Computer science is a field of dual identities. On the one hand, it’s abstract, mathematical, and conceptual. On the other, it’s deeply applied, data-driven, and user-focused. These two identities are reflected in the two primary modes of research in computer science: theoretical and empirical.
At Learn in Europe, we believe that doctoral students should not only specialize in their niche—but also understand the broader landscape of research methodologies. Let’s take a closer look at what defines these two approaches, how they differ, and why both are essential to advancing the field.
📐 What Is Theoretical Research in Computer Science?
Theoretical computer science focuses on foundational questions:
What can be computed? How efficiently? What are the limits of algorithms, systems, or models?
It uses mathematics and formal logic as its primary tools.
🔎 Key Characteristics:
Abstract: Concerned with idealized models like Turing Machines, graphs, or formal languages
Precise: Often involves mathematical proofs and formal reasoning
Timeless: Results are general, reusable, and not dependent on hardware or context
🧾 Example Topics:
Algorithm analysis and complexity theory
Formal verification of systems
Cryptography and secure communication
Automata theory and compiler design
✅ Strengths:
Produces general truths that apply across time and platforms
Lays the groundwork for technological breakthroughs
🚧 Limitations:
May be disconnected from practical implementation
Difficult to validate through real-world testing
🧪 What Is Empirical Research in Computer Science?
Empirical computer science focuses on observing, measuring, and experimenting with real-world phenomena—whether that’s how users interact with a system or how an algorithm performs under real conditions.
It is based on experiments, data collection, and statistical analysis.
🔎 Key Characteristics:
Applied: Often deals with user studies, system performance, or software evaluation
Contextual: Results may depend on specific settings or conditions
Iterative: Builds knowledge through repeated testing and refinement
🧾 Example Topics:
Usability testing in human-computer interaction (HCI)
Benchmarking of machine learning models
Performance analysis of distributed systems
A/B testing in web interfaces
✅ Strengths:
Grounds research in actual practice
Reveals unexpected issues or behaviors
Essential for validating theories and improving systems
🚧 Limitations:
Results may not generalize beyond the experiment
Dependent on careful design and statistical rigor
🔄 How They Complement Each Other
| Aspect | Theoretical Research | Empirical Research |
|---|---|---|
| Goal | Discover truths, limits, and guarantees | Understand performance and behavior |
| Methods | Proofs, formal models, simulations | Experiments, data collection, analysis |
| Output | Theorems, models, frameworks | Findings, metrics, usability reports |
| Validation | Logical consistency | Reproducible results, significance tests |
| Strength | Universality, abstraction | Practical insight, context awareness |
In reality, the strongest research projects often combine both approaches. You might begin with a theoretical insight, implement a prototype, and then empirically test it. Or you may discover patterns in empirical data that inspire a new theoretical model.
🧠 Final Thought
Whether you're proving the limits of computation or conducting a user study on mobile apps, both theoretical and empirical research are crucial to the growth of computer science. Each approach asks different questions—but together, they push the field forward.
At Learn in Europe, we equip PhD students not only with cutting-edge tools but also with the mindset to bridge theory and practice. After all, it’s at the intersection of the two that true innovation happens.
👉 Ready to expand your research skills? Explore our doctoral training programs here.
