Biomedical informatics leverages virtually all areas of biomedical, computational, physical and social science. Research groups include cancer, clinical phenotyping, mHealth, healthcare analytics, human genetics, high-throughput drug discovery, infectious diseases, neurosciences, pathology, radiology and real-time streaming health analytics, among others.

We study computational linguistics, information representation and processing, and online user behavior. We develop new techniques for natural language processing, information retrieval and web search and text mining, question answering, and social media analysis.
Related Links:
Mind & Language Lab
Intelligent Information Access (IR) Lab
Natural Language Processing (NLP) Lab

Our research investigates how the brain organizes and analyzes information, and how we can use those observations to influence data computation in other domains.

We research how to build and improve scalable distributed systems, databases and networking to support the collection, analysis and understanding of large volumes of data.

We study algorithms and methods for finding patterns and insights in data to build more intelligent and effective systems.

Secure and interoperable information sharing

We work on storage, organization, management and retrieval of medical, public health, web and biological data.
Related Links:
AIMS Lab
IR Lab

At the heart of finding and exploiting patterns in data is the art of understanding what patterns are and computational methods for analysing them. The theory and discrete mathematics groups investigates fundamental questions in computer science and mathematics.

Metacomputing, distributed systems, collaboration technologies, and networking. Current projects are focusing on self-organizing frameworks for cooperative resource-sharing and collaboration group communication in the context of peer-to-peer networks.

We study how the true power of data analysis and computation can be unleashed through novel and improved interfaces to computers and other devices.

Development of methods for labeling anatomic and micro-anatomic structures found in images, systems software and techniques for management and analysis of very large image data, and techniques and software tools for sharing this labeling information in a computational grid architecture. Computational methods for large-scale image processing, in particular, image deblurring, image reconstruction, image registration.

Numerical Linear Algebra, Numerical Optimization, Numerical Partial Differential Equations, Numerical Methods for Inverse Problems, Image Processing, Geophysics, Computational Fluid Dynamics, Numerical Methods for Complex Networks, Parallel and Distributed Computing. Scientific computing research involves the development of fast, reliable numerical algorithms that are needed to solve difficult mathematical problems in a wide variety of applications.