Rice quantum computing research garners awards
CS students Jason Ludmir and Jason Han won first prizes at SC24 and SIGMETRICS; professor Tirthak Patel was awarded an Honorable Mention at SC24
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‘Human Flourishing in the Age of AI’ conference tackles how AI is quietly reshaping what it means to live a fully human life.
New imaging approach could enable faster, more accurate 3D sensing for robotics, manufacturing and autonomous systems.
As part of the Defense Advanced Research Projects Agency’s Bio-attribution Challenge, a team of Rice University computer scientists is building a computational tool to screen environmental DNA samples for pathogens.
What does it mean for human beings to flourish in a world shaped by AI?
John Mellor-Crummey receives Secretary of Energy Achievement Award
Rice computer science professor recognized for his work on DOE’s 7-year, $1.8B Exascale Computing Project
Two Rice CS faculty members named to endowed chairs
Faculty exemplify excellence in research across engineering and computing disciplines
Rice computer scientists develop solutions for making AI models more efficient and customizable
Shrivastava and team present three papers on LLM advancements at NeurIPS
Rise of DeepSeek: Experts weigh in on the disruptive impact of new Chinese open-source AI model
Rice CS faculty members Anshumali Shrivastava and Xia Ben Hu discuss the future of AI
Rice uses generative AI to enhance research security, saving hundreds of expert hours each week
PRISM helps identify hidden research risks and ensures compliance with federal security standards
Kavraki named IEEE RAS distinguished lecturer
Robotics professor nominated for three-year appointment by RAS’s Technical Committee on Motion Planning
Rice earns top 10 spot in US News rankings for online graduate computer science program
The OMCS program advanced four spots to No. 10
Kavraki lab advances immunotherapy research
Modeling tool APE-Gen2.0 expands peptide-MHC modeling repertoire to aid in targeted therapy development
Rice CS lab makes leaps in memory compression for machine learning
Novel approach achieves 10,000X reduction in memory use