Five UC Berkeley computer science graduate students have been named Siebel Scholars for 2027, receiving $35,000 each to pursue research spanning AI, robotics, security and machine-learning hardware. The useful detail is not simply that Berkeley has won another academic laurel. The awards are buying time for work aimed at making intelligent systems safer, more dexterous and more capable in the physical world.
Watch Desk analysis
What happened
The Daily Cal reports that Alexander Proshkin, Nicholas Rakela, Saathvik Selvan, Hiya Shah and Karen Shieh were selected through UC Berkeley’s internal nominations. Each scholar receives $35,000, with more than 90 recipients named globally each year.
Their research covers a notably broad slice of computing. Proshkin, Selvan and Shah work on how robotics and AI interact with the physical world. Proshkin and Selvan focus on dexterous manipulation, while Shah studies the safety of AI systems’ interactions. Shieh researches AI agents for security, and Rakela works on hardware for machine-learning systems.
The students told the Daily Cal that most of the award will go towards tuition. Shieh said the financial support should allow her to devote more time and energy to research, while Proshkin described the recognition as confirmation that people are paying attention to his work.
Why it matters
Research funding is often discussed in terms of billion-pound laboratories and shiny new computing clusters. This is the smaller, more human unit of progress: five researchers gaining some room to ask difficult questions without quite so much financial noise in the background.
The topics also point towards the next layer of AI development. Better models are only part of the story. Systems still need to move safely through the physical world, resist manipulation, operate on suitable hardware and behave sensibly when deployed as agents. That is where the glamour tends to meet the floor, occasionally with consequences.
Our read
This is a modest but worthwhile signal about where serious computer science work is heading. The awards do not prove that any particular research project will produce a breakthrough, and the supplied report gives no results, publication records or deployment timelines to judge yet.
What it does show is a cohort working on the awkward connective tissue between AI capability and usable systems. Supporting that work is less dramatic than announcing a new model, but arguably more useful. The real test will be whether these projects produce methods that other researchers can reproduce and builders can safely use.
What to watch
- Research results:
whether the scholars publish findings, datasets or open-source tools. - Robotics progress:
whether dexterous manipulation moves beyond controlled demonstrations. - AI safety work:
whether Shah and Shieh’s research produces practical evaluation or security methods. - Hardware developments:
whether Rakela’s work improves the efficiency or capability of machine-learning systems.
Discussion spark: Should scarce AI research funding prioritise bigger models, or the less glamorous work of safety, robotics, security and specialised hardware?
Sources and evidence
- 5 UC Berkeley CS students distinguished as Siebel Scholars, awarded $35K each – Daily Cal (23 September 2026, 12:15 UTC)
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