Computer Science Faculty Receive Google Machine Learning and Systems Awards

Two Computer Science faculty are receiving inaugural Google Machine Learning and Systems Junior Faculty Awards.

Assistant Professor Pardis Emami-Naeini and Assistant Professor Lisa Wu Wills will each receive an unrestricted gift of USD $100K to support their future research and will be invited to participate in a symposium with fellow awardees. They were selected in recognition of the significance and promise of their work in security & privacy and hardware acceleration, respectively.

Emami-Naeini’s research focuses on enhancing privacy and security decision-making and interactions with emerging technologies. Her work has been covered by multiple outlets, including Wired and The Wall Street Journal. On the policy and advocacy front, she has worked closely with the National Institute of Standards and Technology (NIST), Consumer Reports and the World Economic Forum to inform their efforts toward designing usable and informative security and privacy tools for consumers.

“The Google award will support our efforts to understand the privacy and security challenges users face in their day-to-day interactions with data-intensive technologies,” she said, “as well as investigate the types of interventions needed to address their concerns.” Her goal is to design solutions that are both usable and effective in empowering informed user interactions.

Wills, who is also an assistant professor in the Pratt School of Engineering, researches the development of performant and efficient hardware-accelerated systems with the goal of processing large volumes of data efficiently. These accelerators can be used to expedite and advance research in big data, the natural sciences, healthcare and artificial intelligence. 

One of her research goals is to greatly simplify the design, deployment and usage of custom hardware. Wills recently published an open-source multi-core accelerator development and composition framework called Beethoven [ISPASS 2025 and ISCA Tutorial 2025]. 

She will use the Google award to fund her team’s ongoing research on retrieval-augmented generation (RAG) for large language models (LLMs). Retrieval-augmented generation allows LLMs — text-focused AIs like ChapGPT — to produce more accurate outputs by pulling data from external sources, rather than having to rely solely on the pre-training they’ve been equipped with.

“I am incredibly grateful and honored that Google has chosen me as a ML and Systems Junior Faculty Award recipient,” she said. “Exploring hardware and software methods to accelerate vector databases and RAG is a promising research direction with the potential for significant impact, enabling LLM-serving systems to be much more efficient and accurate.”