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Stories about the Allen School’s people, research and impact. 

The Association for Computing Machinery (ACM) recognized Mahajan (Ph.D., ‘05) among its 2025 class of ACM Fellows for his groundbreaking “contributions to network verification and network control systems and their transfer to industrial practice.”

Allen School alum Kiana Ehsani (Ph.D., ‘21) co-founded Vercept to advance AI for automating repetitive computer tasks. The company spun out of the Ai2 Incubator and raised $50 million in seed funding before its acquisition by Anthropic, developer of the Claude AI assistant.

At an event in the Paul G. Allen Center for Computer Science & Engineering, UW President Robert J. Jones and Microsoft Vice Chair and President Brad Smith announced they are deepening their partnership with a new effort aimed at preparing Washington state residents for an AI-driven economy.

As part of this year’s cohort in the Outstanding Undergraduate Researcher Awards, the Computing Research Association (CRA) recognized four Allen School undergraduates — awardee Haoquan Fang, finalist Hao Xu and honorable mention recipients Kaiyuan Liu and Lindsey Wei.

Iyer, co-director of the interdisciplinary CS for the Environment Initiative, was recognized among the 2026 class of fellows for his early-career efforts to address sustainability challenges — from recyclable electronics, to battery-free robotics, to AI-optimized hardware design.

Rothvoss, who is a member of the Allen School’s Theory of Computation Group, collected the inaugural Trevisan Prize in the mid-career category for his breakthrough contributions in the study of optimization problems.

In a paper published in the journal Nature, a team of Allen School and Ai2 researchers unveiled OpenScholar, a system that can cite scientific papers as accurately as human experts and incorporate new research after it has been trained.

The Institute of Electrical and Electronics Engineers (IEEE) recognized Kemelmacher-Shlizerman for her “contributions to face, body, and clothing modeling from large image collections,” including pioneering virtual try-on tools and bringing the technology to the mainstream.

A team of Allen School and Ai2 researchers were recognized for developing an efficient, scalable system for indexing petabyte-level text corpora with minimal storage overhead to better understand the data on which large language models are trained.

Allen School researchers led the development of a benchmark dataset of 26,000 real-world, open-ended queries to evaluate the creative generation of large language models. They discovered major LLMs all generate similar outputs as if they’re part of an Artificial Hivemind.