Five UChicago CS students named to Siebel Scholars class of 2025
Three UChicago Computer Science PhD students and two students in the MS in Computational Analysis and Public Policy (MS-CAPP) program were named to the 2025 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, 38 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 1,900 scholars, researchers, and entrepreneurs.
This year’s class of UChicago CS Siebel Scholars includes students working on delivering better health and care for vulnerable individuals, improving the digital implementation of public benefits programs, making the internet more fair and transparent, making quantum computing more efficient, and protecting society from misuses of generative AI systems. Read more about each student below.
Zewei Liao
Zewei Liao is a student in the MS in Computational Analysis and Public Policy (MS-CAPP) program at the University of Chicago. He earned a BA in Sociology with a minor in Statistics in 2023, also at the University of Chicago. As the managing editor of the Chicago Journal of Sociology, Zewei co-led a team of 20 editors to revive this once-vibrant student-led publication from a COVID-era hiatus, culminating in its first successful publication in three years. Zewei’s research focuses on understanding how to leverage data and computational tools to deliver better health and care for vulnerable individuals. As part of his work, he has led an effort to assess the quality of high-volume data from Medicaid to be used in a study to understand how public insurance plans can help promote early treatment amid the ongoing opioid epidemic.
Michael Rosenbaum
Michael Rosenbaum earned a BA with honors in Public Policy Studies at the University of Chicago in 2015. He is currently a student in the MS in Computational Analysis and Public Policy (MS-CAPP) program at UChicago. Prior to joining MS-CAPP, he held research and management roles at Innovations for Poverty Action (IPA) and The Behavioral Insights Team (BIT) and was a Public Interest Fellow in the Evanston/Skokie school district. At IPA and BIT, his focus was on the synthesis of data science and social science, where he worked with survey and administrative datasets to analyze the impacts of a variety of topics, including encouraging debt forgiveness among Michigan teachers, defining cross-cultural understanding of financial health, and determining the efficacy of phone survey methodology during the pandemic. At UChicago, he has continued to link social science research and data science with the goal of improving the digital implementation of public benefits programs.
Brennan Schaffner
Brennan Schaffner is a rising 5th-year PhD student in the Department of Computer Science at the University of Chicago. He is advised by Marshini Chetty in the Amyoli Internet Research Lab. Through his work, Brennan aims to make the internet more fair and transparent with a blend of both qualitative and quantitative methods. He researches ideas related to upholding user agency and understanding/exposing manipulative designs. In addition to publishing work at top HCI conferences, Brennan’s work often has an added flavor of consumer protection. He has written many comments to regulatory agencies with evidence-based policy suggestions informed by his research. Brennan is currently working on projects related to measuring the impact of platform design on user behavior, platform moderation of user-generated content, and meta-frameworks for researching manipulative platform design.
Lennart Maximilian Seifert
Lennart Maximilian Seifert is a PhD student in Computer Science at the University of Chicago under the guidance of Professor Fred Chong. He obtained his undergraduate degree in Physics at the Dresden University of Technology in 2020. His research focuses on making quantum computing more efficient through the low-level control and characterization of quantum systems, with special focus on control pulse design and optimization. Max has published important work on the optimal design of multi-level quantum gates (qudit gates) and compiler transformations that reduce the errors in quantum chemistry computations. He has also worked at industry internships at Infleqtion and Amazon, where he has worked on optimizations for quantum sensing and new architectures for quantum computing with superconducting devices.
Shawn Shan
Shawn Shan is a PhD student in Computer Science at the University of Chicago, advised by professors Ben Zhao and Heather Zheng. He obtained his BS (computer science) with honors in 2020 and his MS in 2022, both from UChicago. His research focuses on improving security and robustness of machine learning systems, with particular emphasis on protecting society from misuses of generative AI systems. He is the lead student on the Glaze and Nightshade projects, tools that have been downloaded nearly 3 million times by artists around the globe to protect themselves against unwanted style mimicry attacks and training without consent. He has 12 publications at the premier research venues in computer security, 6 as first author. He is the winner of the USENIX Internet Defense Prize, USENIX Security distinguished paper award, Chicago Innovation Award, and has been named to one of Forbes Magazine’s 30 Under 30 for 2024. His project Glaze has also earned a special mention in TIME Magazine’s Best Inventions for 2023.
