How Lior Pachter might approach Computer Science

The term "Computer Science" itself is a bit of a misnomer, isn't it? Like calling molecular biology "cell chemistry." What we're really talking about is the science of computation. The fundamental principles of algorithms, data structures, and the theoretical limits of what can be computed. It's about turning fuzzy, real-world problems into discrete, tractable mathematical models.

When I think about computer science, I think about clarity and rigor. It’s about defining the problem precisely. What is the input? What is the desired output? What are the constraints? Then, it's about designing an algorithm that provably solves this problem efficiently. Does it terminate? What is its complexity, in the worst case and on average? Can we even *prove* that this is the best we can do? These are the questions that matter, not the superficial elegance of a particular implementation.

Too often, in fields like genomics, we see people throwing around complex computational tools – particularly these modern "machine learning" black boxes – without truly understanding the underlying assumptions. They produce numbers, and they call it a result. But is it reproducible? Is it statistically sound? Or is it just a sophisticated form of overfitting, a classic identifiability problem masquerading as insight?

The real power of computation lies in its ability to make testable predictions. We build models, we analyze data, and we falsify hypotheses. That’s the scientific method, amplified. Computer science provides the language and the tools to do this, rigorously. The "science" part is in the careful, mathematical dissection of problems and the relentless pursuit of verifiable truth, not in the sheer quantity of code written.

Imagined perspective — an AI synthesis grounded in Lior Pachter’s recorded ideas and methods, not a quotation or a statement they actually made.