The Siebel Scholars Foundation has awarded fellowships to five Princeton University graduate students in computer science: Uthsav Chitra, Uma Girish, Jane Pan, Clayton Thomas and Teague Tomesh.

Now in its 22nd year, the Siebel Scholars program annually recognizes students from graduate schools of business, computer science and bioengineering. The 83 distinguished fellows of the Class of 2023 join a worldwide network of more than 1,700 fellowship alumni.

Founded in 2000 by the Thomas and Stacey Siebel Foundation, the Siebel Scholars program awards grants to 16 universities in the United States, China, France, Italy and Japan. Selection for the fellowship is competitive and based on academic achievement and leadership. Siebel Scholars receive a $35,000 award for their final year of studies.

Uthsav Chitra is a Ph.D. student whose research focuses on developing statistical models and computational methods for addressing problems in biology and machine learning. His work has applications in spatial transcriptomics, epistasis, somatic mutations in cancer, anomaly detection, and modeling opinion polarization in social networks. Chitra was awarded a National Science Foundation Graduate Research Fellowship. He received bachelor’s degrees in math, applied math and computer science at Brown University. Before coming to Princeton he worked as a software engineer at Facebook.

Uma Girish is a Ph.D. student broadly interested in computational complexity and algorithms, with a specific interest in the area of quantum computing. Her primary focus is studying the advantages of quantum systems over classical computers with regard to computation and communication. A secondary focus of her work is understanding the power of randomness and intermediate measurements for quantum algorithms. She received her bachelor’s degree in mathematics and computer science and master’s in computer science from Chennai Mathematical Institute in India.

Jane Pan is a master’s student in the Princeton Natural Language Processing group. Her research focuses on large language, as well as in-context learning and prompting methods for structured prediction. She graduated magna cum laude with a bachelor of science degree in applied mathematics from Columbia University, where she was a Davis Scholar performing research with the Data Science Institute. Before pivoting to machine learning research, Pan worked in quantitative finance and data science at Morgan Stanley and various quantitative asset management shops.

Clayton Thomas is a Ph.D. student focusing on theoretical research in algorithmic mechanism design, a field that bridges computer science, economics and operations research. Where traditional computer science studies the ease of running an algorithm on a computer, Thomas’ research studies the ease of explaining an algorithm to a human. His goal is to explain the algorithms that increasingly run our everyday lives. Thomas completed a double major in mathematics and computer science at Purdue University.

Teague Tomesh is a Ph.D. student researching how to close the gap between high-level quantum algorithms and the low-level physical implementation of the quantum computer. His recent work focuses on improving quantum circuit compilation techniques, reducing the resource requirements of quantum chemistry algorithms, and designing benchmark suites for quantum computers. Tomesh received a bachelor of science degree in astronomy and physics with minors in computer science and mathematics from the University of Wisconsin-Madison. While an undergraduate, he researched the evolution of the Milky Way galaxy and worked as a research intern at Argonne National Laboratory.

(This story was adapted from the Siebel Scholars Foundation’s announcement.)

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