CSD, LTI Students Named 2025 Siebel Scholars

School of Computer Science students Sara McAllister and Aashiq Muhamed have been named 2025 Siebel Scholars. As part of the program, each of them will receive $35,000.

Founded in 2000 by the Thomas and Stacey Siebel Foundation, the Siebel Scholars program recognizes nearly 80 students each year whose work influences the technologies, policies, and economic and social decisions that shape the future.

"Every year, the Siebel Scholars continue to impress me with their commitment to academics and influencing future society. This year's class is exceptional, and once again represents the best and brightest minds from around the globe who are advancing innovations in healthcare, artificial intelligence, financial services and more," said Thomas M. Siebel, chair of the Siebel Scholars Foundation. "It is my distinct pleasure to welcome these students into this ever-growing, lifelong community, and I personally look forward to seeing their impact and contributions unfold."

McAllister is a Ph.D. student in the Computer Science Department. Her work on computer systems focuses on distributed, caching and storage systems, leveraging hardware-software co-design and grounding system design in mathematical modeling to enable more efficient and sustainable systems. McAllister also strongly supports diversity, equity and inclusion in computing. She co-created the CS-JEDI (Intro to Justice, Equity, Diversity, and Inclusion in Computer Science) course that is now required for the computer science Ph.D. program.

Muhamed is a master's student in the Language Technologies Institute. His work focuses on responsible, data-centric artificial intelligence and the challenges and opportunities presented by large language models. Before coming to CMU, Muhamed spent five years as an applied scientist at Amazon, where he worked on hardware and algorithmic efficiency in large-scale search and generative modeling. He holds two patents, one for end-to-end speaker verification on raw waveforms and one for television advertisement detection with 3D convolutional neural networks

Learn more about this year's class on the Siebel Scholars website.