Algorithms and Theory at Khoury College of Computer Sciences
Advancing knowledge about the foundations of computing
The Algorithms and Theory research area at Khoury College focuses on fundamental questions in computer science, developing knowledge that supports progress in any specialty: for instance, determining what it means for something to be computable, what kind of computations can be made, investigating whether in principle sets of procedures — algorithms — can be created for a type of problem, and analyzing those algorithms’ characteristics.
In this way, algorithms and theory research supports computer science across a range of other areas such as fueling cryptography, AI, and data science, areas where practitioners building tools need to know that the foundational theory their work depends on is sound, what the limits are, and what’s feasible.
Pushing the boundaries of what’s possible
Insights from Khoury College algorithms and theory research have an impact on many aspects of computing. In particular, theory research has helped discover how cryptography can help keep information you look up from a resource private, with the potential that someday you could use a search engine but it wouldn’t see what you’re doing — a potential revolution in privacy.
Study of algorithms can help ground core work in software development. By looking at the general and abstract characteristics of a computational problem, you can assess its complexity and what the limits may be to creating algorithms to tackle it. In this way, Khoury College is helping build the foundation for better approaches to building programs and tools, pushing the boundaries of what is feasible technically through better understanding of theoretical limits.
Sample research areas
- Algorithms for massive data
- Approximation algorithms
- Computational complexity
- Cryptography
- Distributed computing
- Cybersecurity and privacy
- Learning theory
- Network algorithms
- Quantum computation
Khoury researchers: At the forefront
Faculty awards and achievements
Best paper
- STOC 2025: Soheil Behnezhad
- SODA 2024: Soheil Behnezhad
- STOC 2023: Daniel Wichs, Wei-Kai Lin, Ethan Mook
- CCC 2008: Emanuele Viola
Best student paper
- ITCS 2023: Lunjia Hu
- ALT 2022: Lunjia Hu
Honors and awards
- Sloan Fellow 2018: Daniel Wichs
Featured project highlights
Modern algorithms must efficiently process unprecedented volumes of data. But this shift alters the fundamental principles of computation and algorithm design, requiring entirely new paradigms for handling massive data. While traditional algorithms assume all of the input is stored in the memory of a single computer that processes it, this assumption is far from what happens in practice where the input is distributed across a large number of machines. Khoury faculty study the foundations of this paradigm shift from various aspects.
How can we trust data driven systems in high-stakes applications? Khoury faculty are addressing this challenge by developing the theoretical and conceptual foundations of trust in AI, machine learning, and data science. This work addresses problems like privacy of the data that drives these systems, reliably quantifying their uncertainty and making sound decisions from their outputs, and hardening these systems to be robust even to malicious attackers.
Can we privately search the internet without revealing our query to the search engine? Khoury faculty are working on novel methods to encrypt data while enabling third parties to perform private computations over it. These technologies have the potential to allow for innovative applications from secure internet search to secure data analytics, vastly increasing our privacy on the internet.
Can we understand the limits of efficient computation? This question is one of the grand challenges in computer science and mathematics, which profoundly shapes the computer systems we use daily. Computer systems must navigate around computationally intractable problems, while modern cryptography depends on problems that we believe are computationally hard. Khoury faculty approach this challenge by forging new connections between computational problems and establishing unconditional impossibility results within restricted yet far-reaching models.