September 19, 2025
Hilman Hanivan, Sophia Henn, Jonathan Liu, Raghav Mehrotra, and Yudai Tanaka were selected to the prestigious program’s Class of 2026.
Two UChicago Computer Science PhD students and three students in the MS in Computational Analysis and Public Policy (MS-CAPP) program were named to the 2026 class of the Siebel Scholars—a program that awards grants to 16 universities in the United States and other countries.
The University of Chicago Department of Computer Science was selected for the Siebel Scholars program in 2017. Since then, 43 UChicago CS students have been chosen by the Thomas and Stacey Siebel Foundation to join the group, which “recognizes the most talented students at the world’s leading graduate schools of business, computer science, bioengineering, and energy science, forming an active, lifelong community among an ever-growing group of leaders,” according to the foundation. This year the foundation selected 78 students to join its network of more than 2,000 scholars, researchers, and entrepreneurs.
This year’s class of UChicago CS Siebel Scholars includes students addressing critical gaps in economic data, investigating how economies organize and grow, studying curriculum development in computer science, studying working conditions in informal labor markets, and exploring computer interfaces that generate sensory feedback by interfacing directly with users’ brains and nervous systems. Read more about each student below.
Hilman Hanivan
Hilman Hanivan is a current student in the MS in Computational Analysis and Public Policy (MS-CAPP) program at the University of Chicago. He holds a Bachelor of Applied Statistics from Politeknik Statistika STIS (Jakarta, Indonesia). Prior to joining MS-CAPP, Hilman worked at Statistics Indonesia for five years. There, he worked collaboratively with village leaders to collect and use data to address their most pressing problems. This effort helped these leaders overcome their skepticism of data-driven governance. Hilman’s research focuses on leveraging non-traditional data sources and modern computational tools like machine learning to enhance official statistics production. Currently, he is developing a temporal disaggregation model that integrates survey data, administrative records, and big data to improve the accuracy of downscaling annual GDP into quarterly estimates. This research addresses critical gaps in Indonesia’s subnational economic data, where surveys only support annual estimates, despite policymakers’ need for higher-frequency indicators.
Sophia Henn
Sophia Henn earned a Bachelor of Arts in Economics and Peace Studies from the University of Notre Dame and is currently a student in the MS in Computational Analysis and Public Policy program at the University of Chicago. Sophia uses data science to investigate how economies organize and grow. Prior to joining MS-CAPP, she worked at Harvard’s Growth Lab analyzing international trade data to study how countries transform their economic structure over time, and particularly away from reliance on natural resources. She worked with the governments of Namibia, Kazakhstan, Azerbaijan, and the State of Wyoming to develop pathways for industrial diversification. Sophia also led an initiative at the Harambee Youth Employment Accelerator to analyze millions of employment records to improve support for young workers. Currently, Sophia is studying the labor market frictions that slow economic transformation using natural language processing and machine learning to analyze employment records and detailed surveys.
Jonathan Liu
Jonathan Liu is a PhD student at the University of Chicago advised by Diana Franklin. His work studies curriculum development in a wide range of computer science classes, and he is currently developing a pedagogical intervention to mitigate technical barriers and encourage the development of metacognitive problem-solving skills in undergraduate Algorithms courses. Previously, he graduated from UC Berkeley with Bachelor’s degrees in Math and Computer Science, where he also ran a student group trying to make Theory CS topics and research more accessible for undergraduates.
Raghav Mehrotra
Raghav Mehrotra holds a Bachelor of Arts in History with a minor in Computer Science from Stanford University and is currently a student in the MS in Computational Analysis and Public Policy program at the University of Chicago. A mixed-methods researcher, Raghav studies working conditions at the lowest ends of global value chains, particularly in informal labor markets in urban India. He also studies how different measurements of the same phenomenon can uncover ground truths and population statistics for understudied and underserved groups. Raghav led teams at the Indian Institute for Human Settlements (IIHS) and at Aajeevika Bureau’s migrant worker resource center. At IIHS, he managed the data pipeline for India’s first nationwide study of gig and platform work. He helped found IIHS’s AI Working Group, where he trained researchers to use generative AI to clean data and led institute-wide workshops around training data bias.
Yudai Tanaka
Yudai Tanaka is a PhD candidate in the Department of Computer Science at the University of Chicago advised by Pedro Lopes. His research explores computer interfaces that generate sensory feedback by interfacing directly with users’ brains and nervous systems. Yudai has developed novel devices that transmit signals straight to nerves—enabling hardware-free touch interactions—and has pioneered a new class of interactive experiences that deliver sensory feedback directly to the brain. He sees these interfaces as the foundation for the next generation of interactive systems, able to augment physical abilities and accelerate skill acquisition. Yudai has published his work in top Human-Computer Interaction (HCI) venues, including ACM CHI and UIST, earning a Best Paper Award (CHI 2023), two Best Paper Honorable Mentions (CHI 2024, UIST 2024), and a Best Demo Award (CHI 2022). His research has also been featured in IEEE Spectrum and New Scientist.