(she/her/hers)
Google Brain
Data Mining, Recommendation Systems, Graph Neural Networks
Jianling Wang is a research scientist working at Google Brain. She obtained her Ph.D. degree from the Department of Computer Science and Engineering at Texas A&M University, advised by Prof. James Caverlee. Before that, she received her Master's degree in Computer Science from Georgia Institute of Technology and her Bachelor's degree in Information Engineering from The Chinese University of Hong Kong. Her research interests generally include data mining and machine learning, with a particular focus on recommendation systems and graph neural networks.
Toward Sustainable Recommendation Systems
While recommendation systems have demonstrated success in connecting users and items across many platforms, most are fundamentally not sustainable: they focus on short-lived engagement objectives, and suffer from performance degradation in many real-world scenarios characterized by the presence of constant change and imperfection.
My research seeks to lay the foundations for a new class of sustainable recommendation systems, which should be fundamentally long-lived, while continuously enhancing both current and future potential to connect users and items. Furthermore, the algorithms and frameworks driving sustainable recommendation systems provide the foundation for new personalized machine learning methods that have wide-ranging impacts. In this talk, we will focus on our major contributions toward sustainable recommendation systems from three perspectives: adaptivity, resilience and robustness.