Alagappan, Ganesan and Lai win Google Junior Faculty Awards

Ram Alagappan, Aishwarya Ganesan and Fan Lai, CS assistant professors at the Siebel School of Computing and Data Science, each won The Google ML and Systems Junior Faculty Awards 2026. The award, created by Google last year, is presented to assistant professors who are leading the analysis, design and implementation of efficient, scalable, secure and trustworthy computing systems. Each winner will receive a $100,000 grant in unrestricted funding.

Ram Alagappan

Alagappan’s research explores how to rethink fundamental abstractions in computer systems to improve performance, scalability and resource efficiency.

“More recently, my group has been investigating data infrastructure for AI agents, particularly how data systems must evolve to support AI agents, rather than humans, as their primary users,” Alagappan said. “Our goal is to develop new systems abstractions and designs that enable these emerging applications to operate efficiently and reliably at scale.”

He plans to use the award to support the exploration of new systems challenges that arise from increasingly capable AI agents. Specifically, he and his team want to develop new abstractions and infrastructure for agents that operate over large-scale continuously evolving data.

“I hope this support will allow us to pursue ambitious, foundational research that can also have a meaningful impact on real-world systems,” he said.

He added his admiration for Google’s support of early-career researchers and encouraging foundational systems research.

“I am particularly grateful that the award recognizes systems research at the intersection of data infrastructure and AI, an area that I believe will become increasingly important,” Alagappan said. “This award is also a recognition of the hard work, creativity and ingenuity of my PhD students.”

Aishwarya Ganesan

Ganesan’s research rethinks how distributed systems are built as the underlying hardware, cloud infrastructure and applications they support continue to evolve. She said a key focus of her work is building reliable and efficient distributed and storage systems that must continue operating correctly despite failures while delivering high performance at scale.

“Traditionally, achieving strong reliability guarantees has often come at the cost of performance,” Ganesan said. “My research challenges this trade-off by developing new abstractions, protocols and system designs that provide strong correctness guarantees without unnecessary overhead.”

She sees emerging applications such as AI agents generating and processing data at unprecedented scales, creating a need for more scalable and efficient computing infrastructure. Ganesan believes disaggregation will play a key role in meeting these demands by enabling computing, memory and storage resources to scale independently while making them available as shared resource pools.

“I plan to use this award to support my students and pursue new research directions that leverage resource disaggregation across different layers of the computing stack to rethink how we build reliable and efficient distributed systems for these emerging workloads,” she said.

Ganesan said it is particularly meaningful to have the significance and promise of her team’s research recognized by those who work on large-scale computing infrastructure. She said the award is more than a personal recognition, but a nod to the contributions of her students and collaborators who were instrumental in advancing the research.

“We are at an exciting point in systems research, where new hardware technologies, cloud storage architectures and emerging AI workloads are creating opportunities to rethink fundamental system designs,” Ganesan said. “I am excited to continue exploring these challenges with my students.”

Fan Lai

Having previously worked with Google Systems Research, Lai has seen firsthand how fundamental research and real-world computing challenges can inspire and advance each other. His research focuses on developing the algorithms and computing systems that will power the next generation of intelligent technologies, paving the way toward superintelligence (SI). As increasingly capable models and autonomous agents emerge, Lai sees a fundamental challenge in how to make them more intelligent, efficient, scalable and reliable for pervasive SI.

“My group approaches this challenge from two complementary directions,” he said. “On the systems side, we build efficient and scalable computing infrastructure for large language models and autonomous agents. On the algorithmic side, we develop new approaches to improving model reasoning, reinforcement learning and multi-agent collaboration.”

His team also explores how machine learning can make computing systems more intelligent, adaptive and autonomous.

This award will help Lai and his team pursue ambitious new research directions toward building the foundations for SI with the long-term vision of moving beyond optimizing individual models or computing systems in isolation.

“Instead, we aim to create an integrated computing ecosystem in which intelligent algorithms and their underlying infrastructure can co-evolve, continuously improving each other to unlock new capabilities and efficiencies,” Lai said.

He added that the award will also provide valuable support for the group’s students. “It will enable them to explore bold, high-risk, high-impact research ideas and help translate fundamental advances into practical systems with real-world impact."

Lai noted that the award also highlights the growing importance of research at the crossroads of machine learning and computer systems, an area that has been central to his research journey. He echoed the gratitude for Google’s continued support of early-career researchers and foundational research in machine learning and computing systems, as well as his appreciation of his students and collaborators.

“Above all, I see this award as recognition of the creativity, dedication and hard work of my lovely students and collaborators,” he said. “I feel incredibly fortunate to work with such a talented team, and this honor belongs to them as much as it does to me.”

Lai believes the most exciting breakthroughs often emerge when people with different perspectives come together to tackle challenging problems.

“We are entering an exciting new era of computing, with tremendous opportunities to rethink how intelligence and computing systems are designed together,” Lai said. “I look forward to working with our students and collaborators to help shape the foundations of future SI and infrastructure.”

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Grainger College affiliations:

Ram Alagappan is an Illinois Grainger professor of computer science in the Siebel School of Computing and Data Science.

Aishwarya Ganesan is an Illinois Grainger professor of computer science in the Siebel School of Computing and Data Science.

Fan Lai is an Illinois Grainger professor of computer science in the Siebel School of Computing and Data Science.