Research

Computational biology and bioinformatics research in the Department of Computer Science spans biological systems from individual genes, proteins, and cells, to networks of interacting molecules, to species and microbial communities. The research projects include protein structure prediction, protein binding and interaction networks, comparative genomics, microbial forensics, and phylogenomics. Research projects are conducted on a wide array of biological systems and species, including, but not limited to, cancer, neurodegenerative diseases (Alzheimer’s, Parkinson’s, etc.), microbiomes, and eukaryotic species. 

In the field of Robotics & Physical AI, our researchers develop intelligent robotic agents to solve problems in manufacturing, clinical healthcare, transportation, search and rescue, and most importantly agents that will improve the quality of life by assisting in daily activities such as wellness needs and education.

The demands on modern computing systems are increasingly complex, from small embedded systems to phones to laptops to servers in the datacenter. Research into the design and implementation of efficient hardware and software systems will enable next generation systems to support an expanding array of services and applications.

We increasingly live in a world governed by software, a world where programming is at the heart of science and industry as well as a basic skill for everyday life. In this world, the vision of a robust, flexible, and accessible process of software design is more relevant than ever. To realize this potential, software must exploit the parallelism of modern computer architectures. Research in programming languages, High Performance Computing (HPC), and formal methods at Rice aims to make this vision possible.

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.

Quantum computing has emerged as an alternative computational model. Realizing the practical acceleration using a Noisy Intermediate-Scale Quantum computer is one of the most important problems of our century. While prototypes are being built now, moving computations to a fully-functional fault-tolerant quantum computer still faces many technical hurdles.