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宾夕法尼亚州立大学
Dr. Qingyun Wu is looking for self-motivated Ph.D. students to work on exciting research problems in machine learning starting from fall 2022 in the College of Information science and technology at Penn State University (https://ist.psu.edu/). Specifically, the lab will be working on research directions including (1) machine learning and optimization for adaptive planning and online decision-making, such as reinforcement learning, multi-armed bandits, and online optimization; and (2) various aspects of automated machine learning, for example, resource-frugal hyperparameter optimization and adaptive model selection, which may have a substantial impact in building practical machine learning systems that can solve real-world problems. Students with a Bachelor's or Master's degree in computer science, electrical engineering, mathematics, or other related disciplines are welcome to apply. In particular, we are looking for students who (1) are passionate and self-motivated to do important/revolutionary work in the aforementioned research directions; (2) have a strong mathematical background; and (3) have good programming skills (students who have experience working on open-source projects are especially preferred). Admitted Ph.D. students will be fully funded through a research assistantship or teaching assistantship.
Bio of Qingyun Wu
Qingyun Wu is an Assistant Professor in the College of Information Science and Technology at Penn State University. She just finished a one-year postdoc at Microsoft Research, New York City Lab, hosted by Dr. John Langford and Sham Kakade. Qingyun obtained her Ph.D. degree in Computer Science from the University of Virginia in June 2020, advised by Prof. Hongning Wang. Qingyun’s research interests lie primarily in machine learning, with a special focus on methods that involve online decision-making, such as multi-armed bandit, contextual bandits, and, more generally, reinforcement learning. Qingyun is especially interested in developing theoretically sound machine learning solutions that can have a substantial impact in real-world applications. The main applications of Qingyun’s research work include (1) information retrieval tasks, such as personalized recommendation, online learning to rank, and ads prediction, and (2) automated machine learning, such as economical hyperparameter optimization and adaptive model selection.Qingyun collaborates a lot with industrial research labs. Qingyun's research work has been published in many top-tier venues, including SIGIR, WWW, KDD, AAAI, and NeurIPS. Qingyun’s work is recognized by multiple prestigious awards, including the Graduate Student Award for Outstanding Research at the University of Virginia, the Virginia Engineering Foundation Fellowship, the Best Graduate Research Short award at CAPWIC 2017, Rising Stars in EECS 2019, and the Best Paper Award at SIGIR 2019. She is one of the main co-authors and main contributors of the open-source AutoML library FLAML: https://github.com/microsoft/FLAML
If you are interested in knowing more about Qingyun’s research work, please visit Qingyun’s homepage at https://qingyun-wu.github.io/ and Qingyun’s google scholar page at https://scholar.google.com/citations?user=Y54J21sAAAAJ&hl=enIf you are interested, please send an email to qingyun.wu@psu.edu with a short introduction of your research interests, CV, and transcripts.
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AI英才汇是「机器学习算法与自然语言处理」旗下用来连接校园与企业的招聘平台。涵盖高校硕博招生、企业招聘、实习和内推等。助力百万学子求职升学！如果你也想进行招生或者公司招聘，请联系我们的负责人忆臻（微信：yizhen-nlp），添加请修改备注为：AI英才汇+学校/公司+姓名+招聘！
