Artificial Intelligence & Machine Learning

We build Artificial Intelligence. And we build it to last.


Artificial Intelligence & Machine Learning

Rice Computer Science does not just study Artificial Intelligence. We build the foundations it runs on: the algorithms, the architectures and the theory that makes AI systems work at scale and work responsibly.

Our faculty cover the full spectrum of modern AI and machine learning: mathematical foundations of ML, graph-based algorithms, reinforcement learning and randomized optimization methods, alongside computer vision, natural language processing, multimodal AI and large language models.

We also work at the intersection of AI and human systems:  human-AI teaming, computational biology, digital health and computational economics. Because scale has real costs, we study efficient AI too: how to build models that perform well without the compute budgets only a handful of organizations can afford.

This work happens in active collaboration with partners across the Greater Houston ecosystem, including the Texas Medical Center, and with industry partners across tech, healthcare and energy.

“Trustworthy AI is not a policy goal. It is an engineering problem. We work on both.”

 

What We Explore

  • How do you build a large language model efficient enough to run outside a data center?
  • What does it mean for a machine learning system to be provably fair?
  • How do vision and language models understand the same scene differently?
  • How do you build AI systems that collaborate with people rather than replace them?
  • What are the actual limits of what an AI system can learn from data?

Faculty

Faculty members leading research in Artificial Intelligence & Machine Learning are as follows: