The Data Science Initiative at Rice University is a multidisciplinary coalition working towards cutting-edge, data-driven solutions and education for next generation citizens.
Rice University is at the forefront in developing methods for data science. Explore the many faculty members at Rice University using these methods.
Computer vision is concerned with processing photographs, videos, and images collected using complex machinery such as systems for remote sensing or medical imaging. Applications considered be researchers at Rice include labeling of images, activity recognition, and scene understanding and decomposition.
Software systems for data science must use modern hardware---CPUs, GPUs, and FPGAs---efficiently, or else analyzing multi-terabyte data sets can be prohibitively time-consuming and expensive. We study how to build software systems that use hardware in a fast and cost-effective manner.
Machine learning is the sub-field of AI concerned with learning models from data. Once learned, these models can be use to label future data, make predictions about the future, aid in decision making, or explain the past. At Rice, we cover multiple sub-fields of machine learning, including deep learning.
Natural language processing (or NLP) is concerned with processing human language. Sub-fields of NLP covered at Rice include voice recognition, parsing of human language, machine reading, and automated question answering.
Optimization (Large-scale Simulation and Optimization
Optimization is the science of choosing the best set of parameters for a particular model. It powers the "learning" in machine learning and statistics. Sometimes, the model to be optimized may be a complex system that is too complex to handle using analytic methods, and must simulated using powerful computers. At Rice, we study a wide variety of fundamental problems in optimization and simulation.
Signal processing studies how to process information. Signals transmit information, such as audio, images, or video, over some medium (often called a "channel"), and signal processing is the study of how to reclaim that information, especially when the channel is noisy or unreliable. At Rice, we study a wide variety of problems in signal processing.
Statistics is the study of building principled, typically stochastic (that is, random or probabilistic) models for the world, inferring those models from data, and applying the models to tasks such as decision making and making predictions about the future. Rice covers all classical and emerging areas of statistics.
Increasingly, the data that are used to fuel machine learning, NLP, and computer vision are huge---terabytes in size, or even larger. At Rice, we study how to build systems that can manage such large data sets and facilitate learning and analytics over them.
Discover how Rice University is using data science in real-world applications as well as the wide range of faulty members who are involved in their respective fields.
Biomedicine (Medical Technologies)
Modern medical devices are powered by data. They collect raw data, often directly from a patient, and must process that data, presenting it to patients and healthcare workers to optimize care and aid in decision making. We study how to build devises that collect data, and how to analyze the data that they collect.
Increasingly, humanities---the study of human culture---has focused on data. Modern data analytics can be used to analyze the artifacts produced by human beings (our writings, drawings, buildings) to better understand the human experience.
Informed decision making in modern healthcare requires the use of data. We study how to use large and multi-modal data sets (including text, gene sequences, images, and structured data) to make decisions in domains such as patient care, public health, psychology, and the environment.
With large amounts of fine-grained data available that allows a firm to understand its customers, marketing in the modern era is fundamentally a data-oriented science. We study how today's data sets can be used to manage customer relationships.
Policy decisions of all kinds: social policy, resource allocation and management, domestic and international governance, increasingly rely on data. We study how to use data to help make informed policy decisions.
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Understanding and optimizing our modern urban environments requires data. At Rice, we collect and host data describing urban environments, and study how such data can be used to shape the urban environments of the future.