Yan Liu, PhD

Yan Liu, PhD

Professor at University of Southern California

    Yan Liu is a Professor in the Computer Science Department and the Director of the Machine Learning Center at the University of Southern California. She received her Ph.D. degree from Carnegie Mellon University. Her research interest is machine learning and its applications to climate science, health care, and sustainability. She has received several awards, including NSF CAREER Award, Okawa Foundation Research Award, New Voices of Academies of Science, Engineering, and Medicine, Best Paper Award in SIAM Data Mining Conference. She serves as general chair for KDD 2020 and ICLR 2023, and program chairs for WSDM 2018, SDM 2020, KDD 2022 and ICLR 2022.

    All Sessions by Yan Liu, PhD

    West Talks 07/22/2024

    Frontiers of Foundation Models for Time Series

    <span class="etn-schedule-location"> <span class="firstfocus">Machine Learning</span> <span class="secfocus">Beginer-Intermediate</span> </span>

    Recent development in deep learning has spurred research advances in time series modeling and analysis. While achieving state-of-the-art results, the best-performing architectures vary highly across applications and domains. Meanwhile, for natural language processing, the Generative Pre-trained Transformer (GPT) has demonstrated impressive performance via training one general-purpose model across various textual datasets. It is intriguing to explore whether GPT-type architectures can be effective for time series, capturing the intrinsic dynamic attributes and leading to significant accuracy improvements. Furthermore, practical applications of time series raise a series of new challenges, such as multi-resolution, multimodal, missing value, distributeness, and interpretability. In this talk, I will discuss the recent development in the area and possible paths to foundation models for time series data. At the end of the talk, I will share my view of future directions for time series research and foundation models. The talk will feature our recent work on ""Tempo: Prompt-based generative pre-trained transformer for time series forecasting"" in ICLR 2024. Learning Objectives and Tools : Time series modeling

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