Pre-College Advice: Why Stanford Students Study Computer Science (Part 1)

INSPIRIT AI HAS AN INCREDIBLE HOST OF INSTRUCTORS FROM ELITE UNIVERSITIES ALL ACROSS THE UNITED STATES. IN THIS NEW SERIES, WE WANTED TO TAKE THE MENTORSHIP BEYOND THE CLASSROOM AND ASK OUR INSTRUCTORS WHY THEY CHOSE TO STUDY COMPUTER SCIENCE IN COLLEGE AND IF THEY HAD ANY ADVICE FOR OUR STUDENTS.

Trenton Chang was drawn to computer science despite initially majoring in American Studies. After taking a couple of probability courses, he was hooked on Artificial Intelligence! “I just never stopped taking more AI classes.” Trenton wants to encourage our students to continue to explore their passion for Artificial Intelligence.

“This is a very exciting time to study AI. There are more and more resources out there for people to self-study, and there's no shortage of great papers/code. I'd encourage you all just to try *things* and build your own projects — it's okay if it doesn't work; you'll learn so much along the way (as I did).”

Harry Sha had a similar experience at Stanford. He initially intended to major in psychology and music but he took two intros to computer science classes his first two quarters at Stanford and enjoyed them so much that he switched to a computer science major!

“I found it really exciting and empowering to suddenly be able to solve problems that I previously had no clue how to solve.”

Harry eventually switched again to major in math, but he focused on studying theoretical computer science and Artificial Intelligence.

Sophia Barton entered Stanford knowing she wanted to major in something in the STEM field. After taking a wide range of classes, anything from math to economics to German, she finally took an introductory computer science class.

“It was definitely a challenging course but I liked it enough to continue 'trying it out' sophomore year, and realized that I loved the problem-solving and detail-oriented aspects of coding.”

But she still felt drawn to a major that would give her the opportunity to apply those problem-solving and coding skills to other areas of study. And the Symbolic Systems (SymSys) major allowed her to do just that!

“I was able to get exposure to a mixture of fields (CS, linguistics, philosophy, psychology & neuroscience) that I honestly hadn't been exposed to at all prior to college. SymSys also gave me a brief introduction to AI because I did the 'Learning' concentration, which was a combination of human education courses and ML courses. By senior year, I realized that I really wanted to hone my technical skills (both out of pure curiosity of deeper CS topics, as well as interest in preparing for a career in technology) so I decided to pursue a Master’s in Computer Science.”

Sophia also chose Artificial Intelligence as the concentration for her Masters because she could expand on her studies from undergrad and study Natural Language Processing as a bridge between linguistics and computer science. And Sophia has a lot of advice for Inspirit AI’s students!

“My advice is to pursue what interests you in the moment, whether or not that is AI, and whether or not that changes. Live life with curiosity and creativity! AI is an ever-changing and fast-paced field, so if you are interested in studying it in college or just keeping up with it on the side, I'd recommend reading plenty of Medium or TowardsDataScience articles online, as well as trying to read some papers published on PapersWithCode. New advances in terms of models and new ethical concerns are always popping up, so staying informed is important. Also, try to build your own models from scratch (whether that's a model that's been published or one you've designed entirely on your own), to see how these things work under the hood as best as possible, as opposed to taking them for granted as black box models.”

Finally, Peter Washington originally entered Stanford wanting to study political science and economics but was instantly intrigued by a programming class his freshman year because it “highlighted how programming can be applied to any field.” To Inspirit AI’s students, Peter says:

“You have learned the basics of what you need to know to start building your own models now! Use the Inspirit notebooks as a reference.”