Computer Science and Engineering

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Professor Laxmi Bhuyan Receives Distinguished Emeriti Award
The Department of Computer Science and Engineering is proud to share that CSE Distinguished Professor-Emeritus Laxmi Bhuyan has been awarded the UCR Emeriti Association Distinguished Emeriti Award. This prestigious honor recognizes emeriti faculty who have set a high standard of achievement in research, teaching, or service during their years since retirement. Professor Bhuyan’s continued contributions exemplify sustained excellence and lasting impact within the community.
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CSE Faculty and Students Receive Best Paper Award at SIGCSE 2026
A collaborative team including CSE PhD student Ashley Pang, Associate Professor Paea LePendu, Professor Mariam Salloum, and Professor Neftali Watkinson, along with Northeastern University's Professor Carla Brodley, has received the Best Paper Award at the 2026 ACM Technical Symposium on Computer Science Education (SIGCSE TS). Their award-winning paper, "Creating a Second Pathway to Computing Majors," details the birth and evolution of UCR's CS 9 series. The team will be officially presented with the award this February in St. Louis.
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Making AI smarter without more training data
UCR researchers develop a method called Test-Time Matching, an approach that significantly improves how AI systems interpret relationships between text and images, especially when presented with unfamiliar combinations.
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Professor Yue Dong and her PhD student Haz Sameen Shahgir
CSE Researchers Develop New Methods for Full-Length RNA Modeling
Researchers from CSE, Professor Yue Dong and her PhD student Haz Sameen Shahgir, have developed BiRNA-BERT, a new RNA language model capable of processing full-length RNA sequences without the truncation limitations seen in prior models. This breakthrough allows the model to handle long non-coding RNAs (lncRNAs) in their entirety, achieving state-of-the-art performance in lncRNA interaction prediction. Ongoing work aims to extend BiRNA-BERT to RNA-based homology search, opening new possibilities for studying evolutionary relationships that were previously inaccessible to language-model-based
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