TomKat Center’s graduate fellows are advancing research that can translate into real world solutions
Academics with impactful applications
A new cohort of Stanford doctoral students has been awarded fellowships allowing them to direct their research toward translational opportunities. The researchers selected for funding in 2024 work in a wide range of domains, including additive manufacturing for energy-efficient electrical grid infrastructure, cooling technologies for AI data centers, large-scale wildland fire management tools, and lithium-sulfur batteries.
“Our goal is to give graduate students the opportunity to focus on the most critical objectives for advancing their project toward a real application” said Matt Kanan, professor of chemistry and director of the TomKat Center. “While it’s not a requirement, each of the awardees in this year’s cohort has identified key external collaborators to help them gain industry insight on the problems they are addressing”.
Starting this fall, the four new fellows will receive support through the TomKat Center for Sustainable Energy’s Graduate Fellowships for Translational Research for the next two years to develop and test their prototypes. The program assists them in cultivating a translational research mindset by fostering a network of peers and mentors with experience in creating new technologies. The fellowships offer full support for two years, providing tuition, stipends, and an additional $5,000 of research funding.
“I really appreciate that the TomKat Center enables this kind of highly interdisciplinary research, which is otherwise not well supported by traditional funding agencies.“ shared Grace Gao, associate professor of aeronautics and astronautics and PhD advisor to the TomKat fellow Daniel Neamati. “This fellowship will allow Daniel to leverage his past research on neural digital twins of cities for autonomous driving to tackle the impactful problem of large-scale fire management at the wildland-urban interface.”
Established in 2020, these fellowships have been awarded to 18 Stanford doctoral students in their third year or beyond. The fellowships may also serve as an on-ramp for their possible participation in the TomKat Center’s Innovation Transfer Program, which assists Stanford teams as they seek to commercialize their technologies and innovative business models by forging new ventures. Since 2013, TomKat has awarded $6.9 million across 121 Innovation Transfer grants. Collectively, these ventures have grown to receive over $3 billion in follow-on funding and a combined valuation of $8.8 billion.
Sanzeeda Baig Shuchi
Scalable surface modification of polymeric separator toward commercial lithium-sulfur batteries
Sanzeeda Baig Shuchi is a PhD candidate in chemical engineering. The goal of her project is to design high-energy density lithium-sulfur batteries to address the ever-growing energy demands and partner with battery companies to move lithium-sulfur batteries toward commercialization.
Research Advisors: Stacey Bent and Yi Cui
Luis Delfin
Additive manufacturing of amorphous steel soft magnets for high-efficiency electric machines
Luis Delfin is a PhD candidate in materials science and engineering. He is developing additive manufacturing processes that simplify the production of amorphous steel transformers, significantly reducing overall costs and accelerating their adoption to help meet the Department of Energy’s new standards requiring increased efficiency of transformers.
Research Advisor: Wendy Gu
Yujui Lin
Yujui Lin is a PhD candidate in mechanical engineering. Through this fellowship, he will explore an advanced cooling and packaging solution to reduce the thermal resistance of chips used in AI computing, eliminating the need for refrigeration infrastructure and resulting in energy and water savings in data centers.
Research Advisor: Kenneth E. Goodson
Daniel Neamati
Neural digital twins to enable large-scale fire management at the wildland-urban interface
Daniel Neamati is a PhD candidate in aeronautics and astronautics. The aim of his research is to improve decision-making surrounding prescribed burns for fire management at the wildland-urban interface and wildfire-prone areas by leveraging recent advances in neural digital twin data models of complex outdoor environments.
Research Advisor: Grace Gao
