terms, always lead to a poor result. On the other hand, curious individuals naturally explore multiple solutions to reach a well-informed answer. Next, while one does not need to be the greatest mathematician, one should be comfortable with math. Daily, I use mathematics and statistics to create and iterate models; moreover, strong mathematical knowledge reveals solutions that can simplify a project’s complexity (i.e. representing a paragraph with vectors to utilize simpler models). Lastly, while resiliency helps in industry, there is a greater importance in academia. There will be hard classes, every semester may not be straight A’s, and sometimes, one may question the value of their effort. These experiences are normal. If one pushes themselves and their knowledge, they will have a very successful career in Data Science.
At what age or stage did you realize that a career in tech was something you wanted to pursue?
As a first-generation university student, I discovered my interest in technology a tad later than most. Early in my career at George Mason University, I studied Painting and Philosophy. Around the age of 21, I decided to reignite an older interest in Aerospace Engineering. The program required the Intro to CS course (CS112). Generally, engineering students expressed their disdain for the class, citing its high failure rate for those who never programmed before. With this information, I avoided the class for as long as possible, which ended up being only a year. In the first week, I instantly fell in love with algorithms. I spent all my free time completing problems and then refactoring their solutions. Consequently, I was able to TA the course, and some others, until I graduated. Even though I was able to attend lectures for CS and focus on computational mechanics in my major, I wish I took the class earlier. I would have switched my major to CS on the first day.
What do you think is the biggest misconception younger people have about pursuing a career in tech?
The tech subfields are very diverse. Just because you do not like programming, does not mean you will not like machine learning (and the many permutations of this within tech).
BONUS for Data Science
You do not, and I repeat, YOU DO NOT ALWAYS NEED A NEURAL NETWORK! 🙂
What do you find most exciting about a career in tech?
I use the same set of algorithms, metrics, and statistical methods for every project; however, each data set is different. For example, you can take a single dataset and modify some fields to obtain dramatically different results. By using this knowledge, one can discover the algorithms that excel in the context. Furthermore, this information, aggregated with previous models’ performances, highlights future modifications to the model. This process of listening to the data and responding to its concerns is one of my favorite tasks. Another honorable mention is using 10 minutes of a meeting to emphasize the distinction between accuracy, precision, and recall.
What tech blogs or resources do you follow regularly?
I am not very big into blogs/resources based on technology. I do browser/datascience on Reddit and I am a huge fan of 3Blue1Brown on YouTube!
What motivates/inspires you to do what you do every day?
After studying AI for a few years, I got involved with anthropocentric applications (how can AI improve humanity). Within the field of AI, the limitations of deep learning are an established quantity. For example, we know models are excellent at solving specific problems; however, these models fail to generalize to other forms of learning. In essence, models lack a fundamental piece of intelligence, and that information will merge AI with fields like Cognitive Science. With this information, we can identify intelligence and the evolution of intelligence. In other words, contributing to the understanding of humanity inspires my work. Quantum computing applications to AI seems like the subsequent step in this story. Given these points, I plan to obtain my Ph.D. and study generalizability of Quantum AI models.
Tech Lives Here 
The Path to Cybersecurity 
The Path to Data Science 
The Path to Programming