Computational Biology

Experimental methods in biology and medicine have created exciting challenges for computer science. With large sets of diverse biological information there is opportunity to develop novel methods for complex, accurate, and consistent interpretation of the data. Insights from computational biology can advance basic science, aid in engineering design, and improve health outcomes at many levels, including in research, policy and clinical settings. At Princeton, researchers in computational biology use the tools of computer science — algorithmic design, bioinformatics, statistics, artificial intelligence, and machine learning — to address fundamental questions in biology and medicine.

Computer Architecture

Spanning the interface between hardware and software, research in computer architecture focuses on the organization, programmability and performance of computer systems. As the end of Moore’s Law causes a seismic shift in computing technology trends, computer architecture comprises efforts to make computers faster, more programmable, more reliable, more secure, and more energy efficient. Research in computer architecture at Princeton includes work on microarchitecture, parallel processing, compilers, future technologies such as quantum computing systems, and broad design goals, including secure hardware.

Economics & Computation

Economics and Computation uses both computational paradigms and economic models to study interactions between self-interested entities. Sometimes also called Algorithmic Game Theory, aspects of the discipline are theoretical, proving theorems after mathematically modeling strategic behavior. Aspects of the discipline are also empirical, analyzing real-world behavior and providing policy guidance. Algorithmic mechanism design, the design of algorithms to be deployed among self-interested agents, is a particularly active research area at Princeton. This includes matching markets, auction design, and consensus protocol design.

Human-Computer Interaction

From desktop computers to smart watches to cell phones to robots, we are constantly using and interacting with technology. Human-Computer Interaction (HCI) studies the ways we interact with technology and how technology shapes our experiences. At Princeton HCI, we develop and study technologies that augment people’s intelligence and extend their abilities, while allowing them to be present in the physical world and form meaningful connections with others. Research at Princeton focuses on social computing, ubiquitous and tangible computing, human-centered machine learning, and creativity tools. This includes research on computer-supported collaborative work, public interest technology, augmented reality and virtual reality interactions and social experiences, explainable AI, fairness, accountability and transparency, and tools for professional and novice artists.

Machine Learning

Using advances in machine learning, modern computers are now able to learn and make decisions. Rather than acting according to an explicit set of instructions, researchers are building intelligent systems designed to deal with uncertainty, adapt to the surrounding environment, and learn from experience. The goal of research in machine learning is to build intelligent systems that learn and assist humans efficiently. At Princeton, research in machine learning includes: the development of new deep learning architectures for computer vision, natural language, and materials science; sophisticated new methods for control and reinforcement learning; theoretical investigation of deep learning; new methods for understanding and correcting bias in machine learning algorithms and data sets; new approaches to automatic differentiation; and exploration of connections to human cognition and neuroscience.

Natural Language Processing

We rely on machines to understand human language and anticipate our instructions. Research in natural language processing seeks to build computers and autonomous systems that can understand and use human knowledge, primarily language and text. The goal of this research is to build intelligent systems that learn and communicate through language. Work in this area pushes the boundaries of artificial intelligence while also enabling advances in practical text processing applications that can have a broad impact on various real-world problems. At Princeton, researchers develop novel algorithms, design new frameworks, and investigate theoretical foundations to tackle challenging problems in language understanding. Researchers draw on techniques like deep neural networks and reinforcement learning.

Programming Languages & Compilers

The field of programming languages is concerned with language design and implementation, as well as methods for reasoning about the behavior of programs. The goal of research in this area is to make it easier for software developers to write correct and performant code. At Princeton, researchers are interested in functional programming languages, type theory, compilers, program analysis and synthesis, and formal verification. Topics span from developing the mathematical theory of programming languages to interdisciplinary work that intersects with domains such as networking, computer architecture, and distributed systems. The Princeton Programming Languages Group website can be found at http://pl.cs.princeton.edu/.

Public Law & Policy

Just as technology shapes everyday life, societies are also shaped by technology. From social media to chat GPT to surveillance, how we use and regulate technology has far-reaching implications for scientists, citizens and governments. Combining expertise in computer science and social science, researchers in this area focus on crafting policies for the use and innovation of technology so communities can benefit. Research in technology policy at Princeton includes data and information privacy, fairness in machine learning and AI, cryptocurrencies, online speech, national security, the social impacts of algorithms, and broadband access.

Robotics

The goal of robotics research is to create more complex tools that can mimic the actions and intelligence of humans and animals. As robots become more sophisticated, their design, operation, and applications become more complex. Research in robotics at Princeton spans a wide variety of research areas including perception, control, learning, and planning. Applications span manipulation, locomotion, drones, autonomous vehicles, construction, architecture, and soft robots.

Security & Privacy

New advances in computing allow for exciting innovation, but can also reveal new threats. As we rely on computers more and more to store private information, perform critical tasks in sensitive areas like banking and national security, and assist in daily tasks, vulnerabilities in these systems can create ever greater problems. Research in privacy and security explores vulnerabilities in applications, networks, and systems and builds protections against possible attacks from bad actors. The goal of this research is to secure our personal information and computer infrastructure so we can harness the power of computing while minimizing threats. At Princeton, research in this area focuses on advances in applied security and privacy, enhancements to cryptography, and building stronger network and systems security.

Systems & Networking

Research in systems and networking focuses on the design of and interconnections between myriad kinds of computational infrastructure and devices: examples include global-scale databases, data centers, the wireless radio access edge, and low-power embedded devices. The goal of research in this domain is to design, build, and experimentally evaluate ever more high-performance, reliable, and secure systems and networks. Systems and networking research at Princeton includes work on distributed systems, operating systems, storage systems, networking and network architectures, mobile and wireless systems, embedded/low-power networked systems, and protocol design.

Theory

Theoretical computer science explores the mathematical underpinnings of computer science, particularly efficient algorithms and protocols, which ultimately make much of modern computing possible. The very concept of computation gives a fundamental new lens for examining the world around us and underlies many 20th century inventions. Theory research has helped propel innovation in every field of computer science, including cryptography, AI, machine learning, computational biology, e-commerce, and quantum computing, among others. An abiding interest in the theory and power of computation has been a regular feature of life at Princeton since the times of Alan Turing, Alonzo Church, Kurt Gödel, and John von Neumann, all of whom were Princeton residents.

Vision & Graphics

Computer vision and graphics impact how we understand and create the visual and tangible world around us. Research in computer vision studies how to make computers see and understand pixels. In computer graphics, research is finding new ways to render images and fabricate 3D shapes. Vision and graphics research at Princeton focuses on computer optics, image rendering, computational fabrication and 3D printing, fairness in visual learning, and visualization of computer audio and music.