博士申请 | 宾夕法尼亚州立大学招收机器学习/人工智能方向全奖博士

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宾夕法尼亚州立大学

宾夕法尼亚州立大学 (PSU, Penn State) 位列 CSRankings 排名全美第 26，其中 AI 方向排名全美第 23，QS ranking 全世界第 96。学校主校区位于宾夕法尼亚州的 State College，是一个典型的大学城，非常安全。绝大多数人口都是大学相关人员。学校日常有校车服务覆盖整个大学城，即使没车也能日常通勤。生活便利有许多中超和中餐馆，生活成本低。周围风景优美，还有一个可以容纳接近 10 万人的橄榄球场。机场离学校大概 15 分钟车程。距离匹兹堡 2.5 小时车程，费城 3 小时车程，华盛顿 DC 3.5 小时车程，纽约 4 小时车程。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.

导师简介

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:

https://qingyun-wu.github.io/ 

Google Scholar Page:

https://scholar.google.com/citations?user=Y54J21sAAAAJ&hl=en

招生要求

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.

申请方式

If you are interested, please send an email to [e-mail] with a short introduction of your research interests, CV, and transcripts.

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