The Siebel Scholars Foundation has awarded Siebel Scholars fellowships to five Princeton University graduate students in computer science: Xiaoqi Chen, Huihan Li, Nikunj Saunshi, Jiaqi Su and Kaiyu Yang.
Now in its 21st year, the Siebel Scholars program annually recognizes exceptional students from leading graduate schools of business, computer science and bioengineering. The 82 students of the Class of 2022 join past Siebel Scholars classes to form a professional and personal network of scholars, researchers and entrepreneurs. The program brings together diverse perspectives to influence the technologies, policies, and economic and social decisions that shape the future.
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. Following a competitive review process by the deans of their respective schools on the basis of outstanding academic achievement and demonstrated leadership, the top graduate students from 27 partner programs are selected each year as Siebel Scholars and receive a $35,000 award for their final year of studies.
Xiaoqi (Danny) Chen is a Ph.D. student whose research focuses on designing efficient algorithms that run in high-speed computer networks, to help network operators measure traffic in real time and improve network performance and security. His works have appeared at top conferences and journals such as ACM SIGCOMM, ACM CoNEXT, and IEEE/ACM Transactions on Networking. He earned a bachelor’s degree from the Computer Science Special Pilot Class (Yao Class) at Tsinghua University, and has worked as a summer intern at Google and Intel’s Barefoot Networks.
Huihan Li is a second-year master’s student in the Princeton Natural Language Processing Group. Her research interests include conversational question answering, distribution shift and out-of-domain generalization abilities of natural language processing models. She earned a B.A. in computer science and cognitive and linguistic sciences from Wellesley College, graduating magna cum laude. At Wellesley, she conducted research in human network analysis, focusing on predicting urban human locations from GPS trajectories. In 2019, Huihan interned with the Google Shopping Assistant team in Pittsburgh to build price query features for Google Assistant using natural language technology.
Nikunj Saunshi is a Ph.D. student whose research interests lie in bridging the gap between theory and practice of machine learning, with the goal of improving and designing machine learning algorithms that are grounded in mathematical principles. His recent work has focused on developing theory for representation learning in self-supervised learning, natural language processing and meta-learning. He earned a bachelor’s degree in computer science from the Indian Institute of Technology-Bombay and master’s in computer science from Princeton. He spent two years in South Korea working as an associate engineer in the R&D division of Samsung Electronics. He has also interned at Microsoft Research, Google and the Institute of Science and Technology Austria.
Jiaqi Su is a Ph.D. student whose research focuses on the use of deep learning to transform and improve audio, with applications including speech enhancement, bandwidth extension, speech source separation and acoustic matching. She worked as an intern at Adobe Research three times between 2019 and 2021 on the long-term project of improving low-quality audio recordings to sound studio-quality. Prior to coming to Princeton, she interned in the machine learning group of Tencent YouTu Lab, working on the classic voice conversion problem. She received an Award for Excellence from the Princeton University School of Engineering and Applied Science in 2020, and received her B.S. degree in computer science from Cornell University.
Kaiyu Yang is a Ph.D. student whose research applies deep learning and symbolic methods to teach machines to reason. This includes reasoning in both formal domains such as automated theorem proving and informal domains such as rule induction from natural language. In addition, he has worked on constructing and analyzing machine learning datasets, especially focusing on fairness, privacy, and mitigating dataset bias. He received an M.S. in computer science from the University of Michigan, a B.Eng. in computer science from Tsinghua University, and a B.S. in mathematics from Tsinghua University.
(This story was adapted from the Siebel Scholars Foundation’s announcement.)