Blog #1: Hello, Computer Science!
16 May 2025
Origin Story
As a young boy, I loved mathematics. I remember taking pride in completing my multiplication tables before my classmates or being the first to grasp a new concept in class. As I grew older, I faced personal challenges that made my home life miserable. I stopped doing my homework, stopped asking questions, and stopped being viewed as "gifted" in math. It wasn't long before I found myself on the lower track, and I started believing I belonged there.
I nearly entered UMass Amherst as a political science major because of the calculus requirement in the natural sciences. But I decided college would be my opportunity to turn things around, and I switched to biology during orientation weekend, no doubt causing headaches for my advisors. I figured I could survive one class of calculus if it meant pursuing a degree I truly cared about.
Only problem: I never took trigonometry or pre-calc. In fact, I never even encountered logarithms or fractional exponents, frequent operations in any calculus class. It felt like I had to work twice as hard to get half the grade, but I somehow scraped by with a B+. Statistics, chemistry, physics, and population genetics (which had more calculus than calculus?) all went smoothly with my newfound confidence, and I achieved a 4.0 every semester after my first.
"Teach me and I may remember, involve me and I learn"
Fast forward to the third summer of my PhD at Yale in the Pathology and Molecular Medicine program. Sick of repetitive and unwieldy analyses, I started learning Python so I could write automation scripts. As I learned more, I became obsessed. The power that just a little bit of programming competency brings to biology is awe-inspiring. Combine that feeling with the artificial intelligence boom, and the realization comes naturally: "oh boy, I need math again".
Between that fateful moment in July 2024 and now (May 2025), I've tried to read my way to a decent understanding of math, complexity, and AI. I started with books like Why Machines Learn by Anil Ananthaswamy, Artificial Intelligence by Melanie Mitchell, and Chaos by James Gleick. I shifted my focus from strictly cell biology and chemistry research articles to applications of machine learning therein. I've also taken full advantage of the generosity of the CS community with top-notch textbooks like Modern Statistics for Modern Biology by Susan Holmes and Wolfgang Huber, and Harvard's CS50 Python course taught by David Malan.
Of course, reading is not sufficient to build the skills I desire. In between experiments, I'm usually working on a programming project. Some are more fun, like building this website, while others are directly related to my work in drug discovery, like CellPyAbility. I also dabble with practice problems, like those in Python Programming Exercises, Gently Explained by Al Sweigert or on Project Euler. The latter is especially informative because the problems are one-part computation and one-part math theory.
As a final piece of practice, I teach the very math I used to struggle with. I tutor for free through the non-profit I founded, Education Equity Mentors, and I lead workshops in STEM-focused outreach programs, like the Julia Robinson Math Festival and Yale Pathways to Science (Blog 3).
I will continue to share updates on my quantitative journey! If you have any advice for me, please feel free to reach out at james.elia@yale.edu
James