Blog #1: Hello, computer science!
16 May 2025
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 logarithims or negative 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 all went smoothly with my newfound confidence, and I achieved a 4.0 every semester after my first. I figured math was happily in my rearview mirror.
Fast forward to the third summer of my PhD at Yale in the Pathology and Molecular Medicine program. Sick of a repetitive and unwieldy analysis, I started learning Python so I could write a simple automation script. Since then, I've become unable to picture my future career without at least some elements of computer science. 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 no, 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. Here's a rough list:
- The Primacy of Doubt by Tim Palmer
- Fooled by Randomness by Nassim Taleb
- Hackers by Steven Levy
- Nexus by Yuval Harari
- Humble Pi by Matt Parker
- Antifragile by Nassim Taleb
- The Joy of X by Steven Strogatz
- Source Code by Bill Gates
- Rationality by Steven Pinker
- Infinite Powers by Steven Strogatz
- Artificial Intelligence by Melanie Mitchell
- Genius by James Gleick
- Chaos by James Gleick
- The Alignment Problem by Christian Brian
- Why Machines Learn by Anil Ananthaswamy
Of course, reading is necessary but 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.
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.
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