The Case for Theory in Computer Science

In today’s increasingly preprofessional college culture, the value of theory has taken a backseat.

Fundamentals learned in the classroom have a harder case than ever to make for themselves. In fields where technical interviews reign and marketable skills are prized, this tension is particularly pronounced.

Computer science at Harvard faces these same criticisms: overly theoretical and unrelated to the true day-to-day of technical careers.

Prospective concentrators may arrive on campus already having heard about this theoretical bend. Especially for those who plan to enter industry or pursue entrepreneurship, this can raise questions about how well the curriculum prepares them for the world beyond the classroom.

At a time when tech hiring feels uncertain and LeetCode looms large, these concerns hit home for many. But those who believe that the prioritization of algorithms and abstraction comes at the expense of practical skills misunderstand both the nature of computer science and the purpose of a Harvard education.

Harvard’s choice of computer science curriculum is grounded in the subject’s theoretical foundations. But the required courses of algorithms, systems, and discrete math are hardly detours from real-world impact, but rather that which make it possible. A rigorous grounding in any subject’s theory is certainly important, but in a field where tools can quickly rise and fall from relevancy, it becomes absolutely essential.

Harvard’s choice of theoretical emphasis is hardly unusual. Peer institutions such as the Massachusetts Institute of Technology, Carnegie Mellon University, and the Georgia Institute of Technology have similar theory-heavy cores. The difference arises in flexibility — while Harvard requires students to build a strong foundation, it leaves considerable room within those requirements for students to pursue their own specialties.

This is for a fundamental reason: computing, like anything else, doesn’t exist in isolation.

From computational linguistics to bioinformatics to economics, much of the most consequential technological advances emerge not within rigid boundaries, but across disciplines. The structure of Harvard’s concentration simply allows students to operate in those spaces. The 145 students who combined CS with another field in the 2024-25 academic year — and the many others who study it as a double or secondary — certainly seem to be making use of this versatility.

Skeptics might be right about one thing: coursework alone doesn’t prepare students for every industry context. But this is true everywhere. No curriculum, no matter how currently practical, can replace internships, hackathons, or personal projects that genuinely excite students. Building outside the classroom is not a weakness of the concentration; it is and always has been a defining feature of the field.

Moreover, as students advance, the concentration offers increasing room for application-focused work. Upper-level electives in areas such as machine learning, graphics, and human-computer interaction allow concentrators to translate theory into tangible systems.

The School of Engineering and Applied Sciences has also responded to industry changes and student calls for practicality by introducing COMPSCI 1060: “Software Engineering with Generative AI,” a course that applies artificial intelligence to software development.

Underlying the debate is a broader question: What should Harvard provide? If the goal is to produce engineers simply ready to implement today’s tools, then a purely practical curriculum might suffice.

But liberal arts has never defined education so narrowly. Rather, it seeks to cultivate the kind of thinking capable of confronting problems that do not yet have names. For a discipline defined by its rapid change, this aim is absolutely pragmatic. Computer science at a liberal arts institution should encourage students to be on the constant lookout for the subject’s intersections with the world around it.

To anxious prospective concentrators, don’t mistake a theoretical core for irrelevance. The engineers who build enduring systems are those who understand why technologies work, not just how to use them.

Our computer science concentration isn’t perfect; no program is. But its deliberate balance reflects a philosophy core to everything we hold dear: mastering fundamentals in lecture halls and applying them independently are complementary skills, not competing ones.

Ananya Gautam ’29, a Crimson Editorial editor, lives in Leverett House.